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arXiv 2024年

作者： Peng, Ruimin Du, Zhenbang Zhao, Changming Luo, Jingwei Liu, Wenzhong Chen, Xinxing Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China AI Platform Software Engineering Research Center Dongfeng Corporation Research & Development Institute Wuhan430072 China China Electronic System Technology Co. Ltd. Beijing100089 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification. © 2024, CC BY.

关键词： Electroencephalography

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Automated Bright Pixel Detection System on LCD Displays

Automated Bright Pixel Detection System on LCD Displays

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Mateus O. Silva David A. O. Ferreira Kethilen Y. Ouchi Gustavo M. Torres Edma V.C. Urtiga Mattos Antônio M. C. Pereira Luciana R. Costa Victor L. G. Cavalcante Caio F.S. Cruz Agemilson P. Silva Ruan J.S. Belem Anderson S. Jesus Wilson C.C. Junior Ricardo G. Paula Thiago B. Bezerra Waldir S. S. Júnior Celso B. Carvalho Federal University of Amazonas and Center for R&D in Elec. and Inf. Tech. (UFAM/CETELI) AM Brazil ENVISION/TPV Group Technology Limited AM Brazil ICTS Institute Center of Development Research in Software Technology AM Brazil

Bright pixel defect occurs in LCDs when a transistor of a subpixel remains bright. Bright pixels reduce the quality of manufactured devices, negatively impacting companies looking to meet an increasingly demanding market. In this wokr, conducted by three partners, called UFAM/CETELI, Envision (TPV Group) and icts, we propose an automated bright pixel defect detection system on LCD displays, which works in conjunction with two proposed methods, the quartile-based method and the standard-based method to identify bright pixels in LCDs. The proposals obtained as best result 100% of accuracy and between 0.397s and 0.193s of average processing time per image. The results of high accuracy and reduced processing time demonstrate the contribution of our proposal, being possible to use it in the detection of bright pixels in real production lines that use LCDs on TVs or monitors.

关键词： TV Conferences Distributed databases Production Companies Liquid crystal displays Proposals

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Synchronous hybrid brain–computer interfaces for recognizing emergency braking intention

Brain-X

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Brain-X 2024年第1期2卷

作者： Ju, Jiawei Feleke, Aberham Genetu Li, Hongqi Li, Haiyang Institute of Neuroscience Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai Shanghai China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Software Northwestern Polytechnical University Shaanxi Xi'an China The Research & Development Institute of Northwestern Polytechnical University in Shenzhen Guangdong Shenzhen China The Yangtze River Delta Research Institute of Northwestern Polytechnical University in Taicang Jiangsu Suzhou China Chemical Engineering Department Pohang University of Science and Technology Gyeongbuk Pohang South Korea

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver's intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one feature-level (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems. © 2024 The Authors. Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical technology.

关键词： electroencephalography electromyography emergency braking hybrid recognition

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Taiwanese Stock Market Forecasting with a Shallow Long Short-Term Memory Architecture 9th

Taiwanese Stock Market Forecasting with a Shallow Long Short...

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9th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2020 and 6th EAI International Conference on Nature of Computation and Communication, ICTCC 2020

作者： Bui, Phuong Ha Dang Tran, Toan Bao Nguyen, Hai Thanh College of Information and Communication Technology Can Tho University Can Tho900100 Viet Nam Center of Software Engineering Duy Tan University Da Nang550000 Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9783030671006

The trading of stock in companies holds an important part in numerous economies. Stock Forecast which is popularly published in the public domain in the forms of newsletters, investment promotion organizations, public/private forums, and scientific forecast services is very necessary to contribute successes in financial for individuals or organizations. Leveraging advancements in machine learning, we propose an approach based on Long Short-Term Memory model and compare the performance to the classic machine learning such as Random Forest model and Support Vector Regression model when we do forecast tasks on Taiwanese stock market. The proposed method with deep learning algorithm shows better performance comparing to the classic machine learning in the tasks of forecasting the stock market in Taiwan. © 2021, ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Long short-term memory

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Feature Selection Based on a Shallow Convolutional Neural Network and Saliency Maps on Metagenomic Data 1

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iCatse International Conference on Information Science and Applications, ICISA 2020

作者： Tran, Toan Bao Phan, Nhi Yen Kim Nguyen, Hai Thanh College of Information and Communication Technology Can Tho University Can Tho900100 Viet Nam Center of Software Engineering Duy Tan University Da Nang550000 Viet Nam Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

ISBN: (数字)9789813363854

ISBN: (纸本)9789813363847

In recent years, personalized medicine has been discovered by scientists to improve existing curative methods. These studies are mainly performed on metagenomic datasets which is the large dataset related to many human diseases, especially genetic data. The development of machine learning models and related algorithms has enabled us to speed up computation and improve disease diagnosis accuracy. However, due to the large dataset and the rather complicated processing of the data, we encountered certain difficulties. Therefore, we propose an approach to the task of selecting features based on the explanatory model. This approach is made up of proposing a small set of features from the original, implemented with Explanations with Saliency Maps. The results exhibit better performances comparing to random feature selection. Explanations generated by Saliency Maps have provided a promising method in selecting features and are expected to apply in practical cases. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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TAM: A Computing in Memory based on Tandem Array within STT-MRAM for Energy-Efficient Analog MAC Operation

TAM: A Computing in Memory based on Tandem Array within STT-...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jinkai Wang Zhengkun Gu Hongyu Wang Zuolei Hao Bojun Zhang Weisheng Zhao Yue Zhang MIIT Key Laboratory of Spintronics Fert Beijing Institute School of Integrated Circuit Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Nanoelectronics Science and Technology Center Hefei Innovation Research Institute Beihang University Hefei China

Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.

关键词：

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An Adaptive Digital Twin-Driven Model Based on Consumer Internet of Things To Ensure the Safe Operation of Aero-Engine

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IEEE Transactions on Consumer Electronics 2024年

作者： Gou, Ruijia Wang, Wei Liu, Zongyao Gadekallu, Thippa Reddy Amoon, Mohammed Lin, Chuan Xiamen University School of Informatics Xiamen361005 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Northwestern Polytechnical University School of Power and Energy Xian710072 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India King Saud University Department of Computer Science Community College Riyadh Saudi Arabia Northeastern University School of Software Shenyang110819 China

The aero-engine, often hailed as the manufacturing's 'crown jewel' presents considerable complexity in its design, manufacturing, and maintenance. To enhance safety and efficacy of the aero-engine especially through comprehensive monitoring and systematic testing, in this paper, we propose an Adaptive Cycle Engine (ACE) model, i.e., a digital twin-driven aero-engine model based on the Consumer Internet of Things (CIoT), to safely model the aero-engine. The proposed ACE model can simulate all categories of the aero-engine's working modes by setting the regulating components that control the opening and closing of bypass ducts, including mode selection valve and variable area bypass injector. Thus, each category of the working modes can be adaptively switched to enable aero-engine flexible adjustment and control. Furthermore, in the proposed ACE model, the common working conditions and the models of each mode are determined by the corresponding analysis of iterative variables and constraint equations for achieving the comprehensive performance analysis of steady state and transient conditions. Evaluation results demonstrate that the proposed ACE model can accurately simulate the operating state of the actual aero-engine, especially when it is compared with some of the famous commercial models. This work aim at promoting the development of consumer aero-engine. © 1975-2011 IEEE.

关键词： adaptive cycle engine (ACE) Consumer Internet of Things Digital twin variable area bypass injector

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H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic

arXiv

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arXiv 2025年

作者： Luo, Xueting Deng, Hao Yang, Jihong Shen, Yao Guo, Huanhuan Sun, Zhiyuan Liu, Mingqing Wei, Jiming Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China China Land Surveying and Planning Institute Beijing100035 China College of Architecture and Urban Planning Tongji University Shanghai200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing401121 China Li-Fi Research and Development Centre University of Cambridge CambridgeCB3 0FA United Kingdom Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou510290 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

The necessity of achieving an effective balance between minimizing the losses associated with restricting human mobility and ensuring hospital capacity has gained significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics;however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can minimize hospital capacity strain while minimizing human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging

3D Anatomical Structure-Guided Deep Learning for Accurate Di...

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IEEE International Symposium on Biomedical Imaging

作者： Xinrui Ma Jian Cheng Wenxin Fan Ruoyou Wu Yongquan Ye Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou Zhejiang China Pengcheng Laboratory Shenzhen Guangdong China United Imaging Healthcare Houston TX USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.

关键词： Deep learning PSNR Accuracy Three-dimensional displays Magnetic resonance imaging Estimation Reconstruction algorithms Diffusion models Feature extraction Microstructure

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Simultaneous Q-Space Sampling Optimization and Reconstruction for Fast and High-Fidelity Diffusion Magnetic Resonance Imaging

Simultaneous Q-Space Sampling Optimization and Reconstructio...

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IEEE International Symposium on Biomedical Imaging

作者： Jing Yang Jian Cheng Cheng Li Wenxin Fan Juan Zou Ruoyou Wu Qiegen Liu Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China Department of Electronic Information Engineering Nanchang University Nanchang China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.

关键词： Water In vivo Limiting Magnetic resonance imaging Harmonic analysis Robustness Image reconstruction

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