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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291-300 订阅

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Wind Power Investment Decision-Making Optimization Model Based on Real Option Method 6

Wind Power Investment Decision-Making Optimization Model Bas...

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6th IEEE conference on Energy Internet and Energy System Integration, EI2 2022

作者： Nan, Li Fang, Li Guihong, Zhang Xue, Ma Jie, Sun Zhejiang University College of Electrical Engineering Zhejiang310027 China Green Energy Development China Qinghai Xining810008 China State Grid Qinghai Economic Research Institute Qinghai Qinghai Xining810008 China North China Electric Power University China Changping School of Economics and Management Beijing102206 China

ISBN: (纸本)9798350347159

At present, China's renewable energy power policy is in the transition stage from fixed on grid electricity price to quota system. Considering the length of the parallel period of the two policies and the relative market share of the two policies, a wind power investment decision model under the uncertainty of green certificate price is established based on the real option principle, and the optimal investment opportunity of wind power generators is solved by using the dynamic programming method. the parameters are calibrated according to the actual situation of wind power development in China, the key factors influencing investment decisions in the policy and their action mechanisms are analyzed, and the model results are empirically discussed in combination with the average price of wind power green certificates in the trial stage of green certificate trading. the results show that the longer parallel period of fixed feed in tariff and quota system and the higher proportion of fixed feed in tariff policy are conducive to reducing the investment threshold, while the increase of green certificate price growth rate and volatility will delay investors' investment decisions. While effectively encouraging investors to invest in wind power projects, the market share of the quota system policy should not be higher than 50% at the current stage in order to achieve the goal of full parity Internet access and the formal implementation of the quota system policy in the early stage of the "fourteenth five year plan"for wind power. © 2022 IEEE.

关键词： Wind power

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Research on a Novel Reactive Power Emergency Control Strategy for HVDC Receiving System 6

Research on a Novel Reactive Power Emergency Control Strateg...

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6th IEEE conference on Energy Internet and Energy System Integration, EI2 2022

作者： Kou, Longze Gu, Huaiguang Gao, Yang Lin, Tao Fu, Xiyue Du, Hui Li, Qingyan State Key Laboratory of Advanced Power Transmission Technology State Grid Smart Grid Research Institute Co.Ltd. Beijing China Wuhan University School of Electrical Engineering and Automation Wuhan China

ISBN: (纸本)9798350347159

In the renewable power system, the renewable energy fed by the high voltage direct current transmission system (HVDC) replaces the conventional unit output near the landing point of the HVDC receiving end power grid, resulting in a relatively tense state of dynamic reactive power near the landing point of the HVDC. When induction motors are the main loads of HVDC receiving power grid, after the fault occurs in AC system, the load bus voltage may continue to decline, and the problem of voltage stability, especially the transient voltage stability, is becoming increasingly prominent. In order to recover the voltage quickly, this paper proposes a method of using HVDC converter station as emergency reactive power supply to realize the emergency control of transient voltage stability in HVDC receiving power grid by temporarily reducing DC current. Aiming at the quantitative calculation of DC current, this paper establishes a nonlinear optimization model with the objective of minimizing the energy transmission reduction of HVDC transmission, and uses the global orthogonal configuration method to convert the differential equations in the objective function and constraints into algebraic equations, so as to convert the optimization model into nonlinear programming problem, and obtain the optimal DC current control scheme, namely, reactive power emergency control strategy, which greatly reduces the difficulty of solving the optimization model. Finally, a modified IEEE-14 node benchmark is used to verify the effectiveness of the method. © 2022 IEEE.

关键词： Reactive power

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Energy-Efficient Multi-Vehicle Edge Networks: A Joint Optimization Algorithm for Task Splitting Offloading and Resource Allocation 7

Energy-Efficient Multi-Vehicle Edge Networks: A Joint Optimi...

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7th IEEE international conference on Smart Internet of things, SmartIoT 2023

作者： Cao, Dun Gu, Ning Sherratt, Robert Simon Zhang, Dengyong College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410004 China School of Biomedical Engineering The University of Reading RG6 6AY United Kingdom

ISBN: (纸本)9798350316575

In Internet of Vehicles (IoV), considering the different task volumes, the changing trajectories and the limited resources of the RSU server, it is a major challenge to effectively coordinate task offloading between multiple vehicles and reduce energy consumption. therefore, to solve the problem of numerous intensive tasks in a multi-vehicle RSU system, we propose an vehicular edge computing (VEC) optimization scheme for joint the computation offloading, unequal task splitting and resource allocation. the goal is to minimize energy consumption with guaranteed task completion latency and limited RSU computing resources. the range of movement of each task vehicle and the optimal vehicle computational resource allocation are considered as well. As the joint optimization scheme is mixed-integer nonlinear optimization problem (MINLP), it is decomposed into two sub-problems. Firstly, given the initial CPU frequency allocation for each vehicle task, the collaborative offloading strategy is determined using game theory. Secondly, taking into account the constraints of computing resources, a sequential quadratic programming (SQP) algorithm is used to provide optimal strategies for unequal task splitting and CPU frequency allocation. Simulation results show that the proposed algorithm can reduce the system energy consumption by about 30% compared with the Game theory-based scheme under the same latency constraint. © 2023 IEEE.

关键词： Game theory

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Improving Chinese Named Entity Recognition Based on Lexical Information Adjustment 6

Improving Chinese Named Entity Recognition Based on Lexical ...

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6th international conference on Computer science and artificial Intelligence, CSAI 2022

作者： Luo, Jinshang Hou, Mengshu Information Center University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781450397773

Lexical enhancement has been proven effective for Chinese Named Entity Recognition (NER), allowing for the use of word boundary information and the reduction of segmentation errors. Most present methods incorporate the lexical information by lattices but neglect the impact of candidate lexicon conflicts. the conflicts may misguide the model to make different predictions. In the work, a novel Long Short-Term Memory (LSTM) model based on the lexical information adjustment strategy (LIA-LSTM) is proposed. Firstly, all potential words are categorized into corresponding word sets. To lessen the influence of lexicon conflicts, the lexical features are introduced for the character sequence via elaborate weight adjustment. then the LSTM and self-Attention mechanism are applied to improve contextual awareness. Experiments on benchmark datasets demonstrate that LIA-LSTM outperforms the state-of-the-art methods compared. LIA-LSTM improves F1 score by 1.4% over baseline Lattice-LSTM on the MSRA dataset. © 2022 ACM.

关键词： Long short-term memory

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Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains 28

Large Neighborhood Search for Robust Solutions for Constrain...

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28th international conference on Principles and Practice of Constraint programming, CP 2022

作者： López, Jheisson Arbelaez, Alejandro Climent, Laura University College Cork School of Computer Science Ireland SFI Centre for Research Training in Artificial Intelligence Cork Ireland Department of Computer Engineering Autonomous University of Madrid Spain

ISBN: (纸本)9783959772402

Often, real-world Constraint Satisfaction Problems (CSPs) are subject to uncertainty/dynamism not known in advance. Some techniques in the literature offer robust solutions for CSPs. Here, we analyze a previous exact/complete approach from the state-of-the-art that focuses on CSPs with ordered domains and dynamic bounds. However, this approach has low performance in large-scale CSPs. For this reason, in this paper, we present an inexact/incomplete approach that is faster at finding robust solutions for large-scale CSPs. It is useful when the computation time available for finding a solution is limited and/or in situations where a new one must be re-computed online because the dynamism invalidated the original one. Specifically, we present a Large Neighbourhood Search (LNS) algorithm combined with Constraint programming (CP) and Branch-and-bound (B&B) that searches for robust solutions. We also present a robust-value selection heuristic that guides the search toward more promising branches. We evaluate our approach with large-scale CSPs instances, including the case study of scheduling problems. the evaluation shows a considerable improvement in the robustness of the solutions achieved by our algorithm for large-scale CSPs. © Jheisson López, Alejandro Arbelaez, and Laura Climent.

关键词： Constraint satisfaction problems

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international Prospects and Trends of artificial Intelligence Education: A Content Analysis of Top-level AI Curriculum across Countries 6

International Prospects and Trends of Artificial Intelligenc...

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6th international conference on Digital Technology in Education, ICDTE 2022

作者： Zhou, Yujun Zhan, Zehui Liu, Lu Wan, Jiayi Liu, Simai Zou, Xuanxuan School of Information Technology in Education South China Normal University Guangzhou China School of Education South China Normal University Guangzhou China

ISBN: (纸本)9781450398091

this study intends to investigate the present situation of AI curriculum offered for grades K-12. We screened 11 representative countries and areas from six continents and assessed the content of their top K-12 AI courses in terms of teaching content and teaching implementation in order to comprehend the current state of K-12 AI courses in diverse nations. Provide some ideas and suggestions for the development of AI courses for students in grades K-12. (1) Countries may choose AI applications, AI influences in various aspects, AI ethics, machine learning, data, classification, reasoning, Identify, and other content to establish independent AI teaching content standards;or choose programming as the core teaching content/starting point;or integrate programming, data, AI, and other content related to improving students’ computational thinking into computer/science/technology courses. (2) Project-based learning is still the primary way of instruction, along with a range of other approaches. there are four categories of available instructional resources, and there is an abundance of them. Most countries emphasize the evaluation of students’ abilities and the results achieved in the learning process. © 2022 Copyright held by the owner/author(s).

关键词： Curricula

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Real-Time Innovation in Future Generation Security for Data Privacy Intrusion System in Machine Learning Approach

Real-Time Innovation in Future Generation Security for Data ...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Hansaraj Shalikram Wankhede R. Venkatesh Sumit Pundr Ravikiran Madala Kamunuri Ganapathi Babu Badepally Mallaiah Department of artificial Intelligence G H Raisoni college of Engineering Nahpur Maharashtra India Department of Computer Science and Engineering Karpagam College of Engineering Coimbatore Tamilnadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India University of Cumberlands Durham North Carolina US. Department of Computer Science and Engineering St. Martin's Engineering College Secunderabad Telangana India Department of Information Technology CVR College of Engineering Hyderabad Telangana India

programming called an intrusion discovery system watches over a solitary PC or an organization of PCs for pernicious movement that intends to take or edit data or change network conventions. Most of intrusion recognition methods utilized today are unequipped for guarding against the dynamic and perplexing nature of cyber-attacks on PC organizations. Albeit successful versatile procedures, for example, unique machine learning calculations, may prompt more noteworthy discovery rates, diminished deception rates, and suitable calculation and transmission costs. Data mining might be utilized to make miniature data streams, classification, and example mining rather frequently. the real-time intrusion identification system and related intrusion counteraction system are introduced in this work utilizing machine learning. To prepare the model, a convolutional brain organization (CNN) is utilized. A 5G data network assessment of the innovation was directed. the improvement of programming characterized networks is thought about by brilliant intrusion location systems (IDS). It utilizes man-made reasoning based demonstrating. Convolutional brain network-based programming parts empower the system to distinguish intrusions that were not recently recognized. Intrusion Prevention System “IPS” is to stop malicious traffic. Based on the evaluation, this solution provides more buffers than comparable alternatives, facilitating real-time management in 5G settings.

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A new composite method of modeling bicycle traffic using convolutional neural networks and genetic programming 6

A new composite method of modeling bicycle traffic using con...

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6th international conference on Smart and Sustainable Technologies, SpliTech 2021

作者： Babic, Matej Ster, Branko Povh, Janez Rodrigues, Joel J.P.C. Faculty of Information Studies Novo mesto Slovenia Faculty of Computer and Information Science University of Ljubljana Slovenia Faculty of Mechanical Engineering University of Ljubljana Slovenia Development Center Novo mesto Slovenia PI Teresina Brazil Instituto de Telecomunicações Portugal

ISBN: (纸本)9789532901122

this study proposes a new composite method of modeling bicycle traffic in the town of Novo mesto, Slovenia, using Convolutional neural networks and Genetic programming. Public passenger transport (PPT) is important for every municipality, as the current transport system faces well-known problems such as congestion, environmental impact, lack of parking areas, increased safety risks and high energy consumption. the wider Novo mesto region, with about 30, 000 inhabitants, is an important industrial center and is heavily dependent on urban traffic. the aim of the research is to analyze and model bicycle rentals. Convolutional neural networks and genetic programming are used to predict bicycle traffic over 35 weeks. © 2021 University of Split, FESB.

关键词： Genetic programming

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End to End Framework for CNN Acceleration on FPGAs with Dynamic Algorithm Mapping 2022

End to End Framework for CNN Acceleration on FPGAs with Dyna...

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14th international conference on Contemporary Computing, IC3 2022

作者： Liu, Haomei Meng, Yuan Kuppannagari, Sanmukh Rao Prasanna, Viktor K. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

ISBN: (纸本)9781450396752

As CNNs are becoming more diverse with respect to per-layer computation characteristics, per-layer strategy selection and fine-grained tuning are required to achieve low end-to-end latency. DYNAMAP is a framework for efficiently selecting algorithms for different layers and re-using a unified accelerator. However, the end-to-end deployment of DYNAMAP faces some challenges in terms of productivity and portability. In this work, we develop an API for automatically extracting the ONNX graph from high-level programming libraries such as Pytorch and Tensorflow. We further integrate this with the front-end of DYNAMAP to increase its portability. We then define an HLS template of the accelerator supporting three parallel algorithm choices for convolution operations to increase the productivity of generating hardware. Using three state-of-the-art CNNs, we demonstrate that our framework is able to automatically deploy CNN models to achieve minimum latency which incurs very low overhead in the deployment process. © 2022 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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Cross-Modal Relation and Sketch Prototype Learning for Zero-Shot Sketch-Based Image Retrieval 6

Cross-Modal Relation and Sketch Prototype Learning for Zero-...

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6th international conference on Computer science and artificial Intelligence, CSAI 2022

作者： Song, Yuanping Yu, Yanwei Tang, Hao Guo, Junyuan Wang, Yibo School of Software Engineering University of Science and Technology of China China School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China

ISBN: (纸本)9781450397773

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is an innovative cross-modal task that utilizes a sketch to retrieve corresponding images in the zero-shot learning scene. At present, most algorithms treat ZS-SBIR as a typical image classification problem, using image-level features with triplet or cross entropy loss to achieve retrieval, while ignore the correspondence between sketches and images on local features. therefore, we propose a new Local Feature Contrastive Network (LFCN) for ZS-SBIR from the perspective of contrastive learning. More specifically, a local feature contrastive method is proposed to establish the cross-modal relationships between images and sketches with creatively applying transformers to extract similarity representation from sketch-image pairs to narrow the domain gap. Furthermore, a feature prototype memory bank is designed to learn sketch prototypes to address the in-class diversity problem in sketch domain. A large number of experiments show that our method notably superior to the state-of-the-art algorithms in both TU-Berlin and Sketchy datasets. © 2022 ACM.

关键词： Zero-shot learning

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