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检索条件"任意字段=Proceedings of the 3rd International Conference on Parallel and Distributed Information Systems"

共 4671 条记录，以下是401-410 订阅

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A Latency Guaranteed Scheduling Strategy under Performance Constraints in Big Data Stream Computing Environments 29

A Latency Guaranteed Scheduling Strategy under Performance C...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Sun, Dawei Guan, Chengjun Fan, Yinuo Rung, Jia Gao, Shang China University of Geosciences School of Information Engineering Beijing100083 China Monash University Department OfData Science and Al ClaytonVIC3800 Australia DeakinUniversity School of Information Technology Waum PondsVIC3216 Australia

ISBN: (纸本)9798350330717

Efficient utilization of computing resources in a stream computing environment is crucial for system performance. Existing scheduling strategies can hardly guarantee latency under performance constraints, let alone accounting for the communication cost incurred by scheduling itself. To address these issues, we propose Lg-Stream, a latency guaranteed scheduling strategy under performance constraints. This paper discusses the Lg-Stream strategy from the following aspects: (1) We model the topology as a queuing network to evaluate the system latency;(2) For scenarios with limited resources and latency constraints, we ensure that each executor-to-component allocation optimizes the system's processing latency to the maximum, consequently altering the components' parallelism;(3) We place executors that communicate with each other on the same node as much as possible. Experimental results demonstrated that in comparison to existing state-of-the-art scheduling strategies, it reduces the average system latency up to 30%. © 2023 IEEE.

关键词： Big data parallelism Resource scheduling Storm Stream processing

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Machine Learning for Privacy Preservation with Minimal information Leakage 3

Machine Learning for Privacy Preservation with Minimal Infor...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Verma, Mr. Vikas Kaur, Simerjeet Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

A wide range of application sectors are progressively using machine learning (ML). A successful machine learning (ML) model often requires a lot of training data and powerful processing resources, especially the newly developing deep neural network model. Due to the possibility of information leakage and the changing legislative landscapes that progressively limit Privacy-sensitive data access and use, the need for large amounts of available data, raises significant privacy concerns. Additionally, adversarial techniques. Therefore, carefully thought-out privacy-preserving machine learning (PPML) solutions are essential and are generating more and more research attention from academia and business. More and more PPML initiatives are being put forth through the incorporation of privacy-preserving methods into machine learning algorithms. As existing PPML techniques intersect with ML, systems, security, and privacy, among others, there is an important requirement to understand current research, associated barriers, and roadmaps for future research. This study offers a PGU model to help evaluate various PPML solutions by intricately breaking down each solution's privacy-preserving functions. It also evaluates and summarizes existing privacy-preserving methodologies in a methodical manner. Phase, Guarantee, and Technical Utility make up the PGU model's trinity. We also address the special features and difficulties of PPML and suggest future study avenues that will be beneficial to a wide variety of research communities in the fields of Machine learning, security systems, distributed systems and privacy system. © 2023 IEEE.

关键词： Deep neural networks

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Optimal dispatching control of distribution network based on IHHO algorithm 3

Optimal dispatching control of distribution network based on...

引用

3rd international conference on Internet of Things and Smart City, IoTSC 2023

作者： Liu, Xinghua Niu, Bingzhen Liu, Wenan Zhang, Xingyong Ma, Tao Song, Yanshan Zhang, Xinsheng Liu, Hongchen State Grid Shandong Electric Power Company Zibo Power Supply Company Shandong Zibo255000 China Department of Electrical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9781510666375

With the implementation of the "double carbon" plan, a large number of wind turbines, photovoltaic generators and energy storage devices are connected to the distribution network (DN), posing new challenges to the stability and economic operation of the DN. The uncertainty of wind power generation and photovoltaic power generation puts forward higher requirements for dispatching control of DN. The addition of energy storage devices can restrain the fluctuation of wind and solar output, but considering its high cost, it is necessary to reasonably use energy storage resources. In order to reasonably dispatch the resources in the DN and reduce the economic cost and network loss, a DN optimal dispatch method based on improved Harris Hawk algorithm (IHHO) is proposed. Firstly, Sobol sequence is used to form uniformly distributed initial population (IP) position;Secondly, the nonlinear energy factor is used to make the algorithm still have the ability to jump out of the local optimum in the later stage;Finally, the Newton iteration method is used to enhance the local search energy in the development stage. The example analysis shows that the optimal control algorithm can effectively reduce the network loss and operation cost of the DN on the basis of the safe operation of the DN. © 2023 SPIE.

关键词： distributed power generation

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Improvement and application of a three-dimensional ecological footprint evaluation model for grass and livestock 3

Improvement and application of a three-dimensional ecologica...

引用

3rd international conference on information Science, parallel and distributed systems, ISPDS 2022

作者： Zhang, Enjing Fang, Jiandong Zhao, Yudong Inner Mongolia University of Technology College of Information Engineering China Key Laboratory of Perception Technology and Intelligent Systems Inner Mongolia Autonomous Region Inner Mongolia Hohhot China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

This Compared with the single-dimensional ecologi-cal footprint evaluation, the three-dimensional ecological footprint evaluation of grass and livestock has the characteristics of categor-ical characterization of natural resource flow occupation and stock consumption status of grass and livestock, and at the same time can reflect the relationship between natural resources and sustain-able development more accurately. In this paper takes the grass-livestock balance relationship as the entry point, based on the eco-logical footprint and ecological carrying capacity, and uses the 3D ecological footprint improvement model to calculate the depth of grass-livestock footprint, the breadth of grass-livestock footprint and the 3D ecological footprint of grass-livestock in the agricul-tural and pastoral areas of each league and city in Inner Mongolia from 2018 to 2020, and then decodes the causes of formation. The results of the simulation experiment show that: at the social level, the more rural population, the larger the grass-livestock ecological footprint;at the economic level, industry accounts for a large pro-portion, there is industrial competition for food, and the grass-live-stock ecological footprint is small;at the natural level, the annual rainfall is more, and the corresponding grass-livestock ecological footprint is small. © 2022 IEEE.

关键词： Three dimensional computer graphics

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G-SM3: High-Performance Implementation of GPU-based SM3 Hash Function 28

G-SM3: High-Performance Implementation of GPU-based SM3 Hash...

引用

IEEE 28th international conference on parallel and distributed systems (IEEE ICPADS)

作者： Dong, Jiankuo Lu, Sheng Zhang, Pinchang Zheng, Fangyu Xiao, Fu Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (纸本)9781665473156

Hash is one of the most important algorithms of cryptography, it is widely used in cryptographic primitives, such as digital signature, key exchange and so on. Further, hash cryptography is also the core operation of blockchain technology. With the explosive growth of the number of IoT devices and the rapid development of blockchain technology, the computing performance of hash has received widespread attention. The GPU high-performance computing platforms with a number of arithmetic cores are widely used in cryptographic optimization and acceleration. In this paper, we propose an efficient parallel accelerated framework of SM3 cryptography hash function based on GPU parallel computing devices, short for GPU-based SM3 (G-SM3). Our G-SM3 optimizes the implementation of the hash cryptographic algorithm from three aspects: parallelism, memory access and instructions. On the desktop GPU NVIDIA Titan V, the peak performance of G-SM3 reaches 23 GB/s, which is more than 7.5 times the performance of OpenSSL on a top-level server CPU (E5-2699V3) with 16 cores. On the embedded GPU which consumes less than 40 W, the SM3 throughput reaches 3.8 GB/s, which is even better than the performance of the server-level CPU. Based on the same GTX 1080, our performance is 1.12 times that of the fastest known GPU implementation, and the latency is reduced by more than 95%. Compared to other platforms, our G-SM3 has a huge advantage.

关键词： SM3 Graphic Processing Unit CUDA Cryptographic Engineering

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Research on low-carbon optimization methods for regional energy interconnection systems considering multi-energy coupling characteristics 3

Research on low-carbon optimization methods for regional ene...

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2024 3rd international conference on Energy Utilization and Automation, ICEUA 2024

作者： Guo, Feng Dou, Zhenlan Sun, Chen Zhang, Chunyan Jiang, Kuan Guo, Bingtao Yang, Zijie Zhang, Baihan State Grid Shanghai Municipal Electric Power Company Shanghai Shanghai200122 China School of Information Engineering Nanchang University Jiang Xi Nanchang330031 China

With the increasing application of multi-energy cogeneration, distributed renewable power generation, and multi-energy storage in various multi-energy systems, their efficient collaborative optimization has become the key to further improving emissions. This article utilizes energy hubs to integrate energy equipment with multi-energy coupling and multi-source supply characteristics and constructs a regional interconnected energy system that considers multi-energy coupling characteristics. A multi-objective optimization model for the above-mentioned energy system is established, including economic, low-carbon, and safe aspects such as operating costs, process emissions, and operational safety, and the raccoon optimization algorithm is applied to solve the model. The experimental results show that compared with classical scheduling algorithms, the proposed method can effectively reduce carbon emissions at a certain operating cost. © Published under licence by IOP Publishing Ltd.

关键词： Operating costs

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visual perception preserved denoising network in Image translation 3

visual perception preserved denoising network in Image trans...

引用

3rd international conference on information Science, parallel and distributed systems, ISPDS 2022

作者： Xu, Ningyuan Song, Kangkang Xiao, Jiangjian Peng, Chengbin School of Materials Science and Optoelectronic University of Chinese Academy of Sciences Beijing China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

Image denoising is a fundamental problem in computer vision and has received much attention from scholars. With the fast development of convolutional neural networks, more and more deep learning-based noise reduction algorithms have emerged. However, current image denoising networks tend to apply image noise reduction only in the RGB color space, ignoring the information at the visual perception level, making the images generated by these algorithms too smooth and lacking texture and details. Therefore, this paper proposes a novel noise reduction network in the image translation area using deep learning feature space instead of the traditional RGB color space to restore more realistic and more detailed texture information in generated images. The network contains a visual perception generator and a multi-objective optimization network. The generator includes a multiscale encoding-decoding sub-network, which extracts high-level perception features from input images. The optimization network contains content consistency loss, multiscale adversarial generation loss, and discriminator feature alignment loss, which effectively retains detailed texture information in the images. We synthesized noise of suitable intensity based on publicly available data sets and conducted multiple experiments to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm significantly improves textures and details in denoised images. The algorithm removes a large amount of noise information while preserving lots of perceptual information at the visual level, generating more realistic images with detailed texture features. © 2022 IEEE.

关键词： Image denoising

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Globally optimal cost control for distributed multi-AUV formation with time-varying communication delays and clock errors 3

Globally optimal cost control for distributed multi-AUV form...

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3rd international conference on Control and Intelligent Robotics, ICCIR 2023

作者： Xu, Jian Wei, Shunxing Yin, Liangang College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Qingdao Innovation Development Base Harbin Engineering University Qingdao266000 China

ISBN: (纸本)9781510671867

This paper studies the globally optimal coordinated control problem of distributed autonomous underwater vehicle (AUV) formation. Remarkably, the global cost function and communication topology are pregiven, which makes the traditional Riccati-based strategy inapplicable. As the main contribution of this paper, an effective design strategy for optimal distributed controller is presented. Detailedly, the formation coordination control problem of multi-AUV system is first transformed into the consensus problem of AUV formation, and the consensus state of leader-follower AUV formation is defined. Secondly, considering the relative drift and offset of AUV clock and the bounded time-varying communication delays in the interaction network, a prediction-based formation consensus controller is proposed by introducing the clock synchronization strategy. Moreover, the stability of AUV formation with this controller is proved. Then, by deeply exploring the internal relationship between global cost and relative state errors, the fully explicit formula of the cost function with respect to gain parameters is derived. Based on this, the existence of the optimal distributed controller is further proved. Simulation results show that the proposed optimal controller is effective. © 2023 SPIE.

关键词： Global optimization

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PACDNet:A landslide extraction network based on parallel attention 3

PACDNet:A landslide extraction network based on parallel att...

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3rd international conference on Environmental Remote Sensing and Geographic information Technology, ERSGIT 2024

作者： Zhu, Meijiang Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

Landslides pose a serious threat to the economy and human safety. Rapid and efficient landslide extraction is essential for post-disaster relief and reconstruction efforts. Current landslide extraction techniques still face challenges in accurately identifying landslides boundaries. To address this issue, we propose the parallel Attention Change Detection Network (PACDNet), a network designed to extract landslides by calculating changes between pre-disaster and post-disaster images. Our method is an encoder-decoder network that utilizes a Siamese network structure with StarBlock from StarNet in the encoding stage, allowing for higher feature extraction capability with lower computational costs. In the decoding phase, the parallel Attention Module (PAM) enhances the model's landslide boundaries extraction capability by simultaneously computing spatial attention and channel attention. To validate the effectiveness of our proposed PACDNet, we used the Global Very-High-Resolution Landslide Mapping (GVLM) dataset. This dataset includes pre-disaster and post-disaster images of 17 different landslide events. After comparing our model with four recently proposed models, PACDNet demonstrated improvements of 2.31% and 3.41% in Intersection over Union (IoU) on the Kaikoura and Tbilisi datasets, respectively. This indicates that PACDNet has a significant advantage in landslide extraction across different landslide events. © 2025 SPIE.

关键词： Network coding

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An Approach to Deploying Convolutional Neural Networks Based on FPGA Technology for Image Recognition 3rd

An Approach to Deploying Convolutional Neural Networks Based...

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3rd international conference on Advances in information and Communication Technology, ICTA 2024

作者： Phuong, Huy Nguyen Mai, Thuong Duong Thi Thai Nguyen University of Technology Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

Nowadays, deep learning architectures like CNN have proven their superiority in image recognition tasks. To effectively deploy CNN networks in practice, especially for AIoT applications, it is essential to find a network model that offers good recognition performance with a small size and a limited number of parameters. Additionally, when deploying to hardware, to ensure rapid task execution, an FPGA-based approach is a suitable choice due to its parallel processing capabilities, low power consumption, low latency, and reconfigurability. In this paper, the authors propose a solution for searching and implementing an optimal CNN model on an FPGA chip for handwritten digit recognition using the MNIST dataset. The comparative results of recognizing 10,000 MNIST image samples on FPGA, CPU, and Raspberry Pi 4 microcontroller demonstrate that the proposed solution achieves high accuracy (98.78%) while maintaining low execution time (36 s). This indicates the potential application of the proposed solution in deploying deep learning tasks in practical applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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