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Sökning: WFRF:(Pashami Sepideh 1985 )

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1.
  • Altarabichi, Mohammed Ghaith, 1981- (författare)
  • Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning
  • 2024
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. A difficulty that encouraged a growing number of researchers to use Evolutionary Computation (EC) algorithms to optimize Deep Neural Networks (DNN); a research branch called Evolutionary Deep Learning (EDL).This thesis is a two-fold exploration within the domains of EDL, and more broadly Evolutionary Machine Learning (EML). The first goal is to makeEDL/EML algorithms more practical by reducing the high computational costassociated with EC methods. In particular, we have proposed methods to alleviate the computation burden using approximate models. We show that surrogate-models can speed up EC methods by three times without compromising the quality of the final solutions. Our surrogate-assisted approach allows EC methods to scale better for both, expensive learning algorithms and large datasets with over 100K instances. Our second objective is to leverage EC methods for advancing our understanding of Deep Neural Network (DNN) design. We identify a knowledge gap in DL algorithms and introduce an EC algorithm precisely designed to optimize this uncharted aspect of DL design. Our analytical focus revolves around revealing avant-garde concepts and acquiring novel insights. In our study of randomness techniques in DNN, we offer insights into the design and training of more robust and generalizable neural networks. We also propose, in another study, a novel survival regression loss function discovered based on evolutionary search.
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2.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Stacking Ensembles of Heterogenous Classifiers for Fault Detection in Evolving Environments
  • 2020
  • Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 1068-1068
  • Konferensbidrag (refereegranskat)abstract
    • Monitoring the condition, detecting faults, and modeling the degradation of industrial equipment are important challenges in Prognostics and Health Management (PHM) field. Our solution to the challenge demonstrated a multi-stage approach for detecting faults in a group of identical industrial equipment, composed of four identical interconnected components, that have been deployed to the evolving environment with changes in operational and environmental conditions. In the first stage, a stacked ensemble of heterogeneous classifiers was applied to predict the state of each component of the equipment individually. In the second stage, a low pass filter was applied to smoothen the predictions cast by stacked ensembles, utilizing temporal information of the prediction sequence. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.
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3.
  • Rahat, Mahmoud, 1985-, et al. (författare)
  • Modeling turbocharger failures using Markov process for predictive maintenance
  • 2020
  • Ingår i: e-proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15). - : European Safety and Reliability Association. - 9789811485930
  • Konferensbidrag (refereegranskat)abstract
    • The advancements of the telematics and connectivity solutions have provided new opportunities for the field of predictive maintenance. The number of sensors installed on a vehicle is increasing over time, and manufacturers are looking for new ways to improve the uptime of their fleet while at the same time reducing the costs related to unexpected breakdowns. The nature of the aggregated data from vehicles is sequential, and it is interesting to investigate existing methods for modeling partially observable state sequences to detect common patterns of failure. In this paper, we introduce a new approach for predicting turbocharger failures of Volvo trucks. The first step of the method deals with modeling a sequence of readouts from each vehicle using a Markov process. To do so, we identify the most informative signals and then employ spatial similarity clustering on the readouts. We interpret each cluster as a Markov state and further convert the history of a truck into a trajectory of states. This trajectory is then aligned with repairs information to form a standard sequence labeling problem. Finally, we train a hidden Markov model (HMM) classifier for assessing the health condition of the equipment. Empirical evaluations obtained on our realworld dataset of trucks suggest that the proposed method improves the AUC score of the final system up to 6% for predicting failures of a turbocharger.
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4.
  • Alabdallah, Abdallah, 1979-, et al. (författare)
  • Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
  • 2023
  • Ingår i: Proceedings of the Asia Pacific Conference of the PHM Society 2023. - New York : The Prognostics and Health Management Society.
  • Konferensbidrag (refereegranskat)abstract
    • Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.
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5.
  • Alabdallah, Abdallah, 1979- (författare)
  • Machine Learning Survival Models : Performance and Explainability
  • 2023
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Survival analysis is an essential statistics and machine learning field in various critical applications like medical research and predictive maintenance. In these domains understanding models' predictions is paramount. While machine learning techniques are increasingly applied to enhance the predictive performance of survival models, they simultaneously sacrifice transparency and explainability. Survival models, in contrast to regular machine learning models, predict functions rather than point estimates like regression and classification models. This creates a challenge regarding explaining such models using the known off-the-shelf machine learning explanation techniques, like Shapley Values, Counterfactual examples, and others.   Censoring is also a major issue in survival analysis where the target time variable is not fully observed for all subjects. Moreover, in predictive maintenance settings, recorded events do not always map to actual failures, where some components could be replaced because it is considered faulty or about to fail in the future based on an expert's opinion. Censoring and noisy labels create problems in terms of modeling and evaluation that require to be addressed during the development and evaluation of the survival models.Considering the challenges in survival modeling and the differences from regular machine learning models, this thesis aims to bridge this gap by facilitating the use of machine learning explanation methods to produce plausible and actionable explanations for survival models. It also aims to enhance survival modeling and evaluation revealing a better insight into the differences among the compared survival models.In this thesis, we propose two methods for explaining survival models which rely on discovering survival patterns in the model's predictions that group the studied subjects into significantly different survival groups. Each pattern reflects a specific survival behavior common to all the subjects in their respective group. We utilize these patterns to explain the predictions of the studied model in two ways. In the first, we employ a classification proxy model that can capture the relationship between the descriptive features of subjects and the learned survival patterns. Explaining such a proxy model using Shapley Values provides insights into the feature attribution of belonging to a specific survival pattern. In the second method, we addressed the "what if?" question by generating plausible and actionable counterfactual examples that would change the predicted pattern of the studied subject. Such counterfactual examples provide insights into actionable changes required to enhance the survivability of subjects.We also propose a variational-inference-based generative model for estimating the time-to-event distribution. The model relies on a regression-based loss function with the ability to handle censored cases. It also relies on sampling for estimating the conditional probability of event times. Moreover, we propose a decomposition of the C-index into a weighted harmonic average of two quantities, the concordance among the observed events and the concordance between observed and censored cases. These two quantities, weighted by a factor representing the balance between the two, can reveal differences between survival models previously unseen using only the total Concordance index. This can give insight into the performances of different models and their relation to the characteristics of the studied data.Finally, as part of enhancing survival modeling, we propose an algorithm that can correct erroneous event labels in predictive maintenance time-to-event data. we adopt an expectation-maximization-like approach utilizing a genetic algorithm to find better labels that would maximize the survival model's performance. Over iteration, the algorithm builds confidence about events' assignments which improves the search in the following iterations until convergence.We performed experiments on real and synthetic data showing that our proposed methods enhance the performance in survival modeling and can reveal the underlying factors contributing to the explainability of survival models' behavior and performance.
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6.
  • Alabdallah, Abdallah, 1979-, et al. (författare)
  • SurvSHAP : A Proxy-Based Algorithm for Explaining Survival Models with SHAP
  • 2022
  • Ingår i: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA). - Piscataway, NJ : IEEE. - 9781665473309 - 9781665473316
  • Konferensbidrag (refereegranskat)abstract
    • Survival Analysis models usually output functions (survival or hazard functions) rather than point predictions like regression and classification models. This makes the explanations of such models a challenging task, especially using the Shapley values. We propose SurvSHAP, a new model-agnostic algorithm to explain survival models that predict survival curves. The algorithm is based on discovering patterns in the predicted survival curves, the output of the survival model, that would identify significantly different survival behaviors, and utilizing a proxy model and SHAP method to explain these distinct survival behaviors. Experiments on synthetic and real datasets demonstrate that the SurvSHAP is able to capture the underlying factors of the survival patterns. Moreover, SurvSHAP results on the Cox Proportional Hazard model are compared with the weights of the model to show that we provide faithful overall explanations, with more fine-grained explanations of the sub-populations. We also illustrate the wrong model and explanations learned by a Cox model when applied to heterogeneous sub-populations. We show that a non-linear machine learning survival model with SurvSHAP can better model the data and provide better explanations than linear models.
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7.
  • Alabdallah, Abdallah, 1979-, et al. (författare)
  • The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
  • 2024
  • Ingår i: Artificial Intelligence in Medicine. - Amsterdam : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 148
  • Tidskriftsartikel (refereegranskat)abstract
    • The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events. 
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8.
  • Alabdallah, Abdallah, 1979-, et al. (författare)
  • Understanding Survival Models through Counterfactual Explanations
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • The development of black-box survival models has created a need for methods that explain their outputs, just as in the case of traditional machine learning methods. Survival models usually predict functions rather than point estimates. This special nature of their output makes it more difficult to explain their operation. We propose a method to generate plausible counterfactual explanations for survival models. The method supports two options that handle the special nature of survival models' output. One option relies on the Survival Scores, which are based on the area under the survival function, which is more suitable for proportional hazard models. The other one relies on Survival Patterns in the predictions of the survival model, which represent groups that are significantly different from the survival perspective. This guarantees an intuitive well-defined change from one risk group (Survival Pattern) to another and can handle more realistic cases where the proportional hazard assumption does not hold. The method uses a Particle Swarm Optimization algorithm to optimize a loss function to achieve four objectives: the desired change in the target, proximity to the explained example, likelihood, and the actionability of the counterfactual example. Two predictive maintenance datasets and one medical dataset are used to illustrate the results in different settings. The results show that our method produces plausible counterfactuals, which increase the understanding of black-box survival models.
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9.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses
  • 2021
  • Ingår i: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021. - : IEEE. ; , s. 1-6
  • Konferensbidrag (refereegranskat)abstract
    • Batteries are a safety-critical and the most expensive component for electric vehicles (EVs). To ensure the reliability of the EVs in operation, it is crucial to monitor the state of health of those batteries. Monitoring their deterioration is also relevant to the sustainability of the transport solutions, through creating an efficient strategy for utilizing the remaining capacity of the battery and its second life. Electric buses, similar to other EVs, come in many different variants, including different configurations and operating conditions. Developing new degradation models for each existing combination of settings can become challenging from different perspectives such as unavailability of failure data for novel settings, heterogeneity in data, low amount of data available for less popular configurations, and lack of sufficient engineering knowledge. Therefore, being able to automatically transfer a machine learning model to new settings is crucial. More concretely, the aim of this work is to extract features that are invariant across different settings.In this study, we propose an evolutionary method, called genetic algorithm for domain invariant features (GADIF), that selects a set of features to be used for training machine learning models, in such a way as to maximize the invariance across different settings. A Genetic Algorithm, with each chromosome being a binary vector signaling selection of features, is equipped with a specific fitness function encompassing both the task performance and domain shift. We contrast the performance, in migrating to unseen domains, of our method against a number of classical feature selection methods without any transfer learning mechanism. Moreover, in the experimental result section, we analyze how different features are selected under different settings. The results show that using invariant features leads to a better generalization of the machine learning models to an unseen domain.
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10.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Fast Genetic Algorithm for feature selection — A qualitative approximation approach
  • 2023
  • Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 211
  • Tidskriftsartikel (refereegranskat)abstract
    • Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a new machine learning model. An approximation (also known as meta-model or a surrogate) of the true function can be used in such applications to alleviate the computation cost. In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. We define “Approximation Usefulness” to capture the necessary conditions to ensure correctness of the EA computations when an approximation is used. Based on this definition, we propose a procedure to construct a lightweight qualitative meta-model by the active selection of data instances. We then use a meta-model to carry out the feature selection task. We apply this procedure to the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation) to create a Qualitative approXimations variant, CHCQX. We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy (as compared to CHC), particularly for large datasets with over 100K instances. We also demonstrate the applicability of the thinking behind our approach more broadly to Swarm Intelligence (SI), another branch of the Evolutionary Computation (EC) paradigm with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available. © 2022 The Author(s)
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11.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
  • 2023
  • Ingår i: Evolutionary Computation Conference Companion (GECCO ’23 Companion), July 15–19, 2023, Lisbon, Portugal. - New York, NY : Association for Computing Machinery (ACM). - 9798400701207 ; , s. 11-12
  • Konferensbidrag (refereegranskat)abstract
    • We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].References[1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.[2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.[3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.© 2023 Copyright held by the owner/author(s).
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12.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Improving Concordance Index in Regression-based Survival Analysis : Discovery of Loss Function for Neural Networks
  • 2024
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • In this work, we use an Evolutionary Algorithm (EA) to discover a novel Neural Network (NN) regression-based survival loss function with the aim of improving the C-index performance. Our contribution is threefold; firstly, we propose an evolutionary meta-learning algorithm SAGA$_{loss}$ for optimizing a neural-network regression-based loss function that maximizes the C-index; our algorithm consistently discovers specialized loss functions that outperform MSCE. Secondly, based on our analysis of the evolutionary search results, we highlight a non-intuitive insight that signifies the importance of the non-zero gradient for the censored cases part of the loss function, a property that is shown to be useful in improving concordance. Finally, based on this insight, we propose MSCE$_{Sp}$, a novel survival regression loss function that can be used off-the-shelf and generally performs better than the Mean Squared Error for censored cases. We performed extensive experiments on 19 benchmark datasets to validate our findings.
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13.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Predicting state of health and end of life for batteries in hybrid energy buses
  • 2020
  • Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 1231-1231
  • Konferensbidrag (refereegranskat)abstract
    • There is a major ongoing transition from utilizing fossil fuel to electricity in buses for enabling a more sustainable, environmentally friendly, and connected transportation ecosystem. Batteries are expensive, up to 30% of the total cost for the vehicle (A. Fotouhi 2016), and considered safety-critical components for electric vehicles (EV). As they deteriorate over time, monitoring the health status and performing the maintenance accordingly in a proactive manner is crucial to achieving not only a safe and sustainable transportation system but also a cost-effective operation and thus a greater market satisfaction. As a widely used indicator, the State of Health (SOH) is a measurement that reflects the current capability of the battery in comparison to an ideal condition. Accurate estimation of SOH is important to evaluate the validity of the batteries for the intended application and can be utilized as a proxy to estimate the remaining useful life (RUL) and predict the end-of-life (EOL) of batteries for maintenance planning. The SOH is computed via an on-board computing device, i.e. battery management unit (BMU), which is commonly developed based on controlled experiments and many of them are physical-model based approaches that only depend on the internal parameters of the battery (B. Pattipati 2008; M. H. Lipu 2018). However, the deterioration processes of batteries in hybrid and full-electric buses depend not only on the designing parameters but also on the operating environment and usage patterns of the vehicle. Therefore, utilizing multiple data sources to estimate the health status and EOL of the batteries is of potential internet. In this study, a data-driven prognostic method is developed to estimate SOH and predict EOL for batteries in heterogeneous fleets of hybrid buses, using various types of data sources, e.g. physical configuration of the vehicle, deployment information, on-board sensor readings, and diagnostic fault codes. A set of new features was generated from the existing sensor readings by inducing artificial resets on each battery replacement. A neural network-based regression model achieved accurate estimates of battery SOH status. Another network was used to indicate the EOL of batteries and the result was evaluated using battery replacement based on the current maintenance strategy. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.
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14.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Rolling The Dice For Better Deep Learning Performance : A Study Of Randomness Techniques In Deep Neural Networks
  • 2024
  • Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 667, s. 1-17
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a comprehensive empirical investigation into the interactions between various randomness techniques in Deep Neural Networks (DNNs) and how they contribute to network performance. It is well-established that injecting randomness into the training process of DNNs, through various approaches at different stages, is often beneficial for reducing overfitting and improving generalization. However, the interactions between randomness techniques such as weight noise, dropout, and many others remain poorly understood. Consequently, it is challenging to determine which methods can be effectively combined to optimize DNN performance. To address this issue, we categorize the existing randomness techniques into four key types: data, model, optimization, and learning. We use this classification to identify gaps in the current coverage of potential mechanisms for the introduction of noise, leading to proposing two new techniques: adding noise to the loss function and random masking of the gradient updates.In our empirical study, we employ a Particle Swarm Optimizer (PSO) to explore the space of possible configurations to answer where and how much randomness should be injected to maximize DNN performance. We assess the impact of various types and levels of randomness for DNN architectures applied to standard computer vision benchmarks: MNIST, FASHION-MNIST, CIFAR10, and CIFAR100. Across more than 30\,000 evaluated configurations, we perform a detailed examination of the interactions between randomness techniques and their combined impact on DNN performance. Our findings reveal that randomness in data augmentation and in weight initialization are the main contributors to performance improvement. Additionally, correlation analysis demonstrates that different optimizers, such as Adam and Gradient Descent with Momentum, prefer distinct types of randomization during the training process. A GitHub repository with the complete implementation and generated dataset is available. © 2024 The Author(s)
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15.
  • Altarabichi, Mohammed Ghaith, 1981-, et al. (författare)
  • Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
  • 2021
  • Ingår i: 2021 IEEE Congress on Evolutionary Computation (CEC). - : IEEE. - 9781728183930 ; , s. 776-785
  • Konferensbidrag (refereegranskat)abstract
    • Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets. We design and evaluate a Surrogate-Assisted Genetic Algorithm (SAGA) which utilizes this concept to guide the evolutionary search during the early phase of exploration. SAGA only switches to evaluating the original function at the final exploitation phase.We prove that the run-time upper bound of SAGA surrogate-assisted stage is at worse equal to the wrapper GA, and it scales better for induction algorithms of high order of complexity in number of instances. We demonstrate, using 14 datasets from the UCI ML repository, that in practice SAGA significantly reduces the computation time compared to a baseline wrapper Genetic Algorithm (GA), while converging to solutions of significantly higher accuracy. Our experiments show that SAGA can arrive at near-optimal solutions three times faster than a wrapper GA, on average. We also showcase the importance of evolution control approach designed to prevent surrogates from misleading the evolutionary search towards false optima.
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16.
  • Asadi, Sahar, 1983-, et al. (författare)
  • TD Kernel DM+V : time-dependent statistical gas distribution modelling on simulated measurements
  • 2011
  • Ingår i: Olfaction and Electronic Nose. - : Springer Science+Business Media B.V.. - 9780735409200 ; , s. 281-282
  • Konferensbidrag (refereegranskat)abstract
    • To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.
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17.
  • Bobek, Szymon, et al. (författare)
  • Towards Explainable Deep Domain Adaptation
  • 2024
  • Ingår i: Artificial Intelligence. ECAI 2023 International Workshops. - Cham : Springer. - 9783031503955 - 9783031503962 ; , s. 101-113
  • Konferensbidrag (refereegranskat)abstract
    • In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from source to target domain. In this work, we present a method that makes the adaptation process more transparent by providing two complementary explanation mechanisms. The first mechanism explains how the source and target distributions are aligned in the latent space of the domain adaptation model. The second mechanism provides descriptive explanations on how the decision boundary changes in the adapted model with respect to the source model. Along with a description of a method, we also provide initial results obtained on publicly available, real-life dataset. © The Author(s) 2024.
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18.
  • Bouguelia, Mohamed-Rafik, 1987-, et al. (författare)
  • Multi-Task Representation Learning
  • 2017
  • Ingår i: 30th Annual Workshop ofthe Swedish Artificial Intelligence Society SAIS 2017. - Linköping : Linköping University Electronic Press. - 9789176854969 ; , s. 53-59
  • Konferensbidrag (refereegranskat)abstract
    • The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.
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19.
  • Chen, Kunru, 1993-, et al. (författare)
  • Forklift Truck Activity Recognition from CAN Data
  • 2021
  • Ingår i: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. - Heidelberg : Springer. - 9783030667696 - 9783030667702 ; , s. 119-126
  • Konferensbidrag (refereegranskat)abstract
    • Machine activity recognition is important for accurately esti- mating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks from on-board data streaming on the controller area network. We show that such recognition works across different sites. We first demon- strate the baseline classification performance of a Random Forest that uses 14 signals over 20 time steps, for a 280-dimensional input. Next, we show how a deep neural network can learn low-dimensional representa- tions that, with fine-tuning, achieve comparable accuracy. The proposed representation achieves machine activity recognition. Also, it visualizes the forklift operation over time and illustrates the relationships across different activities. © Springer Nature Switzerland AG 2020
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20.
  • Chen, Kunru, 1993-, et al. (författare)
  • Material handling machine activity recognition by context ensemble with gated recurrent units
  • 2023
  • Ingår i: Engineering applications of artificial intelligence. - Oxford : Elsevier. - 0952-1976 .- 1873-6769. ; 126:Part C
  • Tidskriftsartikel (refereegranskat)abstract
    • Research on machine activity recognition (MAR) is drawing more attention because MAR can provide productivity monitoring for efficiency optimization, better maintenance scheduling, product design improvement, and potential material savings. A particular challenge of MAR for human-operated machines is the overlap when transiting from one activity to another: during transitions, operators often perform two activities simultaneously, e.g., lifting the fork already while approaching a rack, so the exact time when one activity ends and another begins is uncertain. Machine learning models are often uncertain during such activity transitions, and we propose a novel ensemble-based method adapted to fuzzy transitions in a forklift MAR problem. Unlike traditional ensembles, where models in the ensemble are trained on different subsets of data, or with costs that force them to be diverse in their responses, our approach is to train a single model that predicts several activity labels, each under a different context. These individual predictions are not made by independent networks but are made using a structure that allows for sharing important features, i.e., a context ensemble. The results show that the gated recurrent unit network can provide medium or strong confident context ensembles for 95% of the cases in the test set, and the final forklift MAR result achieves accuracies of 97% for driving and 90% for load-handling activities. This study is the first to highlight the overlapping activity issue in MAR problems and to demonstrate that the recognition results can be significantly improved by designing a machine learning framework that addresses this issue. © 2023 The Author(s)
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21.
  • Chen, Kunru, 1993-, et al. (författare)
  • Predicting Air Compressor Failures Using Long Short Term Memory Networks
  • 2019
  • Ingår i: Progress in Artificial Intelligence. - Cham : Springer. - 9783030302405 - 9783030302412 ; , s. 596-609
  • Konferensbidrag (refereegranskat)abstract
    • We introduce an LSTM-based method for predicting compressor failures using aggregated sensory data, and evaluate it using historical information from over 1000 heavy duty vehicles during 2015 and 2016. The goal is to proactively identify trucks that will require maintenance in the near future, so that component replacement can be scheduled before the failure happens, translating into improved uptime. The problem is formulated as a classification task of whether a compressor failure will happen within the specified prediction horizon. A recurrent neural network using Long Short-Term Memory (LSTM) architecture is employed as the prediction model, and compared against Random Forest (RF), the solution used in industrial deployment at the moment. Experimental results show that while Random Forest slightly outperforms LSTM in terms of AUC score, the predictions of LSTM stay significantly more stable over time, showing a consistent trend from healthy to faulty class. Additionally, LSTM is also better at detecting the switch from faulty class to the healthy one after a repair. We demonstrate that this stability is important for making repair decisions, especially in questionable cases, and therefore LSTM model is likely to lead to better results in practice. © Springer Nature Switzerland AG 2019
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22.
  • Chen, Kunru, 1993-, et al. (författare)
  • Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals
  • 2022
  • Ingår i: Sensors. - Basel : MDPI. - 1424-8220. ; 22:11
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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23.
  • Chen, Kunru, 1993-, et al. (författare)
  • Toward Solving Domain Adaptation with Limited Source Labeled Data
  • 2023
  • Ingår i: 2023 IEEE International Conference on Data Mining Workshops (ICDMW). - Piscataway, NJ : IEEE Computer Society. - 9798350381641 ; , s. 1240-1246
  • Konferensbidrag (refereegranskat)abstract
    • The success of domain adaptation relies on high-quality labeled data from the source domain, which is a luxury setup for applied machine learning problems. This article investigates a particular challenge: the source labeled data are neither plentiful nor sufficiently representative. We studied the challenge of limited data with an industrial application, i.e., forklift truck activity recognition. The task is to develop data-driven methods to recognize forklift usage performed in different warehouses with a large scale of signals collected from the onboard sensors. The preliminary results show that using pseudo-labeled data from the source domain can significantly improve classification performance on the target domain in some tasks. As the real-world problems are much more complex than typical research settings, it is not clearly understood in what circumstance the improvement may occur. Therefore, we provided discussions regarding this phenomenon and shared several inspirations on the difficulty of understanding and debugging domain adaptation problems in practice. © 2023 IEEE.
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24.
  • Cooney, Martin, 1980-, et al. (författare)
  • Avoiding Improper Treatment of Persons with Dementia by Care Robots
  • 2019
  • Konferensbidrag (refereegranskat)abstract
    • The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.
  •  
25.
  • Cooney, Martin, 1980-, et al. (författare)
  • Pitfalls of Affective Computing : How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?
  • 2018
  • Ingår i: WWW '18 Companion Proceedings of the The Web Conference 2018. - New York, NY : ACM Publications. ; , s. 1563-1566
  • Konferensbidrag (refereegranskat)abstract
    • What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.
  •  
26.
  • Dahl, Oskar, et al. (författare)
  • Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters : Using Clustering and Rule-Based Machine Learning
  • 2020
  • Ingår i: Proceedings of the 2020 3rd International Conference on Information Management and Management Science, IMMS 2020. - New York : Association for Computing Machinery (ACM). - 9781450375467 ; , s. 13-22
  • Konferensbidrag (refereegranskat)abstract
    • Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks. In this paper we propose a framework that aims to extract costumers' vehicle behaviours from Logged Vehicle Data (LVD) in order to evaluate whether they align with vehicle configurations, so-called Global Transport Application (GTA) parameters. Gaussian mixture model (GMM)s are employed to cluster and classify various vehicle behaviors from the LVD. Rule-based machine learning (RBML) was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis. © 2020 ACM.
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27.
  • Del Moral, Pablo, 1989-, et al. (författare)
  • Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models
  • 2022
  • Ingår i: Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. - State College, PA : PHM Society. - 9781936263363 ; , s. 110-117
  • Konferensbidrag (refereegranskat)abstract
    • One of the main challenges for predictive maintenance in real applications is the quality of the data, especially the labels. In this paper, we propose a methodology to filter out the misleading labels that harm the performance of Machine Learning models. Ideally, predictive maintenance would be based on the information of when a fault has occurred in a machine and what specific type of fault it was. Then, we could train machine learning models to identify the symptoms of such fault before it leads to a breakdown. However, in many industrial applications, this information is not available. Instead, we approximate it using a log of component replacements, usually coming from the sales or maintenance departments. The repair history provides reliable labels for fault prediction models only if the replaced component was indeed faulty, with symptoms captured by collected data, and it was going to lead to a breakdown.However, very often, at least for complex equipment, this assumption does not hold. Models trained using unreliable labels will then, necessarily, fail. We demonstrate that filtering misleading labels leads to improved results. Our central claim is that the same fault, happening several times, should have similar symptoms in the data; thus, we can train a model to predict them. On the contrary, replacements of the same component that do not exhibit similar symptoms will be confusing and harm the ML models. Therefore, we aim to filter the maintenance operations, keeping only those that can be used to predict each other. Suppose we can train a successful model using the data before a component replacement to predict another component replacement. In that case, those maintenance operations must be motivated by the same, or a very similar, type of fault.We test this approach on a real scenario using data from a fleet of sterilizers deployed in hospitals. The data includes sensor readings from the machines describing their operations and the service logs indicating the replacement of components when the manufacturing company performs the service. Since sterilizers are complex machines consisting of many components and systems interacting with each other, there is the possibility of faults happening simultaneously.
  •  
28.
  • Del Moral, Pablo, 1989-, et al. (författare)
  • Hierarchical Multi-class Classification for Fault Diagnosis
  • 2021
  • Ingår i: Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021). - Singapore : Research Publishing Services. - 9789811820168 ; , s. 2457-2464
  • Konferensbidrag (refereegranskat)abstract
    • This paper formulates the problem of predictive maintenance for complex systems as a hierarchical multi-class classification task. This formulation is useful for equipment with multiple sub-systems and components performing heterogeneous tasks. Often, the data available describes the whole system's operation and is not ideal for accurate condition monitoring. In this setup, specialized predictive models analyzing one component at a time rarely perform much better than random. However, using machine learning and hierarchical approaches, we can still exploit the data to build a fault isolation system that provides measurable benefits for technicians in the field. We propose a method for creating a taxonomy of components to train hierarchical classifiers that aim to identify the faulty component. The output of this model is a structured set of predictions with different probabilities for each component. In this setup, traditional machine learning metrics fail to capture the relationship between the performance of the models and its usefulness in the field.We introduce a new metric to evaluate our approach's benefits; it measures the number of tests a technician needs to perform before pinpointing the faulty component. Using a dataset from a real-case problem coming fro the automotive industry, we demonstrate how traditional machine learning performance metrics, like accuracy, fail to capture practical benefits. Our proposed hierarchical approach succeeds in exploiting the information in the data and outperforms non-hierarchical machine learning solutions. In addition, we can identify the weakest link of our fault isolation model, allowing us to improve it efficiently.
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29.
  • Del Moral, Pablo, 1989-, et al. (författare)
  • Hierarchical multi-fault prognostics for complex systems
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • The field of predictive maintenance for complex machinery with multiple possible faults is an important but largely unexplored area. In general, one assumes, often implicitly, the existence of monitoring data specific enough to capture every possible fault independently from all the others.In this paper, we focus on the problem of predicting time-to-failure, or remaining useful life, in situations where the above assumption does not hold. Specifically, what happens when the data is not good enough to uniquely predict every fault, and, more importantly, what happens when different faults share the same symptoms on the recorded data.We demonstrate that prognostics approaches learning independent models for each fault are inadequate. In particular, in the presence of faults that produce similar failure patterns, they produce false alarms disproportionately often or miss the majority of failures. We propose the HMP framework (Hierarchical Multi-fault Prognosis) to solve this problem by extracting a hierarchy of faults based on the similarity of the data they produce. At each node of the hierarchy, we train a regression model to predict the time-to-failure for any of the faults contained in this node. The intuition is that while it might be impossible to predict individual time-to-failure in the presence of similar faults, a model trained on aggregated data can still provide useful information. We demonstrate through experiments the validity of our approach.
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30.
  • Del Moral, Pablo, 1989-, et al. (författare)
  • Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Using hierarchies of classes is one of the standard methods to solve multi-class classification problems. In the literature, selecting the right hierarchy is considered to play a key role in improving classification performance. Although different methods have been proposed, there is still a lack of understanding of what makes a hierarchy good and what makes a method to extract hierarchies perform better or worse.To this effect, we analyze and compare some of the most popular approaches to extracting hierarchies. We identify some common pitfalls that may lead practitioners to make misleading conclusions about their methods.To address some of these problems, we demonstrate that using random hierarchies is an appropriate benchmark to assess how the hierarchy's quality affects the classification performance.In particular, we show how the hierarchy's quality can become irrelevant depending on the experimental setup: when using powerful enough classifiers, the final performance is not affected by the quality of the hierarchy. We also show how comparing the effect of the hierarchies against non-hierarchical approaches might incorrectly indicate their superiority.Our results confirm that datasets with a high number of classes generally present complex structures in how these classes relate to each other. In these datasets, the right hierarchy can dramatically improve classification performance.
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31.
  • Del Moral, Pablo, 1989-, et al. (författare)
  • Why Is Multiclass Classification Hard?
  • 2022
  • Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 10, s. 80448-80462
  • Tidskriftsartikel (refereegranskat)abstract
    • In classification problems, as the number of classes increases, correctly classifying a new instance into one of them is assumed to be more challenging than making the same decision in the presence of fewer classes. The essence of the problem is that using the learning algorithm on each decision boundary individually is better than using the same learning algorithm on several of them simultaneously. However, why and when it happens is still not well-understood today. This work’s main contribution is to introduce the concept of heterogeneity of decision boundaries as an explanation of this phenomenon. Based on the definition of heterogeneity of decision boundaries, we analyze and explain the differences in the performance of state of the art approaches to solve multi-class classification. We demonstrate that as the heterogeneity increases, the performances of all approaches, except one-vs-one, decrease. We show that by correctly encoding the knowledge of the heterogeneity of decision boundaries in a decomposition of the multi-class problem, we can obtain better results than state of the art decompositions. The benefits can be an increase in classification performance or a decrease in the time it takes to train and evaluate the models. We first provide intuitions and illustrate the effects of the heterogeneity of decision boundaries using synthetic datasets and a simplistic classifier. Then, we demonstrate how a real dataset exhibits these same principles, also under realistic learning algorithms. In this setting, we devise a method to quantify the heterogeneity of different decision boundaries, and use it to decompose the multi-class problem. The results show significant improvements over state-of-the-art decompositions that do not take the heterogeneity of decision boundaries into account. © 2013 IEEE.
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32.
  • Del Moral Pastor, Pablo José, 1989- (författare)
  • Hierarchical Methods for Self-Monitoring Systems : Theory and Application
  • 2022
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Self-monitoring solutions first appeared to avoid catastrophic breakdowns in safety-critical mechanisms. The design behind these solutions relied heavily on the physical knowledge of the mechanism and its fault. They usually involved installing specialized sensors to monitor the state of the mechanism and statistical modeling of the recorded data. Mainly, these solutions focused on specific components of a machine and rarely considered more than one type of fault.In our work, on the other hand, we focus on self-monitoring of complex machines, systems composed of multiple components performing heterogeneous tasks and interacting with each other: systems with many possible faults. Today, the data available to monitor these machines is vast but usually lacks the design and specificity to monitor each possible fault in the system accurately. Some faults will show distinctive symptoms in the data; some faults will not; more interestingly, there will be groups of faults with common symptoms in the recorded data.The thesis in this manuscript is that we can exploit the similarities between faults to train machine learning models that can significantly improve the performance of self-monitoring solutions for complex systems that overlook these similarities. We choose to encode these similarity relationships into hierarchies of faults, which we use to train hierarchical supervised models. We use both real-life problems and standard benchmarks to prove the adequacy of our approach on tasks like fault diagnosis and fault prediction.We also demonstrate that models trained on different hierarchies result in significantly different performances. We analyze what makes a good hierarchy and what are the best practices to develop methods to extract hierarchies of classes from the data. We advance the state-of-the-art by defining the concept of heterogeneity of decision boundaries and studying how it affects the performance of different class decompositions. 
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33.
  • Englund, Cristofer, 1977-, et al. (författare)
  • AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
  • 2021
  • Ingår i: Smart Cities. - Basel : MDPI. - 2624-6511. ; 4:2, s. 783-802
  • Tidskriftsartikel (refereegranskat)abstract
    • Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusions it is difficult for humans to trust it. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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34.
  • Eskilsson, Claes, et al. (författare)
  • A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance
  • 2023
  • Ingår i: Proceedings of the 15th European Wave and Tidal Energy Conference.
  • Konferensbidrag (refereegranskat)abstract
    • Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.
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35.
  • Eskilsson, Claes, et al. (författare)
  • Estimation of nonlinear forces acting on floating bodies using machine learning
  • 2023
  • Ingår i: Advances in the Analysis and Design of Marine Structures. - London : CRC Press. - 9781003399759 ; , s. 63-72
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.
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36.
  • Eskilsson, Claes, et al. (författare)
  • Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems
  • 2023
  • Ingår i: The Proceedings of the 33rd International Ocean and Polar Engineering Conference.
  • Konferensbidrag (refereegranskat)abstract
    • We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.
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37.
  • Fu, Jia, et al. (författare)
  • Component atention network for multimodal dance improvisation recognition
  • 2023
  • Ingår i: PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2023. - : Association for Computing Machinery (ACM). ; , s. 114-118
  • Konferensbidrag (refereegranskat)abstract
    • Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, including recognition and generation, is often limited to skeletal data. However, data of other modalities, such as audio, can be recorded and benefit downstream tasks. This paper explores the application and performance of multimodal fusion methods for human motion recognition in the context of dance improvisation. We propose an attention-based model, component attention network (CANet), for multimodal fusion on three levels: 1) feature fusion with CANet, 2) model fusion with CANet and graph convolutional network (GCN), and 3) late fusion with a voting strategy. We conduct thorough experiments to analyze the impact of each modality in different fusion methods and distinguish critical temporal or component features. We show that our proposed model outperforms the two baseline methods, demonstrating its potential for analyzing improvisation in dance.
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38.
  • Gama, Joao, et al. (författare)
  • XAI for Predictive Maintenance
  • 2023
  • Ingår i: KDD '23. - New York, NY : Association for Computing Machinery (ACM). - 9798400701030 ; , s. 5798-5799
  • Konferensbidrag (refereegranskat)abstract
    • The field of Explainable Predictive Maintenance (PM) is concerned with developing methods that can clarify how AI systems operate in the PM domain. One of the challenges of creating maintenance plans is integrating AI output with human decision-making processes and expertise. For AI to be helpful and trustworthy, fault predictions must be contextualized and easily comprehensible to humans. This involves providing tailored explanations to different actors depending on their roles and needs. For example, engineers can be connected to technical installation blueprints, while managers can evaluate system downtime costs, and lawyers can assess safety-threatening failures' potential liability. In many industries, black-box AI systems analyze sensor data to predict failures by detecting anomalies and deviations from typical behavior with impressive accuracy. However, PM is just one part of a broader context that aims to identify the most probable causes, develop a recovery plan, and estimate remaining useful life while providing alternative solutions. Achieving this requires complex interactions among various actors in industrial and decision-making processes. Our tutorial explores current trends, and promising research directions in Explainable AI (XAI) relevant to Explainable Predictive Maintenance (XPM), and future challenges and open issues on this topic. We will also present three case studies that highlight XPM's challenges in bus and train operations and steel factories. © 2023 Owner/Author.
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39.
  • Helldin, Tove, et al. (författare)
  • Supporting analytical reasoning : A study from the automotive industry
  • 2016
  • Ingår i: Human Interface and the Management of Information: Applications and Services. - Cham : Springer International Publishing Switzerland. - 9783319403960 - 9783319403977 ; , s. 20-31
  • Konferensbidrag (refereegranskat)abstract
    • In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area.
  •  
40.
  • Holst, Anders, et al. (författare)
  • Eliciting structure in data
  • 2019
  • Ingår i: CEUR Workshop Proceedings. - Aachen : CEUR-WS.
  • Konferensbidrag (refereegranskat)abstract
    • This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. 
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41.
  • Khaliq, Ali, 1986-, et al. (författare)
  • Bringing Artificial Olfaction and Mobile Robotics Closer Together : An Integrated 3D Gas Dispersion Simulator in ROS
  • 2015
  • Ingår i: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses.
  • Konferensbidrag (refereegranskat)abstract
    • Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.
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42.
  • Khoshkangini, Reza, 1984-, et al. (författare)
  • Bayesian network for failure prediction in different seasons
  • 2020
  • Ingår i: 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020. - : Research Publishing Services. - 9789811485930 ; , s. 1710-1710
  • Konferensbidrag (refereegranskat)
  •  
43.
  • Khoshkangini, Reza, 1984-, et al. (författare)
  • Baysian Network for Failure Prediction in Different Seasons
  • 2020
  • Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - 9789811485930 ; , s. 1710-1710
  • Konferensbidrag (populärvet., debatt m.m.)abstract
    • In recent years, there have been many attentions in developing technologies with the aim of monitoring and predicting emerging issues such as break downs, component failures, and quality degradations e.g., R, Prytz et al. (2015), as a means to provide predictive maintenance solution in modern vehicle industries. These existing technologies exploit several fault predictions and diagnostic pipelines ranging from statistics methods to machine learning algorithms e.g., M, You et al. (2010), Y, Lei et al. (2016). However, these solutions have not particularly concentrated on the ability to predict the component failures and the cause of the failures taking into consideration vehicle usage patterns and history of failures over time in different seasons.This is not a trivial task since modern vehicles with their huge functionalities and dependency among their components bring out a challenge to the manufacturer to plan their maintenance strategy in this complex domain. This is truly a complex challenge since failures can be sourced and affected by multiple features, which are highly related to each other and change over time in different contexts (e.g., location, time, season).  Under such conditions, an advanced early prediction capability is desired, because manufacturers can exceedingly serve from early prediction of potential vehicle component failures, and more specifically the chain of the features and their dependencies which may lead to a failure over time in different seasons.  This is considered important due to the fact that different seasons may have a potential effect on certain component failures, so predicting these dependencies and the actual failure enables a higher level of maintenance for optimally planning and managing total cost and more importantly safety. In this study, we build a probabilistic prediction model in a time series, on top of vehicle usage pattern, which is represented by the Live Vehicle Data (LVD). LVD logged and captured using multiple sensors located in Volvo vehicles that includes usage and specification of the vehicles aggregated in a cumulative fashion. We exploit and apply a type of supervised machine learning algorithm called Bayesian Network N, Friedman. (1997), on the engineered LVD (we applied a type of data engineering process to extract hidden patterns from LVD), which is logged through different seasons. These result a very complex network of dependency in each time stamp that indicates how a failure sourced by different features and their quantitative influences. In addition, integrating all these networks reveal how the usage can influence failure over time. Moreover, the quantitative influences allow us to extract the main chain of effect on a failure. This is strongly beneficial for the manufacturers and maintenance strategy to find out the main reason of failures, which can be extracted by vehicle usage pattern during their operation. © ESREL2020-PSAM15 Organizers
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44.
  • Khoshkangini, Reza, 1984-, et al. (författare)
  • Early Prediction of Quality Issues in Automotive Modern Industry
  • 2020
  • Ingår i: Information. - Basel : MDPI. - 2078-2489. ; 11:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Many industries today are struggling with early identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with customers' requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the Original Equipment Manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using Machine Learning (ML) to forecast the failures of a given component across the large population of units.In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage.We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.  © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
  •  
45.
  • Khoshkangini, Reza, 1984-, et al. (författare)
  • Warranty Claim Rate Prediction using Logged Vehicle Data
  • 2019
  • Ingår i: Lecture Notes in Computer Science. - Heidelberg : Springer. - 0302-9743 .- 1611-3349. ; 11804, s. 663-674
  • Tidskriftsartikel (refereegranskat)abstract
    • Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.
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46.
  • Pashami, Sepideh, 1985-, et al. (författare)
  • A trend filtering approach for change point detection in MOX gas sensors
  • 2013
  • Konferensbidrag (refereegranskat)abstract
    • Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6].
  •  
47.
  • Pashami, Sepideh, 1985-, et al. (författare)
  • Causal discovery using clusters from observational data
  • 2018
  • Konferensbidrag (refereegranskat)abstract
    • Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data.One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.
  •  
48.
  • Pashami, Sepideh, 1985-, et al. (författare)
  • Change detection in an array of MOX sensors
  • 2012
  • Konferensbidrag (refereegranskat)abstract
    • In this article we present an algorithm for online detection of change points in the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system.True change points occur due to changes in the emission modality of the gas source. The main challenge for change point detection in an open sampling system is the chaotic nature of gas dispersion, which causes fluctuations in the sensor response that are not related to changes in the gas source. These fluctuations should not be considered change points in the sensor response. The presented algorithm is derived from the well known Generalized Likelihood Ratio algorithm and it is used both on the output of a single sensor as well on the output of two or more sensors on the array. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, or mixture ratio. The performance measures considered are the detection rate, the number of false alarms and the delay of detection.
  •  
49.
  • Pashami, Sepideh, 1985- (författare)
  • Change detection in metal oxide gas sensor signals for open sampling systems
  • 2015
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • This thesis addresses the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an Open Sampling System (OSS). Changes can occur due to gas source activity such as a sudden alteration in concentration or due to exposure to a different compound. Applications such as gas-leak detection in mines or large-scale pollution monitoring can benefit from reliable change detection algorithms, especially where it is impractical to continuously store or transfer sensor readings, or where reliable calibration is difficult to achieve. Here, it is desirable to detect a change point indicating a significant event, e.g. presence of gas or a sudden change in concentration. The main challenges are turbulent dispersion of gas and the slow response and recovery times of MOX sensors. Due to these challenges, the gas sensor response exhibits fluctuations that interfere with the changes of interest.The contributions of this thesis are centred on developing change detection methods using MOX sensor responses. First, we apply the Generalized Likelihood Ratio algorithm (GLR), a commonly used method that does not make any a priori assumption about change events. Next, we propose TREFEX, a novel change point detection algorithm, which models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We also propose the rTREFEX algorithm as an extension of TREFEX. The core idea behind rTREFEX is an attempt to improve the fitted exponentials of TREFEX by minimizing the number of exponentials even further.GLR, TREFEX and rTREFEX are evaluated for various MOX sensors and gas emission profiles. A sensor selection algorithm is then introduced and the change detection algorithms are evaluated with the selected sensor subsets. A comparison between the three proposed algorithms shows clearly superior performance of rTREFEX both in detection performance and in estimating the change time. Further, rTREFEX is evaluated in real-world experiments where data is gathered by a mobile robot. Finally, a gas dispersion simulation was developed which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model.
  •  
50.
  • Pashami, Sepideh, 1985-, et al. (författare)
  • Detecting changes of a distant gas source with an array of MOX gas sensors
  • 2012
  • Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 12:12, s. 16404-16419
  • Tidskriftsartikel (refereegranskat)abstract
    • We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.
  •  
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