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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Aibin Yan Zhuoyuan Lin Guangzhu Liu Qingyang Zhang Zhengfeng Huang Jie Cui Xiaoqing Wen Patrick Girard School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile and single-event-upset (SEU)-recoverable latch based on FeFET and CMOS for energyharvesting devices. The latch uses n-type FeFET devices to provide nonvolatility without any additional control signals. Moreover, since the soft error problem has become increasingly severe, radiation hardening by design gains a great attention as a promising approach to mitigate the reliability issue. The latch uses feedback interlocked loops with n-type FeFETs and C-elements, enabling it to provide nonvolatility and SEU-recovery simultaneously. Simulation results with Candence Virtuoso verifies that the proposed latch design has correct functioning with excellent performance compared to the state-of-the-art designs.

关键词： Performance evaluation Latches Nonvolatile memory Radiation hardening (electronics) Circuits and systems Simulation Reliability engineering

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Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring systems

Deep Reinforcement Learning Empowered Activity-Aware Dynamic...

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IEEE International Conference on Communications (ICC)

作者： Ziqiaing Ye Yulan Gao Yue Xiao Zehui Xiong Dusit Niyato National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Nanyang Technological University Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose a Dynamic Activity-Aware Health Monitoring strategy (DActAHM), as a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model, for striking a balance between optimal monitoring performance and cost efficiency while ensuring precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

关键词： Measurement Medical devices Costs Medical services Deep reinforcement learning Real-time systems Bioinformatics

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3D Class A Bézier curves with monotone curvature

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CAD computer Aided Design 2023年 159卷

作者： Wang, Aizeng He, Chuan Zheng, Jianmin Zhao, Gang School of Mechanical Engineering & Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology & Systems Beihang University Beijing100191 China School of Computer Science and Engineering Nanyang Technological University Singapore

This paper introduces a special class of 3D Bézier curves that are defined by their degree, a starting point, the first leg of their control polygons, and a 3D affine transformation composing of a uniform scaling and a rotation. We present new formulas for the curvature of such Bézier curves and based on the new formulas we derive sufficient conditions for the curves to have monotonic curvature. The conditions are expressed by a simple constraint on the rotation angle and the scaling factor. This facilitates constructing 3D Class A Bézier curves that are a generalization of planar typical curves proposed by Mineur et al. (1998), which are often used in automotive and other design applications. Some examples are provided to demonstrate the effectiveness of our construction. © 2023 Elsevier Ltd

关键词： Geometry

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RTOD-YOLO: Traffic Object Detection in UAV Images Based on Visual Attention and Re-parameterization

RTOD-YOLO: Traffic Object Detection in UAV Images Based on V...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xuesen Ma Weixin Wei Jindian Dong Biao Zheng Ji Ma School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

Unmanned Aerial Vehicles (UAVs) are widely deployed in transportation. Detection of traffic objects like vehicles in UAV images is helpful for traffic monitoring, urban transport planning, and estimation of traffic flow. However, detecting objects in UAV images has some inevitable problems, such as large changes in object scale, occlusion between objects in dense detection scenes, and interference caused by complex backgrounds. To reduce the influence of the above factors and achieve real-time detection, we propose RTOD-YOLO based on YOLOv5s in this study. Firstly, a small object detection layer is added to improve the model's performance to detect small objects. Secondly, the Global Context Attention Module (GCAM) is introduced to enable RTOD-YOLO to concentrate more on the target itself and reduce the interference caused by complex backgrounds. Thirdly, the YOLO detection head is replaced by the Shift Double Prediction Head (SDPH) to improve the bounding box regression and classification ability of the proposed model. Finally, RepBlock and CSPRepBottleneck are introduced to re-parameterize the model structure, for improving feature extraction capabilities and adequately mining semantic information in images. The experimental result shows that the mAP@0.5 of RTOD-YOLO reaches 44.6% on the VisDrone dataset, which is 10.7% higher than that of the baseline YOLOv5s, while still accommodating real-time detection.

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An Adaptive Direct-Discretized RNN With Fuzzy Factor for Handling Future Distinct-Layer Inequality and Equation System Applied to Robot Manipulator Control

An Adaptive Direct-Discretized RNN With Fuzzy Factor for Han...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Binbin Qiu Kehan Li Zihang Li Jinjin Guo School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Shenzhen China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This paper proposes an adaptive direct-discretized recurrent neural network (ADD-RNN) algorithm with fuzzy factor to address the problem of future distinct-layer inequality and equation system (FDLIES). For comparison purposes, a traditional indirect-discretized RNN (TID-RNN) algorithm is also developed to solve the same problem. The superior performance of the proposed ADD-RNN algorithm is validated through numerical experiment. The applicability and meliority of the proposed ADD-RNN algorithm are further demonstrated by applying it to tackle a motion planning and control problem of a robot manipulator with an additional constraint, which is formulated as a specific FDLIES problem.

关键词： Fuzzy control Recurrent neural networks Adaptive systems Accuracy Manipulators Planning Convergence

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ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Accelerator for Online Deep Learning

ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Acce...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Heng Zhou Bing Wu Huan Cheng Wei Zhao Xueliang Wei Jinpeng Liu Dan Feng Wei Tong Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan China

ReRAM-based Processing-In-Memory (PIM) architectures have demonstrated high energy efficiency and performance in deep neural network (DNN) acceleration. Most of the existing PIM accelerators for DNN focus on offline batch learning (OBL) which requires the whole dataset to be available before training. However, in the real world, data instances arrive in sequential settings, and even the data pattern may change, which calls concept drift. OBL requires expensive retraining to solve concept drift, whereas online deep learning (ODL) is evidenced to be a better solution to keep the model evolving over streaming data. Unfortunately, when ODL optimizes models over a large-scale data stream in the PIM system, unbalanced writes are more severe than OBL, due to the heavier weight updates, resulting in the amplification of unbalanced writes and lifetime deterioration. In this work, we propose ODLPIM, an online deep learning PIM accelerator that extends the system lifetime through algorithm-hardware co-optimization. ODLPIM adopts a novel write-optimized parameter update (WARP) scheme that reduces the non-critical weight updates in hidden layers. Besides, a table-based inter-crossbar wear-leveling (TIWL) scheme is proposed and applied to the hardware controller to achieve wear-leveling between crossbars for lifetime improvement. Experiments show that WARP reduces weight updates on average to 15.25% and up to 24% compared to that without WARP, and eventually prolongs system lifetime on average to 9.65% and up to 26.81%, with a negligible rise in cumulative error rate (up to 0.31%). By combining WARP with TIWL, the lifetime of ODLPIM is improved by an average of $\mathbf{12}.\mathbf{59}\times$ and up to $\mathbf{17}.\mathbf{73}\times$ .

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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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Design Automation Conference

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and µ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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A Fuzzy-Power Direct-Discretization RNN Algorithm for Solving Discrete Multilayer Dynamic systems With Robotic Applications

A Fuzzy-Power Direct-Discretization RNN Algorithm for Solvin...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Binbin Qiu Zihang Li Kehan Li Jinjin Guo School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Shenzhen China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This work proposes a new recurrent neural network (RNN) algorithm for solving discrete multilayer dynamic systems (DMDSs). First, by utilizing the direct-discretization technique, a direct-discretization RNN (DDRNN) algorithm for solving the DMDSs is introduced. On this basis, a fuzzy-power direct-discretization RNN (FDDRNN) algorithm is proposed, which integrates a fuzzy control system (FCS) within the DDRNN framework. This integration dynamically modulates the time step τ by exerting a fuzzy-power factor r, which is determined based on the logarithm of the residual, as τ r . Finally, comparative evaluations among the gradient-based RNN (GBRNN), the DDRNN, and the FDDRNN algorithms are conducted through numerical experiments and robotic application experiments involving both planar and spatial robots. The results indicate that the proposed FDDRNN algorithm outperforms the GBRNN and DDRNN algorithms in terms of accuracy, convergence speed, and stability.

关键词： Recurrent neural networks Accuracy Heuristic algorithms Dynamics Nonhomogeneous media Stability analysis Dynamical systems Robots Numerical stability Convergence

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An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

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Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection

arXiv

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arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

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