检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2024, CC BY.

关键词： Ammonia

来源：评论

学校读者我要写书评

暂无评论

Using a Neurosimulator in the Problem of Maximizing the Output Value of a Dynamic Object

Using a Neurosimulator in the Problem of Maximizing the Outp...

引用

Information, Control, and Communication Technologies (ICCT), International Conference on

作者： Dmitry Feofilov Andrey Kozyr Sergey Feofilov Department of Automatic Control Systems Tula State University Tula Russia Digital Control Systems Laboratory Tula State University Tula Russia

ISBN: (数字)9798331517564

ISBN: (纸本)9798331517571

This paper considers the problem of finding quasi-optimal control for an object with an incomplete mathematical model. The main difficulty in practice is obtaining a system of differential equations that describe the dynamics of an object. In the absence of an accurate verified mathematical model of the system, the use of classical methods of optimal control theory is impossible. To solve the problem, it is proposed to use a neurosimulator of the control object and its use in the Chernousko-Krylov numerical iterative method, based on the Pontryagin maximum principle. This approach makes it possible to find quasi-optimal control for "black box" type objects. An example of using a neurosimulator in the problem of maximizing the output phase variable of a dynamic system when setting restrictions on the control signal and control time is given. A comparative analysis of the results obtained for an object specified using differential equations and its neurosimulator was carried out, and the advantages and disadvantages of this approach were identified.

关键词： Training Jacobian matrices Neural networks Closed box Optimal control Differential equations Search problems Mathematical models Object recognition optimization

来源：评论

学校读者我要写书评

暂无评论

Research on Protection optimization of Distribution Network Containing Distributed Power Generation Based on Sparrow Algorithm

Research on Protection Optimization of Distribution Network ...

引用

2021 International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology, MEMAT 2021

作者： He, Yu-Hua Luo, Yi Li, An-Hao Wang, Teng-Fei Cheng, Zhe Peng, Yan-Hua School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology Guilin541004 China

In recent years, the increasing energy crisis and environmental pollution have brought attention to distributed renewable power generation such as photovoltaics and wind power. However, the integration of distributed power sources puts forward higher requirements for the safety, stability and economic operation of the power system. Traditional protection setting methods face greater difficulties in solving the contradiction between action time and protection selectivity under complex conditions including distributed power generation. In many cases, the setting value is not optimal. With the increase in the penetration rate of distributed power sources and the increasingly complex operation of distribution networks, how to achieve the optimal setting of overall protection performance has become a current research hotspot. For this reason, this paper proposes a new scheme based on the Sparrow algorithm to optimize the online protection of distributed power distribution network protection, which is verified by a calculation example. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Multi-Level Evidence Active Learning for Water Surface Object Detection 8

Multi-Level Evidence Active Learning for Water Surface Objec...

引用

8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Zhong, Wanjun Han, Tianyu Hu, Haohao Wang, Yuerong Li, Li Zan, Peng School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China

ISBN: (纸本)9798350335231

Object detection is a rapidly evolving field, but there remains a challenge that the requirement of a significant amount of labeled data. In the recent five years, several active learning strategies have been proposed to achieve improved performance with reduced annotation cost on object detection tasks. However, these approaches have some limitations, including the incomplete utilization of epistemic uncertainty and bounding box information. To address these limitations, this paper proposes a novel active learning method named Multi-Level Evidence Active Learning (MLEAL) for water surface object detection. We propose to utilize Evidence Deep Learning (EDL) for uncertainty estimation and designed an Evidence optimization Head (EOH) to adapt EDL for water surface object detection. To compute the information of an image by considering the multi-level information of the bounding box, we introduce to the utilization of Multi-Level Uncertainty Aggregation (MLUA) to represent the information over an image. The proposed MLEAL is valid on the Water Surface Object Detection Dataset (WSODD). The results demonstrate that the MLEAL outperforms existing active learning methods. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusion Networks

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusi...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Yi Jin Xu Wang Mengxia Hu Hui Yu Yidong Li Tao Wang Songhe Feng Congyan Lang State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China China Automotive Engineering Research Institute Co. Ltd. School of Psychology & Neuroscience University of Glasgow Glasgow UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Task-oriented point cloud sampling methods have attracted considerable attention for their ability to adaptively select important point sets based on downstream tasks, achieving an excellent balance between data simplification and task performance. However, existing task-oriented sampling models, primarily based on single-branch designs, struggle to fully extract features from input point clouds that comprehensively reflect multi-dimensional key information, thus limiting their sampling performance. In this paper, we introduce a dual-branch sampling network, named DBS-NET, which conducts crucial point sampling from both the global and local importance perspectives separately before merging them, thereby preserving multi-dimensional key information of the input data during the sampling process. Qualitative and quantitative experimental results demonstrate the competitive performance of DBS-NET on the classification benchmark task.

关键词： Point cloud compression Limiting Merging Network architecture Benchmark testing Sampling methods Feature extraction Data mining optimization Cybernetics

来源：评论

学校读者我要写书评

暂无评论

Metamorphic relationship generation based on Faure Sequence Initialized Slime Mould Algorithm

Metamorphic relationship generation based on Faure Sequence ...

引用

International Conference on Electronics Information and Emergency Communication

作者： Tingzhi Hong Leiyan Qian Jiayi Fang Tongshuai Wang Zhicheng Zhu Xin Qin Wei Nai Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Pablic Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798350361896

ISBN: (纸本)9798350361902

Metamorphic relationship is an extremely important software testing method in the field of software engineering, and after years of research by scholars, a relatively complete system has been formed. However, the objective function of the metamorphic relationship generation algorithm is essentially a non-differentiable L1 norm, which means that the algorithm can finally be transformed into a non-smooth optimization problem. If traditional gradient dependent methods like gradient descent (GD) or stochastic gradient descent (SGD) are used, the objective function cannot be solved. The coordinate descent (CD) or sub-gradient descent (SubGD) methods usually used to solve the objective function are prone to falling into the trap of local minima, making it difficult to find the global optimal solution. Therefore, it is necessary to find a derivative-free optimization method to solve the objective function of metamorphic relationship generation algorithm. In this paper, a novel slime mold algorithm (SMA), which is called the Faure sequence initialized SMA (FSSMA) has been proposed for global optimization of the objective function. FSSMA can effectively overcome the shortcomings of CD and SubGD methods. Through numerical experiments, the effectiveness and computational efficiency of FSSMA have been demonstrated.

关键词： Software testing Search methods Software algorithms optimization methods Linear programming Computational efficiency Software engineering

来源：评论

学校读者我要写书评

暂无评论

Space-Time Continuum: Continuous Shape and Time State Estimation for Flexible Robots

arXiv

引用

arXiv 2024年

作者： Teetaert, Spencer Lilge, Sven Burgner-Kahrs, Jessica Barfoot, Timothy D. Continuum Robotics Laboratory University of Toronto Canada Autonomous Space Robotics Laboratory University of Toronto Canada

This extended abstract introduces a novel method for continuous stateestimation of continuum robots. We formulate the estimation problem as afactor-graph optimization problem using a novel Gaussian-process prior that isparameterized over both arclength and time. We use this to introduce the firstcontinuous-time-and-space state estimation method for continuum robots. © 2024, CC BY.

关键词： State space methods

来源：评论

学校读者我要写书评

暂无评论

Joint Probabilistic modelling and Sampling from Small Data Via Probabilistic Learning on Manifolds and Decoupled Multi-Probability Density Evolution Method

SSRN

引用

SSRN 2024年

作者： Wan, Zhiqiang Lyu, Meng-Ze Hong, Xu Song, Yupeng Chen, Jianbing Ghanem, Roger School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Disaster Reduction in Civil Engineering Tongji University Shanghai200092 China College of Civil Engineering Tongji University Shanghai200092 China School of Civil Engineering Hefei University of Technology Hefei230009 China College of Civil Engineering Nanjing Tech University Nanjing211816 China Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States

Computational models are of utmost importance in various aspects of structural design and optimization, uncertainty quantification, risk assessment, and other engineering fields. Among numerous critical issues for model-based uncertainty quantification, the issue of joint probabilistic modelling of dependent model parameters under the condition of small data is investigated in this work. In contrast to most viewpoints, which see model parameters by observation or experiment as mere data, we regard them as stochastic responses that are realized through implicit (and possibly complicated) physical processes with respect to underlying random variables. Based on this thought, a novel approach for estimating the joint probabilistic model of n-dimensional dependent model parameters is proposed in two stages: Firstly, the probabilistic learning on manifolds (PLoM) is adopted to generate ample "virtual" realizations of model parameters that are statistically consistent with the original small data. This aims to construct an approximately dependent probability structure of model parameters;Secondly, the decoupled multi-probability density evolution method (PDEM) is employed to calculate the joint probabilistic model from a perspective of uncertainty propagation, where the original small data are equipped with assigned probabilities that are calculated from the dependent probability structure via PLoM. Moreover, taking the advantages of the proposed method, a low-dimensional data storing and a novel acceptance-rejection sampling technique are proposed, which is particularly convenient for the case that n > 3. A benchmark case is studied to illustrate and verify the proposed method, which is found to be robust to complex engineering data. Three applications, including double doughnut configuration-type's data, mechanical parameters of concrete, and location and time parameters of typhoon genesis, are presented to demonstrate the new capacities of the proposed method. © 2024, T

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Joint Placement and Beamforming Design in UAV Enabled Multi-Stage ISAC System

引用

IEEE Transactions on Communications 2025年

作者： Xu, Linlin Zhu, Qi Xia, Wenchao Wang, Zhongbin Quek, Tony Q. S. Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Wireless Computing Research Institute Shenzhen518055 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore Yonsei University Yonsei Frontier Laboratory Seoul03722 Korea Republic of

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms. © 1972-2012 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Traffic Signal optimization by Integrating Reinforcement Learning and Digital Twins

Traffic Signal Optimization by Integrating Reinforcement Lea...

引用

Smart World Congress (SWC), IEEE

作者： Vijayalakshmi K Kumarasamy Abhilasha Jairam Saroj Yu Liang Dalei Wu Michael P. Hunter Angshuman Guin Mina Sartipi Department of Computer Science and Engineering University of Tennessee at Chattanooga USA Applied Research for Mobility Systems Oak Ridge National Laboratory USA School of Civil and Environmental Engineering Georgia Institute of Technology USA

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：