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Robust fault detection, location, and recovery of damaged data using linear regression and mathematical models

IFAC-PapersOnLine 2018年第24期51卷 300-306页

作者： Latocha, A. AGH University of Science and Technology Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow al. Mickiewicza 30 30-059 Poland

A new method using linear regression and mathematical models is proposed for fault detection, location and the recovery (FDIR) of damaged data. Linear regression was used to identify the mathematical model of the system from source data. For this reason the paper focuses on the class of fault whose impact on the system causes that the measured data are differ from the expected values. Based on the mathematical model of the process developed during identification from source data, the damage area can be pre-located, using proposed recurrence algorithm of fault recovery (FR) we can exactly estimated expected value on the damage area. Based on estimated expected value the damage area can be precisely located and the class of faults can be estimated. This paper proposes an innovative approach to recurrence self-tuning of fault recovery (FR) from parts incomplete or damaged data of the pre-allocated area. © 2018

关键词： Fault detection

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Multiple plans are better than one: Diverse stochastic planning

arXiv

引用

arXiv 2020年

作者： Ghasemi, Mahsa Crafts, Evan Scope Zhao, Bo Topcu, Ufuk Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin AustinTX78712 United States

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human’s preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative — small and diverse — behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Specifications

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Kinect-based human gait recognition using a novel adaptive dynamics learning approach

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ASME 2019 Dynamic Systems and Control Conference, DSCC 2019

作者： Chen, Xiaotian Stegagno, Paolo Dong, Xiaonan Yuan, Chengzhi Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI02881 United States Department of Mechanical Industrial and Systems University of Rhode Island KingstonRI02881 United States

ISBN: (纸本)9780791859148

This paper proposes a human gait recognition method based on a novel adaptive dynamics learning approach (i.e., deterministic learning) and the Kinect depth camera. First, a series of real-time human joint position data is captured and collected by using Microsoft Kinect and Robot Operating System (ROS) packages. Second, the collected position data is converted to four time-varying features that could best represent the human gait dynamics. Third, the human gait dynamics associated with the four features from each gait pattern are effectively modeled using a novel discrete-time adaptive dynamics learning algorithm. A gait dynamics database is developed, based on which a multimodel recognition scheme is implemented to realize real-time rapid recognition of human gait. Finally, a Python-based GUI is created by integrating all the algorithmic components to facilitate user operations and visualizations. In the experiment study, by using the 2-fold, 10-fold cross-validation, and leave-one-out validation approaches, the recognition success rates are reported to be average 93.7% based on our proposed method, which outperforms other state-of-the-art machine learning approaches. Copyright © 2019 ASME.

关键词： Robot Operating System

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Low cost laboratory plant for control system education

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IFAC-PapersOnLine 2018年第6期51卷 289-294页

作者： Docekal, Tomas Golembiovsky, Matej Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava 17.listopadu 15 Ostrava Czech Republic

This article deals with design and development of low cost laboratory plant for control system education. The apparatus includes DC motor with incremental quadratic encoder. The Arduino platform is used to realize digital control system. It is also possible to use Raspberry Pi to interact with laboratory plant and REX Control System to design control algorithm. © 2018

关键词： Costs DC motors Digital control systems Embedded systems Microcontrollers Microprocessor chips Arduino Arduino platforms Control system education Design and Development Design control algorithms Low costs Raspberry pi

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Evaluating the effectiveness of the "Individual Assistance Plan" for Italian chronic patients

Evaluating the effectiveness of the "Individual Assistance P...

引用

Mediterranean Electrotechnical Conference (MELECON)

作者： Anna Alloni Daniele Santonastaso Marco Villa Matteo Gabetta Mauro Bucalo Silvana Quaglini Biomeris s.r.l. Pavia Italy DSP Solutions Milanese (MI) Italy ATS Valpadana Cremona Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

The aim of this work is the evaluation of a relatively new intervention for chronic patients in Italy, namely the PAI, i.e., "Piano Assistenziale Individuale" (Individual Care Plan). It is a service based on the paradigm of the personalised medicine, which should optimize several aspects of the individual care, such as patients' compliance to therapy, ease of access to care delivery, and a tighter monitoring of the patient's status on the long run. The expected outcomes from the PAI introduction are both the improvement of the patients' status and reduction of costs for the care provider. A case-control study has been performed, involving more than 20000 patients, and preliminary results seem to confirm the effectiveness of the new service, in particular by reducing patients' access to hospital and emergency room.

关键词： Pathology Sociology Statistics Monitoring Hospitals Task analysis

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Deep Learning in Cardiology

arXiv

引用

arXiv 2019年

作者： Bizopoulos, Paschalis Koutsouris, Dimitrios Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use. Copyright © 2019, The Authors. All rights reserved.

关键词： Cardiology

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The Application of Virtual Logic Models to Simulate Real Environment for Testing Advanced Driving-Assistance Systems

The Application of Virtual Logic Models to Simulate Real Env...

引用

International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Michal Pikus Jaroslaw Was Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland

Advanced driving-assistance systems (ADAS) have become more popular and received more aid in automotive business. ADAS systems evolution requires more efforts to prepare efficient and reliable systems. Because of safety restrictions we need to validate systems by driving over several million kilometers in different conditions. That is why research, development and verification processes are highly required. The best solution to solve this problem are computer simulations. Application of digital scenarios saves time, money and human resources. There are many simulators which are developed by automotive companies. In our paper we would like to present set of simulators which are used to simulate and validate camera sensors, control and path planning algorithms for ADAS. Our tools are able to simulate/emulate vehicle components and road environment elements. However, mainly in case of ADAS systems this is a difficult task. Our paper presents approaches the methodology of virtual simulations [1].

关键词： Software Sensors Hardware Automobiles Roads Safety

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Detection of hard exudates in fundus images using convolutional neural networks 7

Detection of hard exudates in fundus images using convolutio...

引用

7th International Conference on Green and Human Information Technology, ICGHIT 2019

作者： Poonkasem, Ittided Theera-Umpon, Nipon Auephanwiriyakul, Sansanee Patikulsila, Direk Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand Graduate School Chiang Mai University Chiang Mai Thailand Department of Electrical Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Ophthalmology Faculty of Medicine Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9781728106274

The patients with diabetes have a chance to have blindness. An impairment of metabolism can cause a high glucose level in blood vessel leading to an abnormality called hard exudates. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research is to detect hard exudates by applying image processing techniques and classify them by using convolutional neuron network (CNN). DIARETDB1 dataset is used in the experiments. The proposed method achieves the area under the curve (AUC) of 0.97 and 0.95 on the training and validation sets, respectively, of 10-fold cross validation experiment. These show that the combination of image processing techniques, three channels of fundus images, and CNN can perform as a promising classification tool in hard exudates detection system. © 2019 IEEE.

关键词： Blood vessels

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Signal2Image Modules in Deep Neural Networks for EEG Classification

arXiv

引用

arXiv 2019年

作者： Bizopoulos, Paschalis Lambrou, George I. Koutsouris, Dimitrios Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as ‘base models’. We compare the accuracy and time performance of four S2Is (‘signal as image’, spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of ‘base models’ (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and present visual comparisons of the outputs of some of the S2Is. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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Baseline Photos and Confident Annotation Improve Automated Detection of Cutaneous Graft-Versus-Host Disease

引用

Clinical hematology international 2021年第3期3卷 108-115页

作者： Xiaoqi Liu Kelsey Parks Inga Saknite Tahsin Reasat Austin D Cronin Lee E Wheless Benoit M Dawant Eric R Tkaczyk Department of Veterans Affairs Tennessee Valley Healthcare System Dermatology Service 1310 24th Avenue South Nashville TN 37212-2637 USA. Department of Electrical Engineering and Computer Science Vanderbilt University 361 Jacobs Hall Nashville TN 37235-1662 USA. Vanderbilt Dermatology Translational Research Clinic (***) Department of Dermatology Vanderbilt University Medical Center 719 Thompson Lane One Hundred Oaks Suite 26300 Nashville TN 37204 USA. Department of Biomedical Engineering Vanderbilt University Nashville TN USA.

Cutaneous erythema is used in diagnosis and response assessment of cutaneous chronic graft-versus-host disease (cGVHD). The development of objective erythema evaluation methods remains a challenge. We used a pre-trained neural network to segment cGVHD erythema by detecting changes relative to a patient's registered baseline photo. We fixed this change detection algorithm on human annotations from a single photo pair, by using either a traditional approach or by marking definitely affected ("Do Not Miss", DNM) and definitely unaffected skin ("Do Not Include", DNI). The fixed algorithm was applied to each of the remaining 47 test photo pairs from six follow-up sessions of one patient. We used both the Dice index and the opinion of two board-certified dermatologists to evaluate the algorithm performance. The change detection algorithm correctly assigned 80% of the pixels, regardless of whether it was fixed on traditional (median accuracy: 0.77, interquartile range 0.62-0.87) or DNM/DNI segmentations (0.81, 0.65-0.89). When the algorithm was fixed on markings by different annotators, the DNM/DNI achieved more consistent outputs (median Dice indices: 0.94-0.96) than the traditional method (0.73-0.81). Compared to viewing only rash photos, the addition of baseline photos improved the reliability of dermatologists' scoring. The inter-rater intraclass correlation coefficient increased from 0.19 (95% confidence interval lower bound: 0.06) to 0.51 (lower bound: 0.35). In conclusion, a change detection algorithm accurately assigned erythema in longitudinal photos of cGVHD. The reliability was significantly improved by exclusively using confident human segmentations to fix the algorithm. Baseline photos improved the agreement among two dermatologists in assessing algorithm performance.

关键词： Cutaneous erythema baseline photos change detection chronic graft-versus-host disease computer vision convolutional neural networks longitudinal photos

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