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30th IEEE International Conference on Electronics, Circuits and systems, ICECS 2023

作者： Khan, Muhammad Umair Mohammad, Baker Khalifa University System on Chip Center Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350326499

This work proposed a vacancy-modulated analog resistive random-access memory (ReRAM) for neuromorphic computing, utilizing a structure composed of Ag/Zn@ZnO/ZnO/FTO. The resistive switching behavior can be modulated by the movement of oxygen vacancies under positive or negative voltages. This characteristic leads to the desired resistive switching behavior for neuromorphic computing. The Ag/Zn@ZnO/ZnO/FTO based analog ReRAM exhibits several desirable features, including a low operation current, forming-free operation, and self-compliance. The conductance change rate of the device can be adjusted efficiently by modifying the amplitudes and pulse intervals of spike pulses. The device can be used for short-time memory (STM) and long-term memory (LTM) memory operations with good retention time. These characteristics align with the requirements for next-generation ReRAM. The introduction of the Ag/Zn@ZnO/ZnO/FTO-based device structure paves the way for the future development of neuromorphic computing memory applications. © 2023 IEEE.

关键词： Zinc oxide

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Position Measurement Based Slave Torque Feedback Control for Teleoperation systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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CFD Simulation of Flight Vehicle Optical Dome and Flow Field Based on Solid-Fluid Coupling Approach 25

CFD Simulation of Flight Vehicle Optical Dome and Flow Field...

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25th IEEE China Conference on system Simulation Technology and its Application, CCSSTA 2024

作者： Li, Jing Wang, Qilong Xu, Liang Wan, Ziming Zhao, Yan Zhao, Xuewen Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China Beijing Institude of Electronic System Engineering Beijing China

ISBN: (纸本)9798350366600

At high velocity flight, the infrared imaging system encounters significant aero-optical disturbances due to the uneven gas flow around the aircraft's forward section and the elevated temperatures of the optical dome. In the simulation study of aero-optical effects, hydrodynamic calculations of the gas flow field and optical dome are required. This paper examines the utilization of the solid-fluid coupling approach in simulating aero-optical phenomena from the perspective of fluid dynamics computations. This paper undertakes the geometric modelling of the airflow environment and optical dome of a blunt-nose front-window flight vehicle. Utilizing ICEM, the mesh generation for both the gaseous flow domain and the solid optical dome is executed. Subsequently, Fluent software facilitates the performance of hydrodynamic simulations, concurrently extracting vital parameters from both the flow field and the optical dome. The method in this paper can be used in the calculation and analysis of aero-optical effects of high-speed flight vehicles. © 2024 IEEE.

关键词： Flow of gases

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Trust Evaluation-based Machine Learning for WSNs 10

Trust Evaluation-based Machine Learning for WSNs

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumar, Aditya Singh, Karan Vats, Satvik School of Computer and System Sciences Jawaharlal Nehru University New Delhi India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have many different applications in industries such as intrusion detection, healthcare, and environmental monitoring. To ensure their success, it is essential to establish trust among the sensor nodes to enhance trustworthiness, collaboration, and security. However, current trust management methods for large-scale WSNs have several drawbacks, including low dependability, high communication, and memory overhead, which make them ineffective. To address these issues, a new approach has been proposed, which uses trust evaluation-based machine learning for WSNs. This method involves determining trust attributes using a model and then creating a new algorithm based on machine learning to categorize and combine these trust features into a final trust value. This trust value can be used in decision-making to improve the effectiveness of WSNs. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Principal component analysis

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Distributed Hierarchical AI Architecture for Meta-Described Local Dynamic Maps in Autonomous Driving 16

Distributed Hierarchical AI Architecture for Meta-Described ...

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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Miyata, Hiroshi Arimoto, Kazutami Saitoh, Masatoshi Hayami, Atsushi Mizuno, Hisayoshi Shimazu, Yukihiko Yokogawa, Tomoyuki Technoaccel Networks Corp. Tokyo Office Tokyo Japan Okayama Prefectural University Computer Science and System Engineering Sousya Okayama Japan Toyota Motor Corporation Information and Communication Planning Division Tokyo Japan Okayama Prefectural University Computer Science and System Engneering Sousya Okayama Japan

ISBN: (纸本)9798350377903

In autonomous driving, sharing information among vehicles to enhance safety is an important issue. However, it is not yet clear how each terminal car processes information and how to share it with other cars. Therefore, this paper proposes a distributed hierarchical AI architecture in which each edge distributed terminal of a car or intermediate cloud is equipped with a processing AI that generates metadata and builds them hierarchically. The hierarchical AI consists of multiple AI information processing layers and generates metadata links by concatenating the output of each layer. The hierarchical and spatially distributed nature of information processing allows for a compact digital twin that enables efficient spatiotemporal processing by receiving only the data necessary for that terminal and facilitating the processing necessary for that terminal. As a concrete example, we created software for vehicle front camera image processing for local dynamic maps for autonomous driving, and we evaluated the intensity and processing load of the proposed architecture. In the future, we will continue to study the software and hardware necessary to increase the hierarchy and speed of AI processing. © 2024 IEEE.

关键词： Spatio-temporal data

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit Dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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A Multi-label Classification Method for Positive and Unlabeled Dataset. 23

A Multi-label Classification Method for Positive and Unlabel...

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Proceedings of the 2023 4th Asia Service sciences and Software engineering Conference

作者： Hikaru Takahashi Zhao Qiangfu Computer Science and Engineering Computer Information System System Intelligence Lab. University of Aizu Japan Computer Science and Engineering System Intelligence Lab. University of Aizu Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning.

关键词： Machine Learning Multi-class Classification Positive and Unlabeled Learning

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RPRB-Net: A Sign Language Recognition Based on Multi-dimension Attentions

RPRB-Net: A Sign Language Recognition Based on Multi-dimensi...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Yang, Tian Shen, Cong Wang, Xinyue Ma, Xiaoyu Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350306545

Sign Language Recognition (SLR) is the process of translating sign language into text or speech, which can also be viewed as a communication and expression tool between deaf individuals and hearing people. SLR using deep neural network has undergone significant development over the last decade. In this study, we design a feature-enhanced SLR technique based on serval spaces. To aid the network learning features from channel and spatial dimensions, we inject various attention mechanisms into the feature extractor backbone. As a result, we named our approach ResNet + Pyramid-Split-Attention + Recurrent-Criss-Cross-Attention + BiLSTM Network (RPRB-Net). Besides, we employ the BiLSTM architecture to encode high-level representations in temporal sequence. The network is ultimately trained using an end-to-end CTC alignment strategy. Extensive trials show that RPRB-Net achieves WERs of 3.5% and 23.6% on the USTC and RWTH-2014 datasets, respectively. Besides, it implies that RPRB-Net significantly surpasses the cutting-edge SLR approaches. Additionally, numerous ablation experiments have confirmed the efficiency of configuration. © 2023 IEEE.

关键词： Audition

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Heterogeneous system Data Storage and Retrieval Scheme Based on Blockchain 7th

Heterogeneous System Data Storage and Retrieval Scheme Based...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Zhang, Ni Ma, BaoQuan Wang, Peng Lei, XuHua Cheng, YeJian Li, JiaXin Huai, XiaoYong Shen, ZhiWei Song, NingNing Wang, Long National Computer System Engineering Research Institute of China Beijing102200 China School of Computer Science and Technology Xidian University Xi’an710071 China

ISBN: (纸本)9783031281235

In the field of information interaction, when a project involves a large amount of heterogeneous information, it is difficult to transmit and update the required information timely, accurately, reliably and securely in such a complex environment to maintain synchronization. At present, blockchain itself has problems such as high storage pressure of nodes, low access efficiency, and simple query. Therefore, this paper takes this as a starting point and proposes a data mapping method of physical resources based on node attributes and heterogeneous nodes, which provides a general method for the data mapping from actual physical resources to information domain. This method can define the attributes of heterogeneous physical information nodes and support unified expression of various physical information resources on the same platform in the real physical world, then connect heterogeneous physical information nodes and improve the sharing ability of data resources between nodes. When accessing data, the corresponding content can be found in the off-chain database by obtaining the index information of the off-chain location stored on the chain. This method takes advantage of the large space and high access efficiency of the off-chain storage system to share the pressure of on-chain data storage. This paper expounds the design idea of the system, introduces the design objective and method in detail, gives the flow chart of the system operation, and carries out a simple software test and verification. Finally, this paper summarizes the work and prospects the development direction of future work, hoping to provide inspiration for solving such problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Receding-Horizon Control of Constrained Switched systems with Neural Networks as Parametric Function Approximators

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SN computer science 2023年第1期4卷 62页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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