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检索条件"任意字段=23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021"

共 285 条记录，以下是101-110 订阅

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TLM-NoC: Two Level Mesh Network-on-Chip for Performance Improvement 23

TLM-NoC: Two Level Mesh Network-on-Chip for Performance Impr...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gagan, N. Bhowmik, Biswajit National Institute of Technology Karnataka Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9781665494571

As the number of cores integrated into a system-on-chip (SoC) increases, the role played by this on-chip communication system becomes more and more critical due to communication bottleneck and scalability. The network-on-chip (NoC) design paradigm is today recognized as the most viable communication infrastructure over SoCs. However, the former architecture imposes latency concerns while the network size increases resulting in significant performance degradation. This paper finds an alternative solution for improving mesh NoCs, especially when long-distance communication takes place. The proposed solution suggests a variant of the 2D mesh topology named 'two-level mesh - network-on-chip (TLM-NoC)' in which a baseline mesh includes sparse mesh interconnection at the upper layer. The upper layer primarily establishes long-distance communications between nodes which in turn reduces latency and improves other metrics. Experimental results show adequate performance improvements by the TLM-NoC over traditional meshes. Evaluation of TLM-NoC shows latency improvement by 17.97%, resulting in 33.33% packet flit loss reduction and 26.80 % increased throughput. Further, the proposed architecture consumes 23.69% less energy for delivering traffic. © 2021 IEEE.

关键词： Performance evaluation Smart cities Scalability Network-on-chip computer architecture Traffic control Throughput

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FIT: Fairness-Aware Intelligent Traffic Signal control with Deep Reinforcement Learning 23

FIT: Fairness-Aware Intelligent Traffic Signal Control with ...

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作者： Chen, Jian Zhang, Zhengwei Feng, Jianhang Zhu, Kun The Key Lab. of Information System Requirement of China Electronics Technology Group Corporation Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665494571

The fast development of learning techniques make it possible to intelligently adjust the traffic signal, also the promotion of the future network(e.g. 6G) provides the possibility to adjust the signal in real time. Several studies have proposed learning-based methods for signal control achieving superior performance compared with traditional methods. However, these methods mainly focus on improving the total efficiency for passing the intersection. To this end, a higher priority is usually given to the lane with larger traffic, which could cause the lane with few vehicles to be starved for a long time. Although traditional methods have better performance in preventing starvation, the efficiency still needs to be greatly improved. In this paper, we consider both the fairness and efficiency in intelligent traffic signal control and try to relieve such starvation while improving the total efficiency. Specifically, a deep reinforcement learning based approach is proposed to dynamically control the traffic signal according to real-time traffic information. Inspired by the proportional fair scheduling (PFS) in wireless networks, a new fairness-aware traffic signal control model is designed to maintain a good trade-off between efficiency and fairness. Extensive experiments are conducted to demonstrate that our method achieves better fairness while also provides a good efficiency guarantee. © 2021 IEEE.

关键词： 6G mobile communication Learning systems Smart cities Wireless networks Reinforcement learning Telecommunication traffic Real-time systems

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Global Cyclic Queuing and Forwarding Mechanism for Large-Scale Deterministic Networks 23

Global Cyclic Queuing and Forwarding Mechanism for Large-Sca...

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作者： Mo, Yijun Yang, Zihan Liu, Huiyu He, Tianliu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665494571

Delay and jitter sensitive industrial control and remote driving application-driven network by best-effort to deterministic service. To meet the deterministic application of delay and jitter demands, industry and academia have conducted detailed research on stream filtering shaping and queue forwarding control. However, the study seldom considers the effects of long-distance topology change, transmission delay fluctuations and short-time traffic overloads, challenging to meet deterministic requirements under large-scale backbone networks. For this challenge, this paper based on Cyclic Queuing and Forwarding, considers source-destination path properties and deterministic traffic intensity. We propose Global Cyclic Queuing and Forwarding 3-Queue. In the deterministic network simulation system based on OMNeT, we constructed the actual network topology NSFNET, which has 14 nodes and 21 links. Experiments compare queue forwarding control mechanisms such as CQF and GCQF-3. The experimental results show that under the traffic matrix with 120% strength, the delay is reduced by 23.6%, the jitter is reduced by 53.6%, and the packet loss rate is reduced by 61%. It shows that GCQF-3 can timely adjust the gate-control and queueing priority according to the deterministic traffic state, achieves the best in delay, jitter and network utilization. © 2021 IEEE.

关键词： Industries Network topology Smart cities Industrial control Packet loss Jitter Logic gates

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False Data Injection Attack Detection Based on Wavelet Packet Decomposition and Random Forest in Smart Grid 23

False Data Injection Attack Detection Based on Wavelet Packe...

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作者： Chen, Zhenyu Yuan, Shuai Wu, Longfei Guan, Zhitao Du, Xiaojiang School of Control and Computer Engineering North China Electric Power University China Brock University Department of Finance Operations and Information Systems ON Canada Fayetteville State University Department of Mathematics and Computer Science Fayetteville United States Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9781665494571

As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk when the parameters are manipulated and cyber-attacks are generated against the state estimation, i.e., under false data injection attack (FDIA). Currently, a rich body of literature has studied on the FDIA defense methods, but most of them focus on the direct current (DC) scenario. This paper proposes a novel detection model that combines the wavelet packet decomposition (WPD) technique with the random forest (RF) algorithm. The WPD is able to capture the deviation of parameters from the normal conditions, whereas the RF is developed to classify these features and effectively identify the malicious data. The proposed model is also evaluated using real-world data on IEEE 118-bus power system. The results demonstrate excellent performance on precision rate and recall rate under varying scenarios. © 2021 IEEE.

关键词： Radio frequency Training data Interference Power system stability Feature extraction Data models Wavelet packets

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Prediction based Auto-Pilot Interface for Drone to Object Chasing using Historical TSPI Data 23

Prediction based Auto-Pilot Interface for Drone to Object Ch...

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23rd international conference on control, Automation and systems, ICCAS 2023

作者： Jang, Shinhyoung Park, Byeonghwi Jeong, Juheon Mahedy, Jack Gebreslassie, Nebey Lee, Minji Matson, Eric T. Chungnam National University Department of Computer Science and Engineering Daejeon34134 Korea Republic of Purdue University Department of Computer and Information Technology West LafayetteIN47906 United States Purdue University Department of Technology West LafayetteIN47906 United States

ISBN: (纸本)9788993215274

Replacing military aircraft with Unmanned Aerial Vehicles (UAV) offers advantages such as reducing costs and risks associated with conventional military aircraft. To defend against enemy UAV attacks, anti-drone systems are used. While most technologies focus on drone detection, this paper proposes a process of tracking targets using arbitrary UAV to enhance camera data quality or directly engage and neutralize targets. The system comprises drone detection, movement prediction, and chasing. Devices such as radar are used to detect drones, and they provide Time-Space-Position Information (TSPI) using suitable data formats. Using Machine Learning (ML) techniques make it feasible to predict an object's future location, represented with equations. Utilizing trained machine learning models, the Auto-pilot Application Programming Interface (API) of the drone is employed to track based on the predicted results. Through experiments, the feasibility of predicting an object's future location using various ML techniques has been confirmed. Furthermore, verification has been conducted to establish the attainability of implementing the Auto-pilot API through the utilization of DJI's API. © 2023 ICROS.

关键词： Drones

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A Noc Centric Low Overhead Multi-chip Interconnection Technology 23

A Noc Centric Low Overhead Multi-chip Interconnection Techno...

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作者： Hu, Xing Zhang, Yang Mao, Li Shen, Jie Xing, Zuocheng College of Computer National University of Defense Technolgy ChangSha China College of Microelectronics Xi'an University of Electronic Science and Technology Xi'an China

ISBN: (纸本)9781665494571

Improving the performance of processors is one of the main tasks of high-performance processor design. It is an effective way to improve the processor's performance through high-speed inter-chip interconnection, which utilizes multiple chips to build one unified computing system with higher performance. The existing multi-chip interconnection technologies, such as CCIX, CXL and NVLink, are faced with the problems of not open protocol, high hardware overhead and difficulty to reuse with existing high-speed peripherals. Considering the limitation of current multi-chip interconnection, we adopt the network centered idea to design FT_Link open architecture, which realizes the interconnection of multiple chips in a low-cost way and enables requests to access other chip spaces transparently. FT_Link system is built on common logic layer and physical layer of the existing high-speed peripherals, making it flexible and versatile. Tests and synthesis results show that FT-Link technology has the feature of high throughput and low latency with very small hardware overhead. Its performance bottleneck depends on the FIFO depth of the high-speed peripheral logical layer which it adopts. © 2021 IEEE.

关键词： Program processors Protocols Smart cities Throughput Physical layer Hardware Hazards

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Efficient Dynamic Routing in Elastic Optical Networks Based on Traffic Prediction and Bandwidth Reservation 23

Efficient Dynamic Routing in Elastic Optical Networks Based ...

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23rd international conference on Transparent Optical Networks, ICTON 2023

作者： Goścień, Róza Wroclaw University of Science and Technology Faculty of Information and Communication Technologies Department of Systems and Computer Network Wyb. Wyspianskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9798350303032

In this paper, we study the problem of efficient dynamic routing in elastic optical networks. We propose a novel resource allocation strategy that benefits from the bandwidth reservation in advance based on the analysis of the traffic prediction. We also propose a mechanism for the strategy's partial implementation to provide its high flexibility and the possibility to control both the efficiency and processing time. Next, we perform numerical experiments on representative European network topology. Their aim is the selection of the strategy's beneficial configuration as well as its performance evaluation. The results prove its high efficiency - the approach reduces the bandwidth blocking probability up to 0.85%, corresponding to 16.925 Tbps of more served traffic (compared to the allocation without traffic-aware bandwidth reservation). © 2023 IEEE.

关键词： Network topology

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KylinArm: An Arm Gesture Recognition System for Mobile Devices 1

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23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhao, Shikun Hong, Jingxuan Zhang, Zixuan Wang, Xuqiang Zhang, Jin Gong, Xiaoli Nankai Univ Coll Comp Sci Tianjin Peoples R China Tianjin Key Lab Brain Sci & Intelligent Rehabil Tianjin Peoples R China State Key Lab Highend Server & Storage Technol Jinan Peoples R China State Grid Tianjin Informat & Commun Co Tianjin Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Gesture-based Human-computer Interaction (HCI) has become a primary means of device control due to its naturalness and humanized characteristics, making it applicable for tasks such as drone control and gaming. Gesture recognition using an inertial measurement unit (IMU) has emerged as a major trend in this field. However, due to the intricate nature of the arm structure and the diversity of gestures, relying on a single IMU system for gesture recognition results in limited accuracy. Modern mobile devices, such as smartphones and smartwatches, are equipped with IMUs that allow for convenient data acquisition methods and offer computing resources for deep learning model inference. In this paper, we propose a real-time arm gesture recognition method, called KylinArm, which achieves high-precision gesture recognition by coordinating 2 IMUs. The KylinArm method is optimized for mobile devices and based on a dual-branch 1D-CNN classifier. It supports the classification of 12 arm gestures with an optimized strategy for mobile devices that have limited computation resources and power supply. Additionally, we adopt an optimization method based on CORrelation ALignment (CORAL) to address the decreasing accuracy that occurs when new users are introduced. Finally, we evaluate KylinArm and test it in real scenes, achieving a recognition accuracy of over 98%.

关键词： Arm Gesture Recognition Inertial Measurement Unit Human-computer Interaction Mobile Devices Deep Learning

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Distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

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作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 IEEE.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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Pose Adaptive Margin Loss for Face Verification 23

Pose Adaptive Margin Loss for Face Verification

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作者： Lei, Jie Zhang, BaiYan Xia, Linglin Ling, HeFei School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Nanchang University Nanchang China

ISBN: (纸本)9781665494571

Face verification is to determine whether two faces belong to the same person, and is widely used in ordinary life. Deep Learning-based Face verification (FV) has made great progress and achieved very high performance on LFW in recent years. And FV is an open-set problem, so is expected to learn discriminative features which intra-class variation of features is small and inter-class difference is large. While at the same time, we notice that the variance in pose greatly degrades the verification performance, due to the intrinsic difficulties in generalization and robust feature extraction from relative few non-frontal face examples. Margin based Softmax loss has achieved outstanding improvements, and we argue that the margin should be adjusted according to different poses. In this paper, we propose an pose adaptive margin loss to improve the generalization ability of margin based Softmax loss in face verification problems, where faces with different poses have different additive margin. Extensive experiments demonstrate that our method achieves better performance than popular margin-based approaches. © 2021 IEEE.

关键词： Training Representation learning Additives Smart cities Benchmark testing Feature extraction Task analysis

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