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SSRN 2024年

作者： Zhang, Qian Zhu, Yi Cordeiro, Filipe R. Chen, Qiu School of Information Technology Jiangsu Open University Nanjing210036 China Department of Electrical Engineering and Electronics Graduate School of Engineering Kogakuin University Tokyo163-8677 Japan Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil

Large-scale image datasets frequently contain unavoidable noisy labels, resulting in overfitting in deep neural networks and declining performance. Most existing methods for learning from noisy labels operate as one-stage frameworks, where training data division and semi-supervised learning (SSL) are intertwined for optimization. Accordingly, their effectiveness is significantly influenced by the precision of the separated clean set, prior knowledge of noise, and the robustness of SSL. In this paper, we propose a progressive sample selection framework with contrastive loss for noisy labels named PSSCL. This framework operates in two stages, using robust and contrastive losses to augment the robustness of the model. The first stage focuses on identifying a small clean set through a long-term confidence detection strategy, while the second stage aims to enhance performance by expanding this clean set. PSSCL demonstrates significant improvement across various benchmarks when compared with state-of-the-art methods. The code will be available at https://***/LanXiaoPang613/PSSCL. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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A Holistic Approach to Rotation Synthesis for Fault- Tolerant Quantum Computation

A Holistic Approach to Rotation Synthesis for Fault- Toleran...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Tian-Fu Chen Cheng-Han Liu Jie-Hong R. Jiang Graduate School of Advanced Technology National Taiwan University Taipei Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Rotation operations are essential ingredients in quantum algorithms. However, fault-tolerant quantum computation is intrinsically digital, based on a finite set of primitive gates, and thus cannot provide an analog rotation operation with arbitrary angles. It must resort to approximating a rotation operation using a basic universal gate set. Prior work on rotation synthesis either focuses on synthesizing individual rotations without considering a quantum circuit as a whole or is restricted to the specific quantum Fourier transform circuit. In this work, we develop a holistic and general approach to rotation synthesis, taking all rotation operations of a circuit into account for quantum cost minimization. Experimental results demonstrate the superiority of our method compared to the state-of-the-art methods in reducing the cost of synthesized Quantum algorithms with rotation operations.

关键词： Fault tolerance Quantum computing Quantum algorithm Costs Fourier transforms Fault tolerant systems Logic gates Minimization Quantum circuit Adders

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Robust Adaptive Control with Reduced Conservatism for a Convertible UAV

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IFAC-PapersOnLine 2023年第2期56卷 4520-4526页

作者： Jonatan M. Campos Daniel N. Cardoso Guilherme V. Raffo Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais - Av. Antônio Carlos 6627 31270-901 Belo Horizonte MG Brazil Department of Electronic Engineering - Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

This work proposes a robust adaptive mixing controller to achieve trajectory tracking throughout the full-flight envelope, with ensured stability, of a quad-tiltorotor convertible plane (CP) vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV), here called QuadCP-VTOL UAV. Accordingly, a nonlinear multi-body dynamic model is obtained using the Euler-Lagrange formalism, from which a linear parameter-varying (LPV) model is derived to describe the dynamics of the QuadCP-VTOL UAV within its flight envelope. The set containing the UAV's flight envelope is partitioned into subsets and, for each subset, a less conservative formulation of the linear robust mixed H 2 /H ∞ candidate controller is proposed. An adaptive mixing scheme is employed to perform a convex combination of the candidate controllers within the subsets intersection. Results of Hardware-In-the-Loop (HIL) experiments are presented to corroborate the efficacy of the proposed control strategy.

关键词： Robust adaptive control Convertible UAV Linear mixed H2/H∞ control

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VR-Based Implicit Authentication Using Electromyogram and Inertial Measurement Unit

VR-Based Implicit Authentication Using Electromyogram and In...

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International Conference on Cyberworlds

作者： Da-Young Yu Hyung-Tak Lee Youngsam Kim Jong-Hyuk Roh Soohyung Kim Jeonghun Ku Han-Jeong Hwang Dept. Electronics and Information Engineering Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Department of Biomedical Engineering College of Medicine Keimyung University Daegu Republic of Korea

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Biometrics based on electromyogram (EMG) and inertial measurement unit (IMU) signals has received significant attention due to its resistance to forgery and tampering, as well as its relatively higher convenience and security compared to other biometric modalities. However, most EMG and IMU signal-based authentication methods require explicit authentication using unnatural gestures, which is not userfriendly. To address this, we collected EMG and IMU data during virtual reality (VR) rhythm game play, which does not require explicit gestures, and performed implicit authentication. Using a linear discriminant analysis (LDA) classifier, we achieved classification accuracies of 91.71% for human identification and 87.91% for human authentication. The result demonstrated the feasibility of a seamless and implicit authentication in VR environments.

关键词： Biometrics Solid modeling Measurement units Accuracy Authentication Inertial navigation Games Virtual reality Rhythm Electromyography

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High magnetic field properties in Ru2−xFexCrSi with antiferromagnetic and spin-glass states

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Physical Review B 2021年第9期103卷 094428-094428页

作者： Masahiko Hiroi Sora Nishiinoue Iduru Shigeta Masakazu Ito Keiichi Koyama Akihiro Kondo Koichi Kindo Isao Watanabe Muneaki Fujii Shojiro Kimura Hirotaka Manaka Norio Terada Department of Electrical and Electronics Engineering Graduate School of Science and Engineering Kagoshima University Kagoshima 890-0065 Japan

We report experimental studies on Ru2−xFexCrSi focusing on properties related to antiferromagnetism in Ru2CrSi and spin glass (SG) in Ru1.9Fe0.1CrSi. By measuring the temperature dependence of magnetization M(T), specific heat C(T), electrical resistivity ρ(T), and muon spin relaxation (μSR), we observed that Ru2CrSi exhibited an antiferromagnetic (AF) transition at a temperature TN of ∼13 K, and Ru1.9Fe0.1CrSi showed SG properties that could be interpreted as successive SG transitions, where the spin-freezing occurred at temperature Tg, and below that strong irreversibility in M(T) and a gradual peak of M(T) at T*(>Tg) appeared. When the data for 0≤x≤0.1 were compared, by substituting Fe the AF order was rapidly destroyed and it appeared to change to two anomalies at T* and Tg. With increasing x, there was a slight change in Tg from TN for x=0; however, T* increased, suggesting that the AF and the SG states are closely related. Furthermore, the results of specific heat, resistivity, and magnetization in high fields were presented and compared. For Ru2CrSi in specific heat under high magnetic fields up to 14 T, the peak shape around TN and the TN value were constant. The resistivity and magnetization in pulsed fields suggested that TN of Ru2CrSi was constant up to over 50 T. These results demonstrated the unusual robustness of the AF transition to magnetic fields. In Ru1.9Fe0.1CrSi, unusual hysteresis in magnetoresistance was observed in static and pulsed magnetic fields, although their appearances differed. These hystereses were considered a manifestation of the curious properties of the SG state with strong irreversibility.

关键词： Antiferromagnetism Magnetoresistance Half-metals Heusler alloy Spin glasses Magnetization measurements Resistivity measurements Specific heat measurements

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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Design Considerations for a Reliable Power electronics-based Arc-Flash Suppressor

Design Considerations for a Reliable Power Electronics-based...

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2021 IEEE Industry Applications Society Annual Meeting, IAS 2021

作者： Amaral, Fernando Silva, Sidelmo Conceicao, Claudio Guimaraes, Gustavo Cardoso, Braz UFMG Graduate Program In Electrical Engineering Belo Horizonte Brazil Universidade Federal De Minas Gerais Electrical Engineering Department Belo Horizonte Brazil Fundação De Desenvolvimento Da Pesquisa Belo Horizonte Brazil

ISBN: (纸本)9781728164014

This paper presents a comprehensive analysis of single- and three-phase arcing faults in industrial and commercial power systems and provides a detailed procedure for the component design of a fast power electronics-based suppressor. Discrete-time simulations are performed with ATP Draw to validate the theoretical developments including failure analysis and online diagnostic techniques, which are important since the proposed solution is related to the preservation of equipment and in particular their operators. © 2021 IEEE.

关键词： Accident prevention

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Personal Mobility Safe Driving System with Knowledge Distillation

Personal Mobility Safe Driving System with Knowledge Distill...

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International Conference on Ubiquitous Robots (UR)

作者： Damin Yeom Heejun Yoon Soyeon Lee Kahyun Lee Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Graduate Program in Smart Factory Ewha Womans University Seoul South Korea

The global personal mobility market has been rapidly expanding due to its convenience. However, the increasing number of accidents involving personal mobility devices has become a growing concern, including falls, collisions with objects, and riders being struck by moving vehicles or objects. In this paper, we propose a deep learning-based safe driving system that considers both user and road images to address this issue. Our system employs CNN-based models to detect whether the user is 1) wearing a helmet and 2) looking ahead in user-side images. At the same time, the roadside image recognizes whether the user is 3) driving on the sidewalk and 4) near the intersection. These tasks are simultaneously performed in parallel to identify the overall situation, which is done by determining the final speed as the minimum speed of speed values extracted from all tasks. Additionally, we employ knowledge distillation techniques to compress the models and enable real-time inference on edge devices, resulting in a fast and accurate system that is well-suited to the characteristics of personal mobility.

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Deep-Hand: A Deep Inference Vision Approach of Recognizing a Hand Sign Language using American Alphabet 2

Deep-Hand: A Deep Inference Vision Approach of Recognizing a...

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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Melegrito, Mark P. Franco Cunanan, Christopher Uy Ii, Edgar E. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines Technological University of the Philippines Department of Electronics and Communications Engineering Manila Philippines Polytechnic University of the Philippines Department of Computer Engineering Philippines Bohol Island State University College of Engineering and Architecture Tagbilaran Philippines

ISBN: (纸本)9781665429214

Sign-Language is to help people with hearing or speaking disabilities who are not able to communicate well with other people. Communicating with deaf people is a challenge for some speakers and people who do not know sign language. What the study proposed is to help people with such disabilities using the American Sign-Language with the corresponding hand gesture. Deaf individuals will be able to communicate or interact with other people conveniently. The study proposed hand gesture or hand sign language detection trained by using the YOLOv3 algorithm that aims to detect hand gestures or hand sign language that can recognize its equivalent letter alphabet. The study tools such as LabelImg for annotating the data set, categorizing each image of hand gestures based on their equivalent letter alphabet. In this study, Model 18 with 95.1804% training accuracy, 90.8242% validation accuracy, and mAP of 0.8275 is used for the final testing. As video with different hand gestures is presented, the results of every hand gesture detected range over 90%. © 2021 IEEE.

关键词： Object detection

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Stacking based ensemble learning framework for identification of nitrotyrosine sites

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Computers in Biology and Medicine 2024年 183卷 109200-109200页

作者： Parvez, Aiman Ali, Syed Danish Tayara, Hilal Chong, Kil To Graduate School of Integrated Energy-AI Jeonbuk National University Jeonju54896 Korea Republic of Department of Electrical Engineering The University of Azad Jammu and Kashmir Muzaffarabad13100 Pakistan Department of Electronics and Information Engineering Jeonbuk National University Jeonju54896 Korea Republic of Department of International Science and Engineering Jeonbuk National University Jeonju54896 Korea Republic of Advanced Electronics and Information Research Center Jeonbuk National University Jeonju54896 Korea Republic of

Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological functions and diseases. Therefore, accurate identification of nitrotyrosine sites plays a significant role in the elucidating progress of associated biological signs. In this regard, we reported an accurate computational tool known as iNTyro-Stack for the identification of protein nitrotyrosine sites. iNTyro-Stack is a machine-learning model based on a stacking algorithm. The base classifiers in stacking are selected based on the highest performance. The feature map employed is a linear combination of the amino composition encoding schemes, including the composition of k-spaced amino acid pairs and tri-peptide composition. The recursive feature elimination technique is used for significant feature selection. The performance of the proposed method is evaluated using k-fold cross-validation and independent testing approaches. iNTyro-Stack achieved an accuracy of 86.3% and a Matthews correlation coefficient (MCC) of 72.6% in cross-validation. Its generalization capability was further validated on an imbalanced independent test set, where it attained an accuracy of 69.32%. iNTyro-Stack outperforms existing state-of-the-art methods across both evaluation techniques. The github repository is create to reproduce the method and results of iNTyro-Stack, accessible on: https://***/waleed551/iNTyro-Stack/ © 2024 Elsevier Ltd

关键词： Adversarial machine learning

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