| 1. |
- Andersson, Alf, et al.
(författare)
-
Inline Process Control – a concept study of efficient in-line process control and process adjustment with respect to product geometry
- 2016
-
Ingår i: Swedish Production Symposium 2016 SPS 2016. - Lund, Sweden.
-
Konferensbidrag (refereegranskat)abstract
- All manufacturing processes have variation which may violate the fulfillment of assembly, functional, geometrical or esthetical requirements and difficulties to reach desired form in all areas. The cost for geometry defects rises downstream in the process chain. Therefore, it is vital to discover these defects as soon as they appear. Then adjustments can be done in the process without losing products or time. In order to find a solution for this, a project with the overall scope “development of an intelligent process control system” has been initiated. This project consists of five different work packages: Inline measurement, Process Evaluation, Corrective actions, Flexible tooling and demonstrator cell. These work packages address different areas which are necessary to fulfill the overall scope of the project. The system shall both be able to detect geometrical defects, propose adjustments and adjust simple process parameters. The results are demonstrated in a demo cell located at Chalmers University of Technology. In the demonstrator all the different areas have been verified in an industrial case study – assembly of GOR Volvo S80. Efficient offline programming for robot based measurement, efficient process evaluation based on case base reasoning (CBR) methodology, flexible fixtures and process adjustments based on corrective actions regarding in going part positioning.
|
|
| 2. |
|
|
| 3. |
- Barua, Shaibal, et al.
(författare)
-
Automated EEG Artifact Handling with Application in Driver Monitoring
- 2018
-
Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:5, s. 1350-1361
-
Tidskriftsartikel (refereegranskat)abstract
- Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
|
|
| 4. |
- Barua, Shaibal, 1982-
(författare)
-
Intelligent Driver Mental State Monitoring System Using Physiological Sensor Signals
- 2015
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving.This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well.For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%.
|
|
| 5. |
- Barua, Shaibal, 1982-
(författare)
-
Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.
|
|
| 6. |
|
|
| 7. |
- Funk, Peter
(författare)
-
Why hybrid case-based reasoning will change the future of health science and healthcare
- 2015
-
Ingår i: CEUR Workshop Proceedings. ; , s. 199-204
-
Konferensbidrag (refereegranskat)abstract
- The rapid development of the medical field makes it impossible even for experts in the field to keep up with new treatments and experience. Already in 2010 all medical knowledge doubled in 3,5 years, to keep up to date with all development even in a narrow field is today far beyond human capacity. The need for decision support is increasingly important to ensure optimal treatment of patients, especially if patients are not "standard patients" matching a gold standard treatment. By ensuring confidentiality and collecting structured cases on a large scale will enable clinical decision support far beyond what is possible today and will be a major leap in healthcare.
|
|
| 8. |
- Janssens, Willem, et al.
(författare)
-
Outcome and Actions of the 2019 Reflection Group of the European Safeguards Research and Development Association (ESARDA)
- 2019
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The European Safeguards Research and Development Association (ESARDA), founded in 1969, is a voluntary association of European organizations formed to foster, advance and harmonize research and development (R&D) in the area of nuclear safeguards. It provides a forum for the exchange of information and ideas between nuclear facility operators, safeguards national authorities, regional and international inspectorates, and individuals engaged in safeguards-related research and development. Today ESARDA includes 33 Parties from within the European Union. In addition, a further eight laboratories, authorities, operators and academic institutions from outside the EU have joined ESARDA as Associate Members, while the Association signed Memoranda of Understanding with the Asia-Pacific Safeguards Network and the African Commission on Nuclear Energy, and a Letter of Intent with the Institute for Nuclear Materials Management.ESARDA seeks to maintain a dynamic approach to the developing priorities, while ensuring that its activities continue to anticipate future needs, which is why the Association periodically undertakes a formal Reflection Group exercise. In the last 2 years, the Reflection Group, RG2019, worked along the following objectives:develop a roadmap to improve and enhance the quality, effectiveness and efficiency of safeguards and non-proliferation in Europe and abroad; andensure that the future activities of ESARDA are both consistent with the Association’s purpose, as stated in the ESARDA Agreement, and address the needs of the ESARDA members and/or stakeholders.In the report, finalized before the ESARDA Symposium in May 2019, three specific goals were identified:establish short term ESARDA priorities (2019 to 2024) and prepare a roadmap - i.e. WHAT;define ESARDA’s long-term future (2019-2050) activities based on the new landscape in Europe and internationally - to be reviewed before 2025 to establish the next 5 year plan; andreview the ESARDA organization, and discuss HOW ESARDA can achieve the identified objectives and implement the identified roadmap.A World Café on these topics was organized and held during the 2019 Symposium. In this paper, the key outcomes and results of the ESARDA Reflection Group 2019 are presented, including their relevance for the international partners of ESARDA.
|
|
| 9. |
- Lehr, Dirk, et al.
(författare)
-
Occupational e-mental health : current approaches and promising perspectives for promoting mental health in workers
- 2016
-
Ingår i: Healthy at work. - Cham : Springer. - 9783319323299 - 9783319323312 ; , s. 257-281
-
Bokkapitel (refereegranskat)abstract
- During the past few years, the Internet has started to change lifestyles and affect all life domains, including working life. It is also increasingly used for targeting mental health issues. The “application of information technology in mental and behavioral health” (Andersson G, Riper H, Carlbring P (2014) Editorial: Introducing Internet interventions—a new open access journal. Internet Intervent 1:1–2) is becoming common in health-care; interventions have already been incorporated into routine care in countries such as the Netherlands, Sweden, the UK, Australia, and the USA. As a next step, Internet interventions in the area of occupational health are progressively emerging. They may offer an evidence-based, cost-effective, and convenient way of promoting workers’ mental health on a large scale. Currently, Internet interventions for workers are the most promising approach in the field of occupational e-mental health. The evolution of occupational e-mental health is embedded in interdisciplinary research, practice, and policy. In the first section of this chapter, the origins of occupational e-mental health will be outlined and a definition proposed. Following this, different approaches to occupational e-mental health will be described and their potentials elucidated. A comparison between Internet interventions and traditional stress-management trainings will provide further insights into the design and characteristics of the most elaborated approach in occupational e-mental health. Subsequently, various Internet training programs will be introduced and the evidence for their efficacy summarized. Finally, important topics for further research and implementation will be outlined.
|
|
| 10. |
- Leon, Miguel
(författare)
-
Enhancing Differential Evolution Algorithm for Solving Continuous Optimization Problems
- 2016
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Differential Evolution (DE) has become one of the most important metaheuristics during the recent years, obtaining attractive results in solving many engineering optimization problems. However, the performance of DE is not always strong when seeking optimal solutions. It has two major problems in real world applications. First, it can easily get stuck in a local optimum or fail to generate better solutions before the population has converged. Secondly, its performance is significantly influenced by the control parameters, which are problem dependent and which vary in different regions of space under exploration. It usually entails a time consuming trial-and-error procedure to set suitable parameters for DE in a specific problem, particularly for those practioners with limited knowledge and experience of using this technique. This thesis aims to develop new DE algorithms to address the two aforementioned problems. To mitigate the first problem, we studied the hybridization of DE with local search techniques to enhance the efficiency of search. The main idea is to apply a local search mechanism to the best individual in each generation of DE to exploit the most promising regions during the evolutionary processs so as to speed up the convergence or increase the chance to scape from local optima. Four local search strategies have been integrated and tested in the global DE framework, leading to variants of the memetic DE algorithms with different properties concerning diversification and intensification. For tackling the second problem, we propose a greedy adaptation method for dynamic adjustment of the control parameters in DE. It is implemented by conducting greedy search repeatedly during the run of DE to reach better parameter assignments in the neighborhood of a current candidate. The candidates are assessed by considering both, the success rate and also fitness improvement of trial solutions against the target ones. The incorporation of this greedy parameter adaptation method into standard DE has led to a new adaptive DE algorithm, referred to as Greedy Adaptive Differential Evolution (GADE). The methods proposed in this thesis have been tested in different benchmark problems and compared with the state of the art algorithms, obtaining competitive results. Furthermore, the proposed GADE algorithm has been applied in an industrial scenario achieving more accurate results than those obtained by a standard DE algorithm.
|
|
