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14th International Conference on Advanced Computer Information Technologies, ACIT 2024

作者： Hoholyuk, Oksana Paranchuk, Yaroslav Stakhiv, Petro Shabatura, Yuriy Kuznyetsov, Oleksiy Rudyy, Taras Lviv Polytechnic National University Institute of Power Engineering and Control Systems Lviv Ukraine Lviv Polytechnic National University Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Lviv Ukraine Hetman Petro Sahaidachnyi National Army Academy Department of Electromechanics and Electronics Lviv Ukraine Lviv State University of Internal Affairs Lviv Department of Informatics Ukraine

ISBN: (纸本)9798350350036

A double-loop structure of the high-speed fuzzy control of the electric arc furnace (AF) electric mode (EM) coordinates for the equalization of phase loads is proposed. System-technical solutions have been developed to increase the control speed of the EM coordinates and the implementation of phase-by-phase independent adjustment of the arc lengths. The components of the system control vector for the implementation of multi-criteria optimization of melting modes are substantiated. The proposed solutions are aimed at comprehensive improvement of the energy efficiency as well as the electromagnetic compatibility of AF and power grid modes. A computer Simulink-model of the developed double-loop AF EM control system was created and a study of the efficiency of the proposed solutions was carried out. A significant decrease of the phase loads asymmetry coefficient, a decrease in the of the EM coordinate dispersion, as well as an improvement in the indicators of electrotechnological efficiency and electromagnetic compatibility of the AF and the power grid are confirmed by the analysis of the results of their use. © 2024 IEEE.

关键词： Electric arc furnaces

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Adversarial Attacks Against Uncertainty Quantification

Adversarial Attacks Against Uncertainty Quantification

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Emanuele Ledda Daniele Angioni Giorgio Piras Giorgio Fumera Battista Biggio Fabio Roli Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electric and Electronic Engineering University of Cagliari Italy

Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under the assumption that such attacks exhibit a higher prediction uncertainty than pristine data, it has been shown that adaptive attacks specifically aimed at reducing also the uncertainty estimate can easily bypass this defense mechanism. In this work, we focus on a different adversarial scenario in which the attacker is still interested in manipulating the uncertainty estimate, but regardless of the correctness of the prediction; in particular, the goal is to undermine the use of machine-learning models when their outputs are consumed by a downstream module or by a human operator. Following such direction, we: (i) design a threat model for attacks targeting uncertainty quantification; (ii) devise different attack strategies on conceptually different UQ techniques spanning for both classification and semantic segmentation problems; (iii) conduct a first complete and extensive analysis to compare the differences between some of the most employed UQ approaches under attack. Our extensive experimental analysis shows that our attacks are more effective in manipulating uncertainty quantification measures than attacks aimed to also induce misclassifications.

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Adversarial Attacks Against Uncertainty Quantification

arXiv

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arXiv 2023年

作者： Ledda, Emanuele Angioni, Daniele Piras, Giorgio Fumera, Giorgio Biggio, Battista Roli, Fabio Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Electric and Electronic Engineering University of Cagliari Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy

Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantification has been recently proposed to detect adversarial inputs, under the assumption that such attacks exhibit a higher prediction uncertainty than pristine data, it has been shown that adaptive attacks specifically aimed at reducing also the uncertainty estimate can easily bypass this defense mechanism. In this work, we focus on a different adversarial scenario in which the attacker is still interested in manipulating the uncertainty estimate, but regardless of the correctness of the prediction;in particular, the goal is to undermine the use of machine-learning models when their outputs are consumed by a downstream module or by a human operator. Following such direction, we: (i) design a threat model for attacks targeting uncertainty quantification;(ii) devise different attack strategies on conceptually different UQ techniques spanning for both classification and semantic segmentation problems;(iii) conduct a first complete and extensive analysis to compare the differences between some of the most employed UQ approaches under attack. Our extensive experimental analysis shows that our attacks are more effective in manipulating uncertainty quantification measures than attacks aimed to also induce misclassifications. © 2023, CC BY.

关键词： Forecasting

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Smart Approaches by Online Monitoring in Transportation 9th

Smart Approaches by Online Monitoring in Transportation

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9th International Conference on Mobility, IoT and Smart Cities, EAI Mobility IoT 2022

作者： Knapčíková, Lucia Behúnova, Annamária Husár, Jozef Tauberová, Rebeka The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Košice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Košice Slovakia

ISBN: (纸本)9783031282249

Current trends in the transport industry, in the area of computerization and digitalization, are the use of intelligent sensors for the control of wagons, the control of railway lines, and the automatic control of trains. Emerging solutions seek to combine biometric information, artificial intelligence (UI), and cloud computing. For train operators and railway companies, Internet of Things (IoT) devices enable the implementation of predictive maintenance to monitor any anomalies. Support for developing intelligent systems and their complex solutions is based on information and communication systems and technologies in transport. It is oriented toward the use of a unified system environment for the collection, processing, sharing, distribution, and use of traffic information in specific information and control applications. The paper deals with the possibilities of online control and the use of intelligent sensors in the measurement and control of various load-bearing and driving rotating chassis. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligent systems

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Noise Reduction in Industry Based on Virtual Instrumentation

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Computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of Computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

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Classifying Fault Category and Severity of UAV Flight controllers’ Reported Issues

Classifying Fault Category and Severity of UAV Flight Contro...

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International Conference on System Reliability and Science (ICSRS)

作者： Anamta Khan Maria Lucia Ferramosca Naghmeh Ivaki Henrique Madeira Department of Informatics Engineering University of Coimbra Centre for Informatics and Systems of the University of Coimbra Coimbra Portugal Department of Control and Computer Engineering Polytechnic of Turin Italy

Unmanned Ariel Vehicles (UAVs) have gained significant importance in diverse sectors. Thus, a profound safety risk analysis/assessment to prevent any possible damage to themselves, the environment, and humans is fundamental for building and utilizing UAVs. To achieve that, two fundamental challenges should be addressed: i) identification of types and frequency of the issues and ii) assessment of their impact. In this paper, we aim to address the first challenge by automatizing the process of data field analysis. To do so, we first performed some statistical analysis of the reported issues of UAV systems (in Github) and manually extracted detailed data from the reports to better understand the type and nature of the issues. Then, to automatize the analysis, we used natural language processing algorithm to extract the keywords from the reports, and then applied four machine learning algorithms to build classifier models to classify the reports according to the fault category and severity level. The good performance results obtained suggest that these analyzes can be performed to further understand the UAV system issues, and help in the risk assessment procedure to identify the hazard and define the frequency and severity of the risk. Moreover, the results of this work can help a big community of developers and researchers in the precise and fast analysis of bug reports and safety risk assessment of any software system.

关键词： Fault diagnosis Machine learning algorithms Statistical analysis Computer bugs Software systems Classification algorithms Autopilot

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Exploring Multiple Perspectives on Psychological Health of Adolescents in Relation to Gender and School Grade—Jiangsu Province,China,2022

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China CDC weekly 2024年第29期6卷 713-718页

作者： Jie Yang Ran Zhang Keyin Zhou Jinshuai Zhou Xin Wang Wenyi Yang Xiyan Zhang Xiao Xu Lifei Wang Yufei Wu Jiaxin Li Xizhe Zhang Yang Wang Yuan Lin Fei Wang School of Public Health Nanjing Medical UniversityNanjing CityJiangsu ProvinceChina Jiangsu Provincial Center for Disease Control and Prevention Nanjing CityJiangsu ProvinceChina Early Intervention Unit Department of PsychiatryThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjing CityJiangsu ProvinceChina Functional Brain Imaging Institute of Nanjing Medical University Nanjing CityJiangsu ProvinceChina School of Biomedical Engineering and Informatics Nanjing Medical UniversityNanjing CityJiangsu ProvinceChina.

Summary What is already known about this topic?Mental health issues in Chinese children and adolescents have emerged as a substantial public health concern,causing distress and strain among families and *** is added by this report?This study examines the effects of gender and school grade on mental health symptoms and risky behaviors among Chinese children and adolescents,with a particular focus on the role of family and school environments.

关键词： Jiangsu distress emerged

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