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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是381-390 订阅

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Coordination of Flexible Resources for Improving Distribution Grid PV Hosting Capacity 7

Coordination of Flexible Resources for Improving Distributio...

引用

7th IEEE international conference on Power and Renewable Energy, ICPRE 2022

作者： Yue, Yuanyuan Wang, Ting Yan, Huan Jia, Honggang Wang, Hui Luo, Xuan Chen, Chen State Grid Shaanxi Electric Power Economic and Technology Research Institute Department of Planning and Evaluation Xi'an China Xi'an Jiaotong University School of Electrical Engineering Xi'an China

ISBN: (数字)9781665450638

ISBN: (纸本)9781665450638

the ever-increasing penetration of distributed photovoltaic (PV) poses challenges to the safe and reliable operation of distribution grids, which limit the hosting capacity of PVs. the flexible resources in distribution grids provide potentials to adjust the operation of grids to cope with the uncertainty from the PVs, so that the PV power output can be better accommodated without jeopardizing operation of distribution grids. this paper proposes an optimization model to coordinate various types of flexible resources such as electric vehicles, mobile energy storage, and network reconfiguration, to maximize the accommodation of PVs in an economical manner. A mixed-integer linear programming model is formulated to model the operation characteristics of flexible resources as well as the operational constraints of distribution grid. the proposed model is validated based on IEEE 37-node test feeder and results show that the promotion of PV accommodation can be achieved, which is of great significance to promote the construction of a new power system with new energy as the main body. © 2022 IEEE.

关键词： Energy storage

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Switching Harmonic Attenuation of Grid-Tied Converters Using LLCL Filters and MPC-SVPWM

Switching Harmonic Attenuation of Grid-Tied Converters Using...

引用

China international Electrical and Energy conference (CIEEC)

作者： Muhamamd Waqas Qaisar Jingyang Fang Wenrui Li School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

this paper introduces an enhancement in grid-tied converter technology by incorporating a Model Predictive Control (MPC)-based space vector pulse width modulation (SVPWM) along with an advanced $LLCL$ filter. this approach aims to improve the conversion efficiency in grid-tied applications to three-phase ac, offering better harmonic attenuation than standard $LC$ and $LCL$ filters. the implementation of dual-mode control for dc voltage stabilization and the use of quadratic programming for inverter switching optimization is proposed to reduce switching losses, thereby augmenting system efficiency. Comparative simulations demonstrate a measurable efficiency improvement over traditional linear inverter methods, indicating a notable advancement in grid-tied converter technology, especially in terms of harmonic reduction and overall system performance enhancement.

关键词： Space vector pulse width modulation System performance Switching loss Switches Harmonic analysis Inverters Quadratic programming

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Intrusion Detection System based on GRU

Intrusion Detection System based on GRU

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2023 7th international conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Bayi Xu Lei Sun Ruiyang Ding Chengwei Liu School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Information Engineering University Zhengzhou China The 28th Research Institute of China Electronics Technology Group Corporation Nanjing China

In recent years, there has been a rise in network attack incidents. Efficiently detecting network attack behaviors is crucial for ensuring cybersecurity in resource-limited environments. this paper proposes a lightweight intrusion detection system based on GRU (Gated Recurrent Unit). the system uses information gain and Extra Tree Classifier methods to rank and select important features. Temporal features of network traffic are extracted using gated recurrent neural networks, and classification is performed using the softmax function. To validate its effectiveness, the NSL-KDD dataset is used for evaluation, and experimental results show that the proposed model outperforms other state-of-the-art models.

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A Hybrid Algorithm for the Cutting-Lines-Oriented Steel Plate Yard Outbound Problem

A Hybrid Algorithm for the Cutting-Lines-Oriented Steel Plat...

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Automation, Control and Robots (ICACR), international conference on

作者： Lebao Wu Zuhua Jiang Minghao Li School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China China Merchants Industry Holdings Corporation (Jiangsu) Jiangsu China

this paper considers the outbound operation of the steel plate yard oriented to the cutting lines in shipbuilding. According to the daily cutting plan, the yard should arrange the outbound order of cutting plates and the relocation strategy for the blocking plates. the objective of this problem is to minimize the makespan of the outbound and cutting operations. A hybrid algorithm composed of two parts is introduced to solve this problem. In the first part, two heuristics are proposed to decide the outbound order. And the second part is a mixed integer programming model determining the relocation strategy. the experiment based on the actual cutting data is conducted to show the performance of the proposed method.

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Explainable AI for Intrusion Prevention: A Review of Techniques and Applications 7th

Explainable AI for Intrusion Prevention: A Review of Techniq...

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7th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Chandre, Pankaj R. Vanarote, Viresh Patil, Rajkumar Mahalle, Parikshit N. Shinde, Gitanjali R. Nimbalkar, Madhukar Barot, Janki Department of Computer Science and Engineering MIT School of Computing MIT ADT University Loni Kalbhor India Department of Information Technology MIT School of Computing MIT ADT University Loni Kalbhor India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Kondhawa India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhawa India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

Intrusion prevention has become a critical concern in today's increasingly complex and interconnected digital systems. Traditional intrusion prevention methods often rely on rule-based approaches that can be inflexible and prone to false positives and false negatives. the emergence of explainable artificial intelligence (AI) techniques has the potential to provide more accurate and interpretable solutions for intrusion prevention. this review paper surveys the current state of the art in explainable AI techniques for intrusion prevention, including their strengths and limitations. We begin by providing an overview of intrusion prevention and the challenges faced by traditional methods. We then explore the different types of explainable AI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Integrated Gradients, and their application to intrusion prevention. We also discuss the different types of datasets and evaluation metrics used in explainable AI-based intrusion prevention, including public datasets such as NSL-KDD and UNSW-NB15. Finally, we highlight the future research directions in explainable AI-based intrusion prevention, including the need for larger and more diverse datasets, better evaluation metrics, and more comprehensive explainable AI models. Overall, this review paper provides a comprehensive overview of the state of the art in explainable AI for intrusion prevention, and serves as a valuable resource for researchers and practitioners in the field. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Additives

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RtTSLC: A Framework for Real-Time Two-Handed Sign Language Translation 7th

RtTSLC: A Framework for Real-Time Two-Handed Sign Language ...

引用

7th international conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ramanujan, Ashwin Srinivasa Boggaram, Ankith Sharma, Aryan Bharathi, R. Boggaram, Aaptha Department of Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9789819907687

Indian Sign Language is one of the most prevalent sign languages used in South Asia. Being a two-handed language, Indian Sign Language faces various problems when it comes to recognition and classification. the research on the interpretation of such two-handed sign languages is still in its early stages of growth. To explore the real-time translation of sign languages, the RtTSLC framework is proposed, which translates 26 finger-spelled English alphabet gestures in Indian Sign Language to their corresponding letters. the framework introduces a new technique that creates bounding boxes for two-handed sign languages, and as for classification, state-of-the-art deep learning architectures like VGG16, EfficientNet, and AlexNet have been used. the RtTSLC framework provided promising results, with a recognition accuracy of 92% with the VGG16 architecture, 99% with EfficientNetB0, and 89% with AlexNet in real time. the results provide evidence that the real-time classification framework proposed is viable for recognition and will help the community of people who are hard of hearing. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Deep reinforcement learning based hierarchical eco-driving strategy for a series-parallel hybrid electric truck

Deep reinforcement learning based hierarchical eco-driving s...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Rui Xue Yahui Zhao Zhong Wang Zimeng Wang Yahui Zhang China North Vehicle Research Institute Beijing China School of Vehicle and Energy Yanshan University Qinhuangdao China School of Mechanical Engineering Yanshan University Qinhuangdao China

It has been well recognized that the development of information technology and the construction of intelligence infrastructure are becoming better solution to energy consumption and pollutant emissions. In this paper, deep deterministic policy gradient (DDPG) algorithm is embedded into a hierarchical eco-driving framework which is designed to improve the driving performance. the structure is divided into two layers, namely, the upper adaptive cruise control (ACC) and the lower energy management strategy (EMS). Because of the complex power model, pre-optimized progress is embedded into the DDPG loop. the experimental results show that compared with the Green Light Optimal Speed Advice (GLOSA) strategy with similar travel time, the proposed EMS can be reduced by about 8% and the fuel economy can reach 87.2% to 90.7% of dynamic programming (DP) based EMS. Overall, the proposed framework shows better energy saving advantages.

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Few-Shot Learning for Plant Disease Classification Using ILP 12th

Few-Shot Learning for Plant Disease Classification Using IL...

引用

12th international Advanced Computing conference, IACC 2022

作者： Varghese, Dany Patel, Uzma Krause, Paul Tamaddoni-Nezhad, Alireza University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031356407

Plant diseases are one of the main causes of crop loss in agriculture. Machine Learning, in particular statistical and neural nets (NNs) approaches, have been used to help farmers identify plant diseases. However, since new diseases continue to appear in agriculture due to climate change and other factors, we need more data-efficient approaches to identify and classify new diseases as early as possible. Even though statistical machine learning approaches and neural nets have demonstrated state-of-the-art results on many classification tasks, they usually require a large amount of training data. this may not be available for emergent plant diseases. So, data-efficient approaches are essential for an early and precise diagnosis of new plant diseases and necessary to prevent the disease’s spread. this study explores a data-efficient Inductive Logic programming (ILP) approach for plant disease classification. We compare some ILP algorithms (including our new implementation, PyGol) with several statistical and neural-net based machine learning algorithms on the task of tomato plant disease classification with varying sizes of training data set (6, 10, 50 and 100 training images per disease class). the results suggest that ILP outperforms other learning algorithms and this is more evident when fewer training data are available. © 2023, Springer Nature Switzerland AG.

关键词： Neural networks

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Defense Strategies for Integrated Energy Cyber Physical Systems Considering Joint Attacks

Defense Strategies for Integrated Energy Cyber Physical Syst...

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Energy, Electrical and Power engineering (CEEPE), international conference on

作者： Danyan Gu Lucheng Hong Qiangqiang Yu Liang Hua College of Software Southeast University Nanjing China College of Electrical Engineering Southeast University Nanjing China State Grid Wuxi Power Supply Company Science and Technology Digital Department Wuxi Power Supply Company Wuxi China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

In the context of a highly interconnected energy landscape, coordinated cyber-physical attacks on integrated energy systems can cause significant damage and trigger considerable economic impacts. In order to ensure the robustness and uninterrupted function of such systems, the interaction between information and physical matrix is analyzed based on the inherent coupling mechanism unique to the integrated energy information-physical framework. After the basic analysis, a threelayer dynamic game model is designed using the strategy of game theory. the model is transformed into a mixed integer linear programming formula, load loss is selected as the vulnerability assessment index, and defense resources are effectively allocated to each component of the integrated energy system. Finally, an IEEE-39-node grid is coupled to an 11-node gas distribution network. Research and analysis, the proposed model and its related methods have been verified.

关键词： Economics Analytical models Power engineering Games Distribution networks Robustness Mixed integer linear programming

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the Trading Strategy of Distributed Energy Storage Participating in Energy and Frequency Modulation Market

The Trading Strategy of Distributed Energy Storage Participa...

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Energy, Electrical and Power engineering (CEEPE), international conference on

作者： Jing Xu Zhixin Fu Chunyi Wang Haoming Liu School of Electrical and Power Engineering Hohai University Nanjing China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

With the deepening reform of the electricity market in China, the study focuses on incentivizing distributed energy storage to provide frequency modulation ancillary services to the power system through market-based mechanisms. the research investigates the transaction decision-making of distributed energy storage in the energy and frequency modulation ancillary services market. An overall trading framework for the energy and frequency modulation ancillary services market is proposed. A bi-level decision model is developed, considering both maximizing the revenue of distributed energy storage and minimizing system costs. A hybrid integer linear programming equilibrium solution algorithm is designed based on duality theory and KKT conditions conversion. the effectiveness of the proposed model is verified through case analysis. the results demonstrate that the revenue from distributed energy storage accounts for more than 50% of the total revenue in the frequency modulation market, and strategic bidding by distributed energy storage can increase its revenue.

关键词： Power engineering Frequency modulation Costs Decision making Integer linear programming Electricity supply industry Safety

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