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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Ling Zhang, Ping-Jun Hu, Peng-Jun Yang, Liu Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030586683

In order to solve the problem that there are few blind path data sets and a lot of manual data collection work in the current blind guide system, computer vision algorithm is used to automatically generate blind path images in different environments. Methods a blind path image generation method based on the depth convolution generative adversary network (DCGAN) is proposed. The method uses the characteristics of typical blind path, which is the combination of depression and bulge. The aim of long short memory network (LSTM) is to encode the depression part, and the aim of convolution neural network (CNN) is to encode the bulge part. The two aspects of information are combined to generate blind path images in different environments. It can effectively improve the blind path recognition rate of the instrument and improve the safe travel of the visually impaired. Conclusion generative adversarial networks (GANs) can be used to generate realistic blind image, which has certain application value in expanding blind channel recognition data, but it still needs to be improved in some details. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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SteadySketch: Finding Steady Flows in data Streams

SteadySketch: Finding Steady Flows in Data Streams

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International Workshop on Quality of Service

作者： Xiaodong Li Zhuochen Fan Haoyu Li Zheng Zhong Jiarui Guo Sheng Long Tong Yang Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China

In this paper, we study steady flows in data streams, which refers to those flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the required memory space by 75%. Experimental results show that SteadySketch improves the Precision Rate (PR) by 81.1% and reduces the Average Relative Error (ARE) by 955.3× compared with the strawman solution. Finally, we provide a concrete case: cache prefetch, and prove that SteadySketch can effectively improve the cache hit ratio. All related codes of SteadySketch are open-source and available at GitHub.

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SketchConf: A Framework for Automatic Sketch Configuration

SketchConf: A Framework for Automatic Sketch Configuration

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International Conference on data engineering

作者： Ruijie Miao Fenghao Dong Yikai Zhao Yiming Zhao Yuhan Wu Kaicheng Yang Tong Yang Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github.

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Cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

作者： Chang, Kuo-Chi Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

In the case of power grid cascading trip, this study puts forward a strategy to prevent cascading trip by changing the output of generator set. Firstly, according to the specific performance of the power grid under the cascade tripping, combined with the action behavior of the line current-type backup protection, and an expression equation for measuring the safety level index between the power grid and the cascading trip failure is given. Secondly, the vulnerable branch is found out through the safety level index, the output of the key power generation nodes on the branch is taken as the optimization variable, and the optimization model is established to improve the safety level index under the chain trip of the power grid. Finally, a combination of sensitivity analysis and particle swarm optimization(PSO) was used to analyze an example on the IEEE-39 node system. The results demonstrate the feasibility and effectiveness of the model and method, and provide a reference for preventing chain trip accidents. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensitivity analysis

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Battery Valuation and Management for Battery Swapping Station with an Intertemporal Framework

arXiv

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arXiv 2023年

作者： Chen, Xinjiang Yang, Yu Wang, Jianxiao Song, Jie He, Guannan College of Engineering Peking University Beijing100091 China Department of Industrial and Systems Engineering University of Florida GainesvilleFL116595 United States National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100091 China

Battery swapping as a business model for battery energy storage (BES) has great potential in future integrated low-carbon energy and transportation systems. However, frequent battery swapping will inevitably accelerate battery degradation and shorten the battery life accordingly. To model the tradeoff of BES use between energy and transportation applications coupled by battery swapping, we develop a life-cycle decision model that coordinates battery charging and swapping. This model is derived based on an improved intertemporal decision framework, in which the optimal marginal degradation cost (MDC) of BES is determined to maximize the BES benefit across time and application. The proposed framework and model are applied to manage a battery swapping station that simultaneously provides battery swapping services to electric vehicle customers and provides flexibility service to the power grid, including energy arbitrage and reserve. The case study shows that while the end of the physical life of BES occurs faster with battery swapping, the economic life becomes considerably longer. The results also reveal that the optimal MDC depends on the battery values in each application, and we analyze how the battery swapping price affects the optimal MDC and battery life. The proposed framework and model can also provide decision support for on-demand BES service, such as battery trading, renting and secondary use. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision support systems

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ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

arXiv

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arXiv 2022年

作者： Jiang, Yuezihan Cheng, Yu Zhao, Hanyu Zhang, Wentao Miao, Xupeng He, Yu Wang, Liang Yang, Zhi Cui, Bin School of Software and Microelectronics Peking University China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Alibaba Group China

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the massive user data at Taobao: low training/serving efficiency due to the huge scale of the graphs, and low recommendation quality due to the information overload which distracts the recommendation model from specific user intentions. ZOOMER achieves this by introducing a key concept, Region of Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the graph with significant relevance to a strong user intention. ZOOMER narrows the focus from the whole graph and "zooms in" on the more relevant ROIs, thereby reducing the training/serving cost and mitigating the information overload at the same time. With carefully designed mechanisms, ZOOMER identifies the interest expressed by each recommendation request, constructs an ROI subgraph by sampling with respect to the interest, and guides the GNN to reweigh different parts of the ROI towards the interest by a multi-level attention module. Deployed as a large-scale distributed system, ZOOMER supports graphs with billions of nodes for training and thousands of requests per second for serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with comparable (even better) AUC performance than baseline methods. Besides, both the offline evaluation and online A/B test demonstrate the effectiveness of ZOOMER. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

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engineering applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Shiji Zhang, Shitao Tian, Hao Deveci, Muhammet Liu, Xiaodi School of Microelectronics and Data Science Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Ma'anshan243032 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan

Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis. © 2025 The Authors

关键词： Information granules

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Optimal Power Generation Output Considering Cascading Failure 1

引用

作者： Deng, Hui-Qiong Luo, Jie Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Aiming at the cascading fault phenomenon of power system, this paper analyzes the disturbed branches involved in the initial fault and their relationship with the power generating nodes. Firstly, according to the performance of the initial fault branch in the cascading fault phenomenon and the action of relay protection, an expression form of evaluating the severity of the initial fault branch is given. Secondly, the relationship between the current of the residual system branch and the injection power of the nodes after the initial fault removal is analyzed. Then, on the basis of synthesizing the severity of the disturbed branch and its correlation with the power generating nodes, the key power generating nodes on the seriously disturbed branch are selected by clustering analysis. Finally, based on the key power generation nodes, the optimal power generation output model considering the power generation operation cost was established, the model was solved by particle swarm optimization (PSO), the model was simulated by IEEE39 bus system in MATLAB environment, and the correctness of the established model and method was verified. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation and Optimization

arXiv

引用

arXiv 2018年

作者： Hu, Zhiwen Bai, Zixuan Bian, Kaigui Wang, Tao Song, Lingyang National Engineering Laboratory for Big Data Analysis and Applications School of Electronics Engineering and Computer Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China

—Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides real-time and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Zero-Order Optimization in Security Constrained Power System Scheduling 4

Zero-Order Optimization in Security Constrained Power System...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： An, Qi Wang, Jianxiao Wu, Shengyu Ren, Weikai Huang, Ruanming Li, Gengyin North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China Peking University National Engineering Laboratory for Big Data Analysis and Applications Beijing China State Grid Energy Research Institute CO. LTD. Beijing China Institute of Energy Peking University Beijing China Planning Center State Grid Shanghai Electric Power Company Shanghai China

ISBN: (纸本)9798350395914

The security and resilience of power systems are gradually challenged. As a typical index measuring the security and resilience of power systems, the frequency stability issue is becoming prominent. Due to the increase in the complexity of power systems, the frequency stability boundary is gradually becoming fuzzy, leading to traditional analytical-modeling algorithms hardly applicable for frequency stability constrained dispatch. Existing data-driven methods for characterizing frequency stability boundary typically requires massive simulation calculations, leading to unnecessary computing power consumption. To this end, zeroth-order (ZO) optimization, which aims to solve optimization problems embedding non-analytic functions, is adopted for solving the frequency stability constrained dispatch in this paper. In contrast to traditional data-driven methods, we directly embed frequency stability simulation check into power system dispatch. The gradient of frequency stability simulation check is estimated through ZO optimization, thereby realizing the interaction optimization between power system dispatch and real-time simulation. In this way, the solution for the frequency stability constrained dispatch is modified towards the optimal point along the gradient descent direction. Cases studies based on IEEE-9 bus system verifies that, compared with traditional data-driven methods, the solution approach we propose could reduce the system cost while significantly improving the calculation efficiency. © 2024 IEEE.

关键词： Frequency stability

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