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Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis +
<p>The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage–current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations. © 2013 Wiley Periodicals, Inc.</p> +
<p>We have implemented an algorithm for detection and segmentation of protein spots in 2-D gel electrophoresis images using symmetry derivative features computed using low level image processing operations. The implementation was compared with a previously published Watershed segmentation and a commercial software. Our algorithm was found to yield segmentation results that were either better than or comparable to the other solutions while having fewer free parameters and a low computational cost. © Springer-Verlag Berlin Heidelberg 2005.</p> +
<p>Effective and creative cyber-physical systems (CPS) development requires expertise in disparate fields that have traditionally been taught in several distinct disciplines. At the same time, students seeking a CPS education generally come from diverse educational backgrounds. In this paper, we report on our recent experience developing and teaching a course on CPS. The course addresses the following three questions: What are the core elements of CPS? How should these core concepts be integrated in the CPS design process? What types of modeling tools can assist in the design of cyber-physical systems? Our experience with the first three offerings of the course has been positive overall. We also discuss the lessons we learned from some issues that were not handled well. All material including lecture notes and software used for the course are openly available online.</p> +
<p>The advances in sensing technology provide us with the opportunity to develop mobile and unobtrusive systems to continuously gather gait data. Accelerometers have been shown to be an adequate choice for recording human motion data. For that reason, many previous works have investigated the use of accelerometers for gait analysis. Previous works were able to extract either static temporal information or dynamic general information about the gait patterns. This work aims at extracting both static and dynamic information from acceleration signals. The ability to extract information about the dynamics of gait is exemplified with a novel symmetry measure. The method presented here is based on the motion language approach. A method based on peak detection was chosen as a reference, which we compare to our method. A Gait Rite pressure sensitive mat was used to detect heel-strike and toe-off ground truths. Results show that the proposed approach is as accurate as, more robust than, and conveys more information than the reference method.</p> +
<p>Effective and creative Cyber-Physical Systems (CPS) development requires expertise in disparate fields that have traditionally been taught in several distinct disciplines. At the same time, students seeking a CPS education generally come from diverse educational backgrounds. In this paper, we report on our recent experience of developing and teaching a course on CPS. The course addresses the following three questions: What are the core elements of CPS? How should these core concepts be integrated in the CPS design process? What types of modeling tools can assist in the design of Cyber-Physical Systems? Our experience with the first four offerings of the course has been positive overall. We also discuss the lessons we learned from some issues that were not handled well. All material including lecture notes and software used for the course are openly available online.</p> +
<p>A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.</p> +
<p>Balance between flexor and extensor muscle activity is essential for optimal function. This has been demonstrated previously for the lower extremity, trunk and shoulder function, but information on the relationship in hand function is lacking. AIM: Was to evaluate whether there are qualitative differences in finger extension force(fef), grip force, force duration, force balance and the muscle activities in the forearm flexor and extensor muscles in healthy men and women in different ages. </p> +
<p>This paper compares several strategies for air-fuel ratio tran-sient control. The strategies are: A factory-standard look-up table based system (a SAAB Trionic 5), a feedback PI controller with and without feed-forward throttle correction, a linear feed-forward control algorithm, and two nonlinear feed- forward algorithms based on artificial neural networks. The control strategies have been implemented and evaluated in a SAAB 9000 car during a transient driving test, consisting of an acceleration in the second gear from an engine speed of 1500 rpm to 3000 rpm. The best strategies are found to be the neural network based ones, followed by the table based factory system. The two feedback PI controllers offer the poorest performance.</p> +
<p>In face of escalating health care costs, new technology holds great promise for innovative solutions and new, more sustainable health care models. Technology centers around the individual, allowing for greater autonomy and control in health issues and access to tailored information and customized health behavior interventions. While this offers good opportunities for both public health impact and improved well-being at individual levels, it also emphasizes the need for properly designed e-health models firmly based on scientific principles and adequate theoretical frameworks. Consequently, this project aims to design an interactive tool utilizing an interdisciplinary approach combining motivational theory with the fields of information technology and business model innovation. In collaboration with two companies from the e-health industry, the purpose is to design, apply and evaluate a person-centered interactive prototype for maintainable and self-determined exercise motivation.</p> +
<p>Health care costs are increasing twice as fast as wealth, making health promotion and development of cost-effective care increasingly important in order to generate sustainable health care solutions. E-health, applications and interactive tools for exercise promotion flourish; but despite this and an overflow of information regarding health benefits of regular physical activity, exercise adherence has proven to be a significant challenge. This article concerns a project aimed to design an interactive tool based on comprehensive knowledge from the field of psychology combined with expertise from information technology and innovation, based on e-health industrial requirements and user needs. The research group will, together with the expertise and infrastructure of the collaborating companies Health Profile Institute AB and Tappa Service AB, support and progress an existing PhD-project on digital interventions in exercise motivation. This will be done by designing; applying and evaluating a person-centred digital intervention prototype for exercise motivation and adherence enhancement based on Self-Determination Theory.</p> +
<p><strong>Purpose</strong>:There is a need for scientifically sound and theory based tools and services in e-health. In this project knowledge from the field of psychology will be complemented by expertise in information technology and innovation science in designing a digital intervention based on Self-determination theory (SDT) aiming to facilitate exercise motivation.</p><p><strong>Methods</strong>:The intervention will be tested by a three wave RCT design in a population of e-health clients (n = 200) in a web based exercise service. Sensors (step counters) and self-reports (Godin Leisure-Time Exercise Questionnaire) will be used to measure objective and subjective exercise behavior while instruments based on SDT (Basic Psychological Needs in Exercise Scale and Behavioral Regulation in Exercise Questionnaire-2 ) will measure factors related to motivation. Advanced mediation variable analyses (MVA) and latent growth curve models (LGCM) will be used to explore motivational processes, changes and profiles in relation to exercise behavior.</p><p><strong>Expected Results</strong>:Based on the SDT process model, it is hypothesized that a (digital) environment supporting basic psychological need satisfaction will facilitate internalization and enhanced self-determined motivation, which in turn will have a positive effect on exercise behavior.</p><p><strong>Conclusions</strong>:Clarifying mechanisms and indirect effects provide knowledge of how intervention effects could be interpreted and understood. Combining high level research design like RCT and advanced analyses as MVA provides valuable contributions to the understanding of theoretical mechanisms of motivation that could inform the tailoring of effective interventions promoting healthy exercise behaviours. In addition, the project might form a prosperous interdisciplinary fusion generating innovative and theory based digital solutions for e-health.</p> +
<p>Many low-level features, as well as varyingmethods of extraction and interpretation rely on directionalityanalysis (for example the Hough transform, Gabor filters,SIFT descriptors and the structure tensor). The theoryof the gradient based structure tensor (a.k.a. the secondmoment matrix) is a very well suited theoretical platform inwhich to analyze and explain the similarities and connections(indeed often equivalence) of supposedly different methodsand features that deal with image directionality. Of specialinterest to this study is the SIFT descriptors (histogram oforiented gradients, HOGs). Our analysis of interrelationshipsof prominent directionality analysis tools offers thepossibility of computation of HOGs without binning, in analgorithm of comparative time complexity.</p> +
<p>A novel score-level fusion strategy based on quality measures for multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted every time an authentication claim is performed based on the estimated quality of the sensed biometric signals at this time. Experimental results combining written signatures and quality-labelled fingerprints are reported. The proposed scheme is shown to outperform significantly the fusion approach without considering quality signals. In particular, a relative improvement of approximately 20% is obtained on the publicly available MCYT bimodal database.</p> +
Domain Analysis for Standardised Functional Safety : A Case Study on Design-Time Verification of Automatic Emergency Breaking +
<p>Simulation traditionally computes individual trajectories, which severely limits the assessment of overall system behaviour. To address this fundamental shortcoming, we rely on computing enclosures to determine bounds on system behaviour instead of individual traces. In the present case study, we investigate the enclosures of a generic Automatic Emergency Braking (AEB) system and demonstrate how this creates a direct link between requirement specification and standardized safety criteria as put forward by ISO 26262. The case study strongly supports that a methodology based on enclosures can provide a missing link across the engineering process, from design to compliance testing. This result is highly relevant for ongoing efforts to virtualize testing and create a unified tool-chain for the development of next generation Advanced Driver Assistance Systems.</p> +
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Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors +
<p>Human activity recognition has become an activeresearch field over the past few years due to its wide applicationin various fields such as health-care, smart homemonitoring, and surveillance. Existing approaches for activityrecognition in smart homes have achieved promisingresults. Most of these approaches evaluate real-timerecognition of activities using only sensor activations thatprecede the evaluation time (where the decision is made).However, in several critical situations, such as diagnosingpeople with dementia, “preceding sensor activations”are not always sufficient to accurately recognize theinhabitant’s daily activities in each evaluated time. Toimprove performance, we propose a method that delaysthe recognition process in order to include some sensoractivations that occur after the point in time where thedecision needs to be made. For this, the proposed methoduses multiple incremental fuzzy temporal windows toextract features from both preceding and some oncomingsensor activations. The proposed method is evaluated withtwo temporal deep learning models (convolutional neuralnetwork and long short-term memory), on a binary sensordataset of real daily living activities. The experimentalevaluation shows that the proposed method achievessignificantly better results than the real-time approach,and that the representation with fuzzy temporal windowsenhances performance within deep learning models. © Copyright 2020 IEEE</p> +
<p>Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation.</p> +
<p>Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation. ©2015 IEEE</p> +
Electromyographic Patterns during Golf Swing : Activation Sequence Profiling and Prediction of Shot Effectiveness +
<p>This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.</p>
<p>Feature set decomposition through cluster-based partitioning is the subject of this study. Approach is applied for the detection of mild laryngeal disorder from acoustic parameters of human voice using random forest (RF) as a base classier. Observations of sustained phonation (audio recordings of vowel /a/) had clinical diagnosis and severity level (from 0 to 3), but only healthy (severity 0) and mildly pathological (severity 1) cases were used. Diverse feature set (made of 26 variously sized subsets) was extracted from the voice signal. Feature-and decision-level fusions showed improvement over the best individual feature subset, but accuracy of fusion strategies did not differ signicantly. To boost accuracy of decision-level fusion, unsupervised decomposition for ensemble design was proposed. Decomposition was obtained by feature-space re-partitioning through clustering. Algorithms tested: a) basic k-Means; b) non-parametric MeanNN; c) adaptive anity propagation. Clustering by k-Means signicantly outperformed feature- and decision-level fusions.</p> +
<p>The development, testing and evaluation of novel approaches to Intelligent Environment data processing require access to datasets which are of high quality, validated and annotated. Access to such datasets is limited due to issues including cost, flexibility, practicality, and a lack of a globally standardized data format. These limitations are detrimental to the progress of research. This paper provides an overview of the Open Data Initiative and the use of simulation software (IE Sim) to provide a platform for the objective assessment and comparison of activity recognition solutions. To demonstrate the approach, a dataset was generated and distributed to 3 international research organizations. Results from this study demonstrate that the approach is capable of providing a platform for benchmarking and comparison of novel approaches.</p> +
