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Parameter fault diagnosis in heat exchange networks with distributed time delay

IFAC-PapersOnLine 2022年第18期55卷 39-44页

作者： Wijaya Kurniawan Katalin M. Hangos Lőrinc Márton Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Systems and Control Laboratory Institute for Computer Science and Control P.O. Box 63 H-1518 Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Tirgu Mures Romania

This paper deals with parameter fault diagnosis in heat exchange networks (HENs) with joining and splitting connections where the change in the heat transfer coefficient is considered as fault. The fault diagnosis oriented model of the HEN elements was developed based on the equivalent LTI realization of distributed delay models. The Signed Directed Graph (SDG) method is used to derive the fault observability conditions. The presence of faults induces bilinear fault-input terms into the system model. Thus, a nonlinear adaptive observer was proposed for fault diagnosis. To verify and validate the proposed method, a case study is presented. The simulation results show that the observers are successfully detecting and estimating the faults and unknown system states.

关键词： Fault diagnosis Heat Exchange Networks, Distributed delay, Process systems

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A Novel DWT-based Encoder for Human Pose Estimation

A Novel DWT-based Encoder for Human Pose Estimation

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2022 Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2022

作者： De Magistris, Giorgio Romano, Matteo Starczewski, Janusz Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Computational Intelligence Czstochowa University of Technology al. Armii Krajowej 36 Częstochowa42-200 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The proposed approach for pose estimation is based on the construction of a Convolutional Neural Network with an encoding-decoding structure and a spatial pyramid based on WASP structure in its bottleneck and a Discrete wavelet transform encoder. These techniques already shown their capabilities to solve the main problems in state of the art related to: different Field of view (FoV) required to analyze the different possible sizes of a specific subject. we want to solve the faulty structure of the modern CNN based Neural Networks in the encoding part using DWT encoder and WASP. This Work also have the objective of demonstrating from a more general point of view which could be the advantages of a Discrete Wavelet Transform (DWT) encoder in any CNN-based approach for Pose Estimation and Object detection in any form, such as for several subjects in the same image or in the internal video due to the almost redundant use of the usual most famous encoding structures for CNN such as ResNet-101, U-Net or VGG16-19. we will do our tests using a U-net Based CNN in order to evaluate the importance of the results of the Discrete Wavelet Transform encoder also in the decoding part through the cropping of theme at the last layers of the network. This is necessary due to the loss of border's pixels during encoding that could be useful for the result's evaluation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Discrete wavelet transforms

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Structural Regularization Based Discriminative Multi-View Unsupervised Feature Selection

SSRN

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SSRN 2023年

作者： Zhou, Shixuan Song, Peng Yu, Yanwei Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Technology Ocean University of China Qingdao266400 China Key Laboratory of Child Development and Learning Science Ministry of Education Southeast University Nanjing210096 China

Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models. © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Deep Learning Algorithms for Detecting Freezing of Gait in Parkinson's Disease: A Cross-Dataset Study

SSRN

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SSRN 2024年

作者： Sigcha, Luis Borzì, Luigi Olmo, Gabriella University of Limerick LimerickV94 T9PX Ireland Group Department of Electronic and Computer Engineering University of Limerick LimerickV94 T9PX Ireland ANTHEA Lab Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy PolitoBIOMed Lab Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy

Freezing of gait (FoG) is a complex and disabling symptom of Parkinson’s disease (PD), which has a significant impact on the quality of life of people with PD (PwPD) and increases the risk of falls and related injuries. This study aims to evaluate the generalization capability of deep learning (DL) algorithms in FoG detection. To address this task, various machine learning (ML) and deep learning (DL) algorithms were implemented, fine-tuned, and evaluated using diverse data splitting and validation strategies. The experiments performed yielded mixed results. Although the implementations demonstrated competitive performance in single-dataset settings, all approaches showed limited robustness in cross-dataset tests and suboptimal generalization across different datasets. These results highlight the importance of standardized data collection procedures to ensure uniformity. The specification of sensor settings and predefined sensor locations can foster homogeneity in datasets, even when dealing with diverse subjects and environments. Such standardization efforts are crucial for advancing generalized methodologies in the detection of FoG applicable to both research and clinical applications. © 2024, The Authors. All rights reserved.

关键词： Wearable sensors

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Microsaccade-inspired Event Camera for Robotics

arXiv

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arXiv 2024年

作者： He, Botao Wang, Ze Zhou, Yuan Chen, Jingxi Singh, Chahat Deep Li, Haojia Gao, Yuman Shen, Shaojie Wang, Kaiwei Cao, Yanjun Xu, Chao Aloimonos, Yiannis Gao, Fei Fermüller, Cornelia Department of Computer Science University of Maryland College ParkMD20742 United States College of Control Science and Engineering Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China College of Optical Science and Engineering Zhejiang University Hangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Institute for Advance Computer Studies University of Maryland College ParkMD20742 United States Institute for Systems Research University of Maryland College ParkMD20742 United States

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem intrinsic to the sensor and therefore challenging to solve algorithmically. Human vision deals with perceptual fading using the active mechanism of small involuntary eye movements - the most prominent ones called microsaccades. By moving the eyes constantly and slightly during fixation, microsaccades can substantially maintain texture stability and persistence. Inspired by microsaccades, we designed an event-based perception system capable of simultaneously maintaining low reaction time and stable texture. In this design, a rotating wedge prism was mounted in front of the aperture of an event camera to redirect light and trigger events. The geometrical optics of the rotating wedge prism allows for algorithmic compensation of the additional rotational motion, resulting in a stable texture appearance and high informational output independent of external motion. The hardware device and software solution are integrated into a system, which we call Artificial MIcrosaccade-enhanced EVent camera (AMI-EV). Benchmark comparisons validate the superior data quality of AMI-EV recordings in scenarios where both standard cameras and event cameras fail to deliver. Various real-world experiments demonstrate the potential of the system to facilitate robotics perception both for low-level and high-level vision tasks. © 2024, CC BY.

关键词： Cameras

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Reliability Assessment of Large DNN Models: Trading Off Performance and Accuracy

Reliability Assessment of Large DNN Models: Trading Off Perf...

引用

IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Junchao Chen Giuseppe Esposito Fernando Fernandes dos Santos Juan-David Guerrero-Balaguera Angeliki Kritikakou Milos Krstic Robert Limas Sierra Josie E. Rodriguez Condia Matteo Sonza Reorda Marcello Traiola Alessandro Veronesi IHP - Leibniz Institute for High-Performance Microelectronics Germany Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Univ Rennes CNRS Inria IRISA - UMR 6074 Rennes France University of Potsdam Germany

ISBN: (数字)9798331539672

ISBN: (纸本)9798331539689

The adoption of Deep Neural Networks (DNNs) in several domains allows for increased effectiveness in applications that deal with massive data-intensive and complex data inputs. When employed in safety-critical scenarios, such as automotive, aerospace, healthcare, and autonomous robotics, assessing the DNNs' reliability and functional safety is crucial to ensure their correct in-field operation, even in the presence of hardware faults. However, the system complexity and the massive amounts of data to be processed by DNNs prevent the effective adoption of traditional strategies for reliability characterization and for identifying the most fault-sensitive structures. Accurate fault assessment strategies usually require unacceptable computational power and large evaluation times. On the other hand, faster strategies commonly lack accuracy in correctly representing system faults. Consequently, it is necessary to develop effective strategies that trade-off between performance and accuracy. This work analyses three reliability assessment strategies for deep neural networks and their underlying hardware, highlighting the main solutions and challenges in terms of evaluation performance and fault characterization accuracy. We overview different solutions to evaluate the hardware accelerators implementing DNNs at three abstraction levels: $i$ ) by physically injecting faults on a GPU running DNNs, ii) by performing microarchitectural characterization of GPUs to develop application-accurate error models, and iii) by using structure-aware cross-layer error modeling on DNN hardware accelerators. Our experimental results indicate that accurate error representation requires structural features from the targeted hardware.

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Dual-Mode Disturbance Observer-based Predictive control with Harmonic Current Suppression for Dual Three-Phase Permanent Magnet Synchronous Motor Drives

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Liu, Huanli Lin, Weiyang Liu, Zhitai Yu, Xinghu Buccella, Concettina Cecati, Carlo Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China Ningbo Institute of Intelligent Equipment Technology Company Ltd Ningbo315200 China University of L'Aquila DigiPower srl Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy

Dual three-phase permanent magnet synchronous motor drives have gained considerable interest in those applications requiring high levels of reliability. However, they often suffer from high-order harmonic currents and mismatch of motor parameters, resulting in degraded performance and reduced efficiency. Therefore, this paper proposes a dual-mode disturbance observer based model predictive current controller. Not only direct current disturbances caused by motor parameters, but also alternating current disturbances caused by high-order harmonic currents are explicitly modeled and estimated to achieve improved model accuracy. In addition, the observer parameter design is given in detail. Furthermore, a generalized predictive current controller with the integration of the proposed observer is developed to provide satisfactory harmonic current suppression capability and parameter robustness simultaneously. Proof of the stability with the proposed controller is given and several experiments and comparisons demonstrate the effectiveness of the proposed method. © 2013 IEEE.

关键词： Permanent magnets

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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A Coalitional Game for Demand-Side Management in a Low-Voltage Resistive Micro-Grid with Multiple Electricity Retailers

arXiv

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arXiv 2024年

作者： Mendoza, Fernando Genis Baldivieso-Monasterios, Pablo R. Bauso, Dario Konstantopoulos, George The Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering University of Patras Rion26500 Greece The Jan C. Willems Center for Systems and Control ENTEG Fac. Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The Netherlands and Dipartimento di Ingegneria Università di Palermo viale delle Scienze Palermo Italy

An existing challenge in power systems is the implementation of optimal demand management through dynamic pricing. This paper encompasses the design, analysis and implementation of a novel on-line pricing scheme based on coalitional game theory. The setting consists of a network with multiple energy retailers competing to attract consumers by announcing a price in a hierarchical leader-follower structure. The process of coalition formation under such a pricing scheme can be viewed as a game for which we show that a Stackelberg equilibrium exists, i.e., given a price, consumers will respond by conforming to a reciprocal power consumption quantity. We propose a coalition formation algorithm and perform a game-theoretic stability analysis on the resulting coalitions. We integrate the pricing setting with a resistive micro-grid dynamic model. In this context we analyse the behaviour of the integrated system, bridging the gap between market and physical layers of the problem. Simulations provide a comparison of profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards a steady-state equilibrium. Additionally, we shed light into the system's physical response when subject to our proposed pricing scheme. © 2024, CC BY.

关键词： Costs

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Empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation 3rd

Empirical Based Irrigation Model Using Predicted Soil Moistu...

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3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ramli, Muhammad Shahrul Azwan Abidin, Mohamad Shukri Zainal Hean, Pui Boon Rahman, Mohd Amiruddin Abd Perumal, Thinagaran Reba, Mohd Nadzri Md Division of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai Malaysia IoT Department MIE Industrial Sdn. Bhd Selangor Puchong Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Skudai Malaysia

ISBN: (纸本)9789811939228

It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

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