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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

Neural Computing and Applications 2024年第30期36卷 18841-18862页

作者： Mamalakis, Michail Banerjee, Abhirup Ray, Surajit Wilkie, Craig Clayton, Richard H. Swift, Andrew J. Panoutsos, George Vorselaars, Bart Department of Psychiatry University of Cambridge Herchel Smith Building Robinson Way CambridgeCB2 0SZ United Kingdom Department of Computer Science University of Sheffield 211 Portobello SheffieldS1 4DP United Kingdom Insigneo Institute for in silico Medicine University of Sheffield The Pam Liversidge Building SheffieldS1 3JD United Kingdom School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield SheffieldS10 2HQ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Mathematics and Physics University of Lincoln Brayford Pool LincolnLN6 7TS United Kingdom Department of Engineering Science Institute of Biomedical Engineering University of Oxford OxfordOX3 7DQ United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. © Crown 2024.

关键词： COVID-19

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments

Numerical approach to design of low frequency magnetic field...

引用

ELEKTRO

作者： Roman Radil Marek Bajtoš Lucia Č arnecká Klaudia Hargaš ová Patrik Kamencay Department of Electromagnetic and Biomedical Engineering University of Zilina Zilina Slovakia Department of Systems Biology and Engineering Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Dept. of Electromagnetic and Biomedical Engineering Organization University of Zilina Zilina Slovakia

ISBN: (数字)9781665467261

ISBN: (纸本)9781665467278

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments.

关键词： Radiation effects Numerical models Magnetic fields Electromagnetics

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Monitoring of Energy Processes in the Electrohydraulic Complex with a Variable-Frequency Electric Drive in Unsteady Operation Modes

Monitoring of Energy Processes in the Electrohydraulic Compl...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Mohamed Zaidan Qawaqzeh Viktoriya Kovalchuk Tetyana Korenkova Oleksandr Serdiuk Oleksandr Bilyk Department of Electrical and Electronics Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department Of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350346848

An energy model of an electrohydraulic complex has been developed. It allows analyzing the energy processes in the power channel in dynamic modes during the development of cavitation and turbulent oscillations in the pipeline network. It has been shown that the nature of energy processes in the considered cases is periodic constant power fluctuations in the analyzed time interval. The variable component in technological and energy signals of the electrohydraulic complex has been assessed. Their frequency analysis has been performed. The coefficient of power controllability of the electrohydraulic complex and its critical values, whereat the development of emergency modes is possible, have been determined.

关键词： Time-frequency analysis Pipelines Process control controllability Hydrodynamics Harmonic analysis Mathematical models

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Object detection and ranging for autonomous navigation of mobile robots

arXiv

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arXiv 2021年

作者： Zim, Md Ziaul Haque Das, Nimai Chandra Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Automatic Control Systems Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

In the recent decade, electronic technology gets advanced day by day the methodologies too should update. For the purpose of ranging various methods such Radio Detection and Ranging (RADAR), Light Detection and Ranging (LIDAR) and Sonic Navigation and Ranging (SONAR) etc. are used. Later, by adapting the earlier technologies and further modifying the purposes of detection and ranging in navigation, the technology of Sonic Detection and Ranging (SODAR) is used in modern robotics. The SODAR can be defined as a child of SONAR and also a twin of Echo sounder. The echo-sounder is used only for ranging. But the SODAR use the low-frequency wave of 33 kHz to measure the underwater depth and also to detect the objects below the water medium. So, this work comprises the designing of a system to evaluate the Object Detection and Ranging for Autonomous Navigation of Mobile Robots. © 2021, CC BY-NC-ND.

关键词： Mobile robots

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Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping

arXiv

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arXiv 2022年

作者： Rikos, Apostolos I. Oliva, Gabriele Hadjicostis, Christoforos N. Johansson, Karl H. The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Unit of Automatic Control Department of Engineering Università Campus Bio-Medico di Roma via Álvaro del Portillo 21 Rome00128 Italy The Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

In this paper, we present a distributed algorithm which implements the k-means algorithm in a distributed fashion for multi-agent systems with directed communication links. The goal of k-means is to partition the network’s agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities. Transmitting quantized values leads to more efficient usage of the available bandwidth and reduces the communication bottleneck. Also, in order to preserve available resources, nodes are able to distributively determine whether they can terminate the operation of the proposed algorithm. We characterize the properties of the proposed distributed algorithm and show that its execution (on any static and strongly connected digraph) will partition all agents to mutually exclusive clusters in finite time. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm. © 2022, CC BY.

关键词： K-means clustering

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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High throughput event filtering: The interpolation-based DIF algorithm hardware architecture

引用

Microprocessors and Microsystems 2025年

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Poland

In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights(DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 × 720 and 428.45 MEPS for a resolution of 640 × 480. The averagevalues of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.

关键词： Dataset Denoising DVS FPGA High-throughput

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Development of the System of Initial Excitation of the Autonomous Induction Generator 2nd

Development of the System of Initial Excitation of the Auton...

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2nd Electric Power and Renewable Energy Conference, EPREC 2021

作者： Chenchevoi, Volodymyr Kuznetsov, Valeriy Zachepa, Iurii Chornyi, Oleksii Chencheva, Olga Kuznetsov, Vitaliy Yatsiuk, Rostyslav Luhova, Olha Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Electric Power Department Railway Research Institute Chlopickiego str. 50 Warsaw Poland Manufacturing Engineering Department Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Department of the Electrical Engineering and Electromechanic National Metallurgical Academy of Ukraine Gagarina ave. 4 Dnipro Ukraine Department of Automation and Computer-Integrated Technologies Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine Department of Electric Machines and Devices Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Kremenchuk Ukraine

ISBN: (纸本)9789811692383

The article defines the conditions of self-excitation of an inductive generator with capacitor excitation as part of an autonomous power source. Recommendations for the design of an autonomous power source based on an asynchronous machine in terms of the implementation of self-excitation conditions and optimal operating modes are formulated. A simulation model of an autonomous inductive generator and a solar battery for the analysis of transient, static and dynamic modes of operation has been developed. The conducted theoretical studies of capacitive self-excitation of inductive machines have created a good basis for solving problems related to the practical use of autonomous sources based on an inductive generator. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Asynchronous generators

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

引用

arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Cybersecurity Threat Detection in the Behavior of IoT Devices: Analysis of Data Mining Competition Results

Cybersecurity Threat Detection in the Behavior of IoT Device...

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Federated Conference on computer Science and Information systems (FedCSIS)

作者： Michał Czerwiński Marcin Michalak Piotr Biczyk Błażej Adamczyk Daniel Iwanicki Iwona Kostorz Maksym Brzęczek Andrzej Janusz Marek Hermansa Łukasz Wawrowski Artur Kozłowski QED Software Warsaw Poland Faculty of Mathematics University of Warsaw Warsaw Poland Łukasiewicz Research Network - Institute of Innovative Technologies EMAG Katowice Poland Department of Computer Networks and Systems Silesian<file://Silesian> University of Technology Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Silesian<file://Silesian> University of Technology Gliwice Poland EFIGO sp. z o.o. Katowice Poland

The paper discusses a data science competition centered around the development of an anomaly detection system for IoT devices. The competition utilized a unique environment that allowed for the operation and monitoring of real IoT devices, including scheduling of attacks on these devices. The environment was used to collect the data, which included both normal and attack-induced behavior of IoT devices. The paper presents the background of the competition, the top models submitted, and the competition results. The paper also includes a discussion about restrictions related to the use of synthetic attack data as input for constructing anomaly detection systems.

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