| 1. |
- Corizzo, Roberto, et al.
(författare)
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Multi-aspect renewable energy forecasting
- 2021
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Ingår i: Information Sciences. - Netherlands : Elsevier. - 0020-0255 .- 1872-6291. ; 546, s. 701-722
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Tidskriftsartikel (refereegranskat)abstract
- The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms. © 2020 Elsevier Inc.
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| 2. |
- Davari, Narjes, et al.
(författare)
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A Fault Detection Framework Based on LSTM Autoencoder : A Case Study for Volvo Bus Data Set
- 2022
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Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</em>Volume 13205 LNCS, Pages 39 - 522022. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031013324 - 9783031013331 ; , s. 39-52
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Konferensbidrag (refereegranskat)abstract
- This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022, The Author(s)
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| 3. |
- Elger, Christian, et al.
(författare)
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Pooled efficacy and safety of eslicarbazepine acetate as add-on treatment in patients with focal-onset seizures: Data from four double-blind placebo-controlled pivotal phase III clinical studies
- 2017
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Ingår i: CNS Neuroscience and Therapeutics. - : Wiley. - 1755-5930 .- 1755-5949. ; 23, s. 961-972
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Tidskriftsartikel (refereegranskat)abstract
- © 2017 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd. Purpose: Pooled evaluation of the key efficacy and safety profile of eslicarbazepine acetate (ESL) added-on to stable antiepileptic therapy in adults with focal-onset seizures. Methods: Data from 1703 patients enrolled in four phase III double-blind, randomized, placebo-controlled studies were pooled and analyzed. Following a 2week titration period, ESL was administered at 400mg, 800mg, and 1200mg once-daily doses for 12weeks (maintenance period). Pooled efficacy variable was standardized (/4weeks) seizure frequency (SSF) analyzed over the maintenance period as reduction in absolute and relative SSF and proportion of responders (≥50% reduction in SSF). Pooled safety was analyzed by means of adverse events and clinical laboratory assessments. Results: SSF was significantly reduced with ESL 800mg (P<0.0001) and 1200mg (P<0.0001) compared to placebo. Median relative reduction in SSF was 33.4% for ESL 800mg and 37.8% for 1200mg (placebo: 17.6%), and responder rate was 33.8% and 43.1% (placebo: 22.2%). ESL was more efficacious than placebo regardless of gender, geographical region, epilepsy duration, age at time of diagnosis, seizure type, and type of concomitant antiepileptic drugs (AED). Incidence of adverse events (AEs) and AEs leading to discontinuation was dose dependent. Most common AEs (>10% patients) were dizziness, somnolence, and nausea. The incidence of treatment-emergent AEs (dizziness, somnolence, ataxia, vomiting, and nausea) was lower in patients who began taking ESL 400mg (followed by 400mg increments to 800 or 1200mg) than in those who began taking ESL 600mg or 800mg. Conclusions: Once-daily ESL 800mg and 1200mg showed consistent results across all efficacy and safety endpoints, independent of study population characteristics and type of concomitant AEDs. Treatment initiated with ESL 400mg followed by 400mg increments to 800 or 1200mg provides optimal balance of efficacy and tolerability.
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| 4. |
- Fernandes, Sofia, et al.
(författare)
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NORMO : A new method for estimating the number of components in CP tensor decomposition
- 2020
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Ingår i: Engineering applications of artificial intelligence. - Oxford : Elsevier. - 0952-1976 .- 1873-6769. ; 96
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Tidskriftsartikel (refereegranskat)abstract
- Tensor decompositions are multi-way analysis tools which have been successfully applied in a wide range of different fields. However, there are still challenges that remain few explored, namely the following: when applying tensor decomposition techniques, what should we expect from the result? How can we evaluate its quality? It is expected that, when the number of components is suitable, then few redundancy is observed in the decomposition result. Based on this assumption, we propose a new method, NORMO, which aims at estimating the number of components in CANDECOMP/PARAFAC (CP) decomposition so that no redundancy is observed in the result. To the best of our knowledge, this work encompasses the first attempt to tackle such problem. According to our experiments, the number of non-redundant components estimated by NORMO is among the most accurate estimates of the true CP number of components in both synthetic and real-world tensor datasets (thus validating the rationale guiding our method). Moreover, NORMO is more efficient than most of its competitors. Additionally, our method can be used to discover multi-levels of granularity in the patterns discovered. © 2020 Elsevier Ltd
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| 5. |
- Fernandes, Sofia, et al.
(författare)
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Tensor decomposition for analysing time-evolving social networks : an overview
- 2021
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Ingår i: Artificial Intelligence Review. - Dordrecht : Springer Netherlands. - 0269-2821 .- 1573-7462. ; 54:5, s. 2891-2916
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Tidskriftsartikel (refereegranskat)abstract
- Social networks are becoming larger and more complex as new ways of collecting social interaction data arise (namely from online social networks, mobile devices sensors, ...). These networks are often large-scale and of high dimensionality. Therefore, dealing with such networks became a challenging task. An intuitive way to deal with this complexity is to resort to tensors. In this context, the application of tensor decomposition has proven its usefulness in modelling and mining these networks: it has not only been applied for exploratory analysis (thus allowing the discovery of interaction patterns), but also for more demanding and elaborated tasks such as community detection and link prediction. In this work, we provide an overview of the methods based on tensor decomposition for the purpose of analysing time-evolving social networks from various perspectives: from community detection, link prediction and anomaly/event detection to network summarization and visualization. In more detail, we discuss the ideas exploited to carry out each social network analysis task as well as its limitations in order to give a complete coverage of the topic. © 2020, Springer Nature B.V.
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| 6. |
- Fernandes, Sofia, et al.
(författare)
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WINTENDED : WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks
- 2023
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Ingår i: Machine Learning. - New York, NY : Springer. - 0885-6125 .- 1573-0565. ; 112:2, s. 459-481
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Tidskriftsartikel (refereegranskat)abstract
- Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.
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| 7. |
- Gama, Joao, et al.
(författare)
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XAI for Predictive Maintenance
- 2023
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Ingår i: KDD '23. - New York, NY : Association for Computing Machinery (ACM). - 9798400701030 ; , s. 5798-5799
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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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| 8. |
- Saadallah, Amal, et al.
(författare)
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BRIGHT - Drift-Aware Demand Predictions for Taxi Networks
- 2020
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Ingår i: IEEE Transactions on Knowledge and Data Engineering. - : IEEE. - 1041-4347 .- 1558-2191. ; 32:2, s. 234-245
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Tidskriftsartikel (refereegranskat)abstract
- Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China) and Stockholm (Sweden), as well as controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task.
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| 9. |
- Saadallah, Amal, et al.
(författare)
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BRIGHT - Drift-Aware Demand Predictions for Taxi Networks
- 2019
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Ingår i: 2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2019). - : IEEE. - 9781538674741 ; , s. 2145-2146
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Konferensbidrag (refereegranskat)abstract
- The dynamic behavior of urban mobility patterns makes matching taxi supply with demand as one of the biggest challenges in this industry. Recently, the increasing availability of massive broadcast GPS data has encouraged the exploration of this issue under different perspectives. One possible solution is to build a data-driven real-time taxi-dispatching recommender system. However, existing systems are based on strong assumptions such as stationary demand distributions and finite training sets, which make them inadequate for modeling the dynamic nature of the network. In this paper, we propose BRIGHT: a drift-aware supervised learning framework which aims to provide accurate predictions for short-term horizon taxi demand quantities through a creative ensemble of time series analysis methods that handle distinct types of concept drift. A large experimental set-up which includes three real-world transportation networks and a synthetic test-bed with artificially inserted concept drifts, was employed to illustrate the advantages of BRIGHT when compared to S.o.A methods for this problem.
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