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检索条件"任意字段=6th International Conference on the Art, Science, and Engineering of Programming, Programming 2022"

共 1074 条记录，以下是431-440 订阅

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artificial Bee Colony Optimization for Delay and Cost Aware Task Scheduling in Serverless Computing Environment

Artificial Bee Colony Optimization for Delay and Cost Aware ...

引用

Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Shah Jalal Palash Roy Sudip Chandra Ghoshal Subrato Basak Belal Hoshan Md. Abdur Razzaque Saiful Azad Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Serverless edge computing is increasingly adopted in smart cities and industrial automation applications, which leverages cloud computing for scalable resources and uses Function as a Service (FaaS) for efficient load balancing, pay-as-you-go execution, and third-party management of provisioning and auto-scaling. However, task scheduling in serverless environments is challenging due to service latency, provider costs, and cold start issues caused by dynamic workloads and resource availability. While existing literature has focused on task scheduling, it often overlooks the joint minimization of service delay and provider costs, as well as long-term workloads, and energy use. In this work, we propose an optimization framework using mixed integer linear programming (MILP) to jointly minimize task execution delay and provider costs, namely DECASE. Given the NP-hard nature of the optimization for large networks, we developed a metaheuristic artificial Bee Colony (ABC) algorithm to provide near-optimal task scheduling and resource allocation within polynomial time. the developed DECASE system significantly reduces delays and serverless resource provider costs by up to 20% and 25% compared to existing methods.

关键词： Costs Job shop scheduling Processor scheduling Smart cities Serverless computing Dynamic scheduling Polynomials Delays Optimization Edge computing

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Fault Diagnosis of Surge Arresters Based on Swarm Optimization Support Vector Neural Network

Fault Diagnosis of Surge Arresters Based on Swarm Optimizati...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Liyuan Zhu Xiaodong Zhang Ying Jin Yong Ding Yamei Luo Zhijun Zhang Yongxia Han Jiantao Zhang Lin Yang Meizhou Power Supply Bureau Guangdong Power Grid Limited Liability Company Meizhou China School of Automation Science and Engineering South China University of Technology Guangzhou China Institute of Electric Power South China University of Technology Guangzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

As the requirements for reliability and safety in power systems continue to rise, accurate assessment of the fault status of critical equipment such as surge arresters becomes increasingly important. Traditional fault assessment methods usually rely on physical inspections or simple data analysis techniques, which often fail to comprehensively reflect the real-time fault status of surge arresters and have limitations in data processing and prediction accuracy. In response to these challenges, a fault status assessment method for surge arresters using swarm optimization support vector neural networks (SOSVNN) is proposed. this method integrates the high accuracy of support vector machine (SVM) with the sequential data processing capabilities of varying parameter recurrent neural networks (VPRNN), and utilizes genetic algorithm (GA) to optimize kernel function parameters and penalty factors, effectively enhancing the accuracy and efficiency of fault status assessments for surge arresters.

关键词： Support vector machines Arresters Accuracy Recurrent neural networks Vectors Safety Quadratic programming Surges Particle swarm optimization Genetic algorithms

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Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear programming for Photovoltaic System Efficiency Improvement under Sudden Change of Environmental Conditions

Hybrid Model of Ensemble RUSBoosted Tree Optimized by Linear...

引用

IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Mpho Sam Nkambule Ali N. Hasan Ahmed Ali Department of Electrical Engineering Science University of Johannesburg Auckland Park South Africa Department of Electrical Engineering Science Higher College of Technology Abu Dhabi United Arab Emirates

Photovoltaic (PV) farms can supply vital merits to the electrical systems economically and environmentally. Nevertheless, the variation of the PV power leads to technical challenges under partially shaded conditions (PSC). For this reason, Global maximum power point (GMPP) tracking represents a critical mechanism for power optimization uncertainty to guarantee system stability. In this study, a novel hybrid algorithm for GMPP tracking for a PV system under PSC is presented. the proposed hybrid system integrates machine learning (ML) Ensemble RUSBoosted tree (ERBT) and Metaheuristic Linear programming (LP) to solve optimization problems under PSC with a fast response time and good convergence speed. the integration of ERBT-LP combines the benefits of the two algorithms into a single model. the presented algorithms have been tested and validated using MATLAB SIMULINK environment with very promising results.

关键词： Maximum power point trackers Photovoltaic systems Training Machine learning algorithms Uncertainty Machine learning System integration

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Video Enhancement with Single Frame 1

引用

6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Galshetwar, Vijay M. Patil, Prashant W. Chaudhary, Sachin Punjab Engineering College Chandigarh India Applied Artificial Intelligence Institute Deakin University Geelong Australia

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Hazy scenes captured by the camera in a hazy environment are the main cause of the deterioration of captured video quality. Hazy environmental conditions degrade the quality of captured videos which leads to poor visibility and color distortion in videos. In this paper, we propose a video enhancement technique with a single frame dehazing encoder-decoder network. the proposed method focuses to restore the haze-free video from the hazy video by developing an encoder-decoder-based dehazing network. the framework comprises of encoder block (EB) and decoder block (DB). Both EB and DB are used to extract the haze-relevant feature maps and to give more attention to haze-specific features by re-calibrating the weights of learned feature maps automatically and helps to recover the haze-free frame. the use of dilated residual module (DRM) helps to improve output results and gain more contextual information by widening the receptive field. the extensive analysis of the proposed method is carried out by considering benchmark synthetic hazy video databases and analyzed quantitative results. Experimental results show that the proposed method out performs the other state-of-the-art (SOTA) existing approaches for video frame dehazing. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deterioration

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Single Frame-Based Video Dehazing with Adversarial Learning 1

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6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Galshetwar, Vijay M. Patil, Prashant W. Chaudhary, Sachin Punjab Engineering College Chandigarh India Applied Artificial Intelligence Institute Deakin University Geelong Australia

ISBN: (数字)9783031113499

ISBN: (纸本)9783031113482

Hazy environmental conditions degrade the quality of captured videos which leads to poor visibility and color distortion in videos. Such deterioration of captured video quality is mainly because of the attenuation caused by the scattering of light due to the haze particles present in the environment. In this paper, we propose an adversarial learning based single frame video dehazing encoder-decoder network. the proposed method comprises of Dilated Residual Block (DRB) used as encoder and Skip connection. DRB module is used to gain more contextual information by achieving large receptive fields. Skip connections are established between each encoder and decoder, which helps to detect and give more attention to haze specific features by adjusting the weights of learned feature maps automatically. this helps to extract the haze-relevant feature maps and recovers the haze-free video frame by using Channel Attention Block (CAB) and Residual Block (ResB) respectively. An extensive quantitative and qualitative analysis of the proposed method is done on benchmark synthetic hazy video database namely DAVIS-16 and NYU depth. Experimental result shows that the proposed method outperforms the other existing state-of-the-art (SOTA) approaches for video dehazing. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deterioration

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Optimization of Distribution Network Considering Distributed Renewable Energy and V2G of Electric Vehicles

Optimization of Distribution Network Considering Distributed...

引用

Power and Energy engineering (ICPEE), international conference on

作者： Yiran Xiao Chuan He Xiangmei Lv Yicheng Li Min Zhang Tengxin Wang College of Electrical Engineering Sichuan University Chengdu China Electric Power Research Institute State Grid Shanxi Electric Power Company Shanxi China

ISBN: (纸本)9781665464765

With the increasing number of electric vehicles (EVs), the connection of large-scale electric vehicles will affect the stability of the power system. Based on the analysis of electric vehicles travel characteristics, this paper proposes an optimal scheduling model for vehicle to grid (V2G) with the cost of power system operation as the objective function. then, simulation calculations are carried out through the IEEE 33-node distribution network by MATLAB. through the results, the scheduling model for vehicles to grid attaches great significance to reducing system cost, flattening the load curve and reducing the wind power curtailment. It is concluded that the optimization of distribution network is beneficial to improve the reliability of the power grid.

关键词： Vehicle-to-grid Costs Distribution networks Optimal scheduling Power system stability Wind power generation Linear programming

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Compressors Evolution based High Speed and Energy Efficient Approximate Signed Multiplier 16

Compressors Evolution based High Speed and Energy Efficient ...

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16th IEEE international conference on Solid-State and Integrated Circuit Technology, ICSICT 2022

作者： Xie, Na Zhang, Renyuan Yan, Han Xie, Chonghang Zhang, Hao Cai, Hao Liu, Bo Southeast University School of Integrated Circuits Wuxi211189 China Nanjing Research Institute of Electronics Technology Nanjing210096 China Southeast University School of Electronic Science and Engineering Nanjing210096 China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

Among various energy improvement techniques, approximate computing can reduce power, area and delay at the cost of calculation accuracy losses. In this paper, we propose an approximate signed multiplier design with a low power consumption and a short critical path using Cartesian Genetic programming (CGP) algorithm and a compressors evolution method to obtain more suitable approximate 3-2 compressors for partial products (PPs) accumulation to compensate the error generated by CGP. Experimental results show that the proposed signed multiplier reduces delay by 42%, area by 25%, power consumption by 24% and the power delay product (PDP) by 56.6%. the NMED of proposed multiplier and recognition accuracy increased from 0.885% to 0.736% and 87.29% to 92.61% respectively through compressors evolution method. the proposed multiplier is also evaluated in a CRNN-based keyword spotting (KWS) system which brought high efficiency and little accuracy loss. © 2022 IEEE.

关键词： Compressors

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A Multilevel Non-interference Vulnerability Analysis Method for Information Leakage Problem 6

A Multilevel Non-interference Vulnerability Analysis Method ...

引用

6th IEEE international conference on Data science in Cyberspace, DSC 2021

作者： Zhong, Wenjing Zhao, Jinjing Hu, Hesuan National Key Laboratory Of Science And Technology On Information System Security Beijing100101 China School Of Electro-Mechanical Engineering Xidian University Xi'an Jiaotong University State Key Laboratory For Manufacturing Systems Engineering Shaanxi Xi'an710071 China

ISBN: (纸本)9781665418157

Security vulnerabilities caused by information leakage are a critical aspect of the security for information systems. Such vulnerabilities can be identified by non-interference analysis. Strong nondeterministic non-interference (SNNI) and bisimulation SNNI (BSNNI) are the typical properties of non-interference. Existing works related to non-interference analysis are mainly based on the setting with only two security levels. Such setting is coarse-grained compared to the real system. In this paper, we analyze the non-interference with multiple security levels by integer linear programming. Obviously, the non-interference with multiple security levels is more fine-grained but more complex than that with only two security levels. On the perspective of complexity, we provide the general and simplified non-interference analyses in turn for multilevel SNNI and BSNNI which are defined on the concepts of upper sets. the general analysis is derived from the definitions of multilevel SNNI and BSNNI, and solve the identification problem of multilevel SNNI and BSNNI directly. By analyzing and removing the impact of upper sets' coverability, the simplified analysis reduces the redundancy and improves the efficiency in the analysis process. © 2021 IEEE.

关键词： Integer programming

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Structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated Images 6th

Structure-Texture Decomposition-Based Enhancement Framework ...

引用

6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Haritha, K.M. Sreeni, K.G. Zacharias, Joseph Jeena, R.S. Department of Electronics and Communication Engineering College of Engineering Trivandrum Thiruvananthapuram India

ISBN: (纸本)9783031113451

Images acquired in poor illumination conditions are characterized by low brightness and considerable noise which constrain the performance of computer vision systems. Image enhancement thus remains crucial for improving the efficiency of such systems. To improve the visibility of low-light images, a novel image enhancement framework based on the structure-texture decomposition is proposed in this paper. Firstly, the low-light image is split into structure and texture layers using the total-variation (TV) based image decomposition approach. the structure layer is initially diffused using Perona-Malik (PM) diffusion model and the local and global luminance enhancement is incorporated in the structure-pathway using an expanded model of biological normalization for visual adaptation. In the texture pathway, the suppression of local noise and the enhancement of image details are attained with the estimation of the local energy of the texture layer and the strategy of energy weighting. Eventually, the final enhanced image of improved quality is obtained by merging the modified structure and texture layers. the effectiveness of the proposed framework is validated using no-reference image quality metrics including IL-NIQE, BRISQUE, PIQE, and BLIINDS-II. the experimental results show that the proposed method outperforms the state-of-the-art approaches. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

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Adaptive Rough-Fuzzy Kernelized Clustering Algorithm for Noisy Brain MRI Tissue Segmentation 6th

Adaptive Rough-Fuzzy Kernelized Clustering Algorithm for No...

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6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Choudhuri, Rudrajit Halder, Amiya St. Thomas College of Engineering and Technology 4 D. H. Road Kolkata India

ISBN: (纸本)9783031113451

Image Segmentation is a crucial processing technique for segregating regions in medical images for analysis and abnormality detection. In this paper, an adaptive rough-fuzzy set based kernelized C means clustering algorithm is presented which is aimed at robust segmentation of brain magnetic resonance imaging (MRI) data. the algorithm is developed by amalgamating fuzzy set and rough set theory along with an adaptive substructure based kernelized distance metric to form a reliable clustering approach for seamless segmentation. Rough and fuzzy sets help in dealing with uncertainties caused due to vagueness, imperceptibility, ambiguities, and overlappingness while clustering data points. Incorporation of an adaptive substructure into the algorithm compensates for noise corruptions and inhomogeneities of intensity values in the image by taking the local neighborhood features and spatial information into consideration. the kernelized distance metric leverages linear separability and allows for easy segregation by mapping data points to a higher dimensional space while preserving fine details and spatial features. Upon experimentation, the proposed approach is proved to be reliable and manages to achieve superior performance when compared to the state of the art methods, especially when segmenting images corrupted by noise or any other artifacts. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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