检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,614 篇 会议
27 篇 期刊文献
2 册 图书

馆藏范围

1,643 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,137 篇 工学
- 658 篇 计算机科学与技术...
- 453 篇 电气工程
- 404 篇 控制科学与工程
- 323 篇 软件工程
- 243 篇 信息与通信工程
- 141 篇 动力工程及工程热...
- 122 篇 电子科学与技术（可...
- 106 篇 机械工程
- 100 篇 石油与天然气工程
- 80 篇 环境科学与工程（可...
- 76 篇 交通运输工程
- 49 篇 材料科学与工程（可...
- 49 篇 安全科学与工程
- 43 篇 仪器科学与技术
- 43 篇 生物工程
- 28 篇 土木工程
- 26 篇 航空宇航科学与技...
- 25 篇 力学（可授工学、理...
- 24 篇 网络空间安全
- 21 篇 水利工程
407 篇 理学
- 301 篇 数学
- 72 篇 系统科学
- 57 篇 物理学
- 56 篇 统计学（可授理学、...
- 49 篇 生物学
218 篇 管理学
- 196 篇 管理科学与工程(可...
- 72 篇 工商管理
- 29 篇 图书情报与档案管...
29 篇 经济学
- 29 篇 应用经济学
13 篇 医学
12 篇 法学
12 篇 农学
7 篇 军事学
1 篇 教育学

主题

134 篇 optimization
39 篇 particle swarm o...
35 篇 neural networks
35 篇 genetic algorith...
31 篇 genetic algorith...
29 篇 wireless sensor ...
26 篇 neural network
25 篇 quality of servi...
24 篇 routing
22 篇 reinforcement le...
22 篇 heuristic algori...
21 篇 model predictive...
21 篇 simulation
21 篇 control systems
21 篇 voltage control
21 篇 mathematical mod...
20 篇 reactive power
19 篇 ant colony optim...
19 篇 resource managem...
18 篇 controllers

机构

6 篇 china electric p...
6 篇 beijing jiaotong...
5 篇 school of new en...
4 篇 school of electr...
4 篇 faculty of trans...
4 篇 changsha univers...
4 篇 state grid corpo...
4 篇 college of elect...
4 篇 power dispatchin...
4 篇 school of manage...
3 篇 state key labora...
3 篇 shanghai jiao to...
3 篇 indian inst tech...
3 篇 global energy in...
3 篇 school of electr...
3 篇 china elect powe...
3 篇 key lab measurem...
3 篇 shanghai jiao to...
3 篇 guangzhou power ...
3 篇 institute of sha...

作者

5 篇 zhang yan
5 篇 wang jun
4 篇 wang jian
4 篇 wang wei
4 篇 yang xu
4 篇 liu yu
4 篇 chen wei
4 篇 zhang yong
3 篇 zhou jie
3 篇 choudhury subhas...
3 篇 yu yixiao
3 篇 roy provas kumar
3 篇 zhang zhang
3 篇 sha linxiu
3 篇 lin zhichao
3 篇 li hengjie
3 篇 peng chen
3 篇 wang li
3 篇 yong zhang
3 篇 zhao liang

语言

1,556 篇 英文
82 篇 其他
7 篇 中文

检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1643 条记录，以下是201-210 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

TLBO Based Optimized Planning Framework of Distribution network considering Electric Vehicle Charging Station and Solar Photovoltaic System 3

TLBO Based Optimized Planning Framework of Distribution Netw...

引用

3rd IEEE International conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Tripathy, Sasmita Nandi, Sharmistha Ghatak, Sreeparna Roy Acharjee, Parimal Sinha, Pampa Kiit University Bbsr School of Electrical Engineering Odisha India Nit Durgapur School of Electrical Engineering West Bengal Durgapur India

ISBN: (纸本)9798350304732

Electric vehicles (EVs) are significantly increasing the burden on the electricity sector, resulting in power loss, a poor voltage profile, and voltage stability difficulties. To address this, a solid planning framework for efficient deployment of EV charging stations and solar energy resources in the distribution network is being established in the present work. A practical probabilistic model of EV is developed using Probability Distribution Function (PDF) considering uncertainty parameters *** distance, trip end time, types of battery and battery capacity extracted from real National Household Travel Survey (NHTS) datasheet. The Teaching Learning Based optimization (TLBO) algorithm is used to solve the problem of optimum joint deployment of EV charging station and solar energy, minimizing power loss reduction index (PLRI) and voltage deviation index(VDI) while considering system constraints. Further the voltage stability of the system is also analyzed with the incorporation of devices in the network. In this paper, the optimization problem is solved using MATLAB (version R2018a) software, and the IEEE 69 bus system is employed as a test system. The simulation outcomes show that optimal deployment of EV charging stations with photovoltaic (PV) units improves voltage profile, the penetration level of EV charging station and also voltage stability index (VSI) of the system, but reduces total real loss. The technique is compared with three different algorithms, including Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Differential Evolution (DE), in terms of convergence characteristics. © 2023 IEEE.

关键词： Charging Station Electric Vehicles Photo Voltaic TLBO Uncertainty VSI

来源：评论

学校读者我要写书评

暂无评论

Material-Driven Grasp optimization with GGCNN-CA for Supermarket Robotic Automation 24

Material-Driven Grasp Optimization with GGCNN-CA for Superma...

引用

3rd International Symposium on control Engineering and Robotics, ISCER 2024

作者： Zheng, Yi Xie, Ruihao He, Qiang School of Artificial Intelligence Jianghan University Wuhan60002790 China

ISBN: (纸本)9798400709951

Grasping tasks executed by robotic arms represent a significant research avenue within the domain of robotic technology. The process of restocking in supermarkets incurs substantial human labor costs, a burden that can be alleviated through automation with the help of collaborative robots. This paper addresses the practical limitations of deep learning-based grasp pose estimation algorithms by proposing a novel pose estimation technique tailored for closed-loop planar grasping tasks. By integrating an attention module into the foundation of a grasp generation convolutional neural network, the accuracy of grasp predictions rendered by the network is enhanced. Furthermore, this paper introduces a methodology for adjusting grasp positions based on the hardness and material properties of objects. Through multi-dimensional evaluation of objects, the proposed technique refines the grasp pose further based on network predictions. Empirical evidence underscores the effectiveness of this approach for object grasping tasks within the supermarket restocking scenario. © 2024 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

On-line Condition Monitoring of Vibration Sensor Based on Wavelet and Particle Swarm optimization-back Propagation Neural network 3

On-line Condition Monitoring of Vibration Sensor Based on Wa...

引用

3rd International conference on Electrical Engineering and control Science, IC2ECS 2023

作者： Huang, Junchang Zeng, Feitong Liu, Ziyuan Liu, Jing Hu, Haoliang China Electric Power Research Institute Wuhan430074 China Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350382426

The vibration sensor can monitor the vibration of transformers, motors, and other equipment in the power grid. Its measurement accuracy is related to the safe and stable operation of the equipment, so it is necessary to monitor the vibration sensor online. When the vibration sensor fails, the acceleration output value after ADC conversion will also change accordingly. A fault diagnosis method based on wavelet decomposition and PSO-BP neural network is proposed to realize the online monitoring of vibration sensors. Firstly, the wavelet decomposition sensor output data is used to extract the feature quantity and judge whether the fault occurs with the normal state. The BP neural network is used to classify the normal operation of the sensor and various typical faults. At the same time, the PSO algorithm is introduced to optimize the neural network parameters, which is better than the original fault diagnosis method, and the fault diagnosis of the vibration sensor is realized. © 2023 IEEE.

关键词： BP fault diagnosis PSO wavelet decomposition

来源：评论

学校读者我要写书评

暂无评论

Power System Topology optimization Method Based on Maximum Connectivity Subgraph 3

Power System Topology Optimization Method Based on Maximum C...

引用

3rd International conference on Intelligent Power and Systems, ICIPS 2023

作者： Lu, Jiangang Guo, Wenxin Tan, Huijuan Dai, Yue Wang, Chen Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Guangdong Guangzhou510000 China

ISBN: (纸本)9798350328691

Power systems typically consist of a large number of nodes and edges, with complex topologies. It is difficult to optimize the global topology of large-scale power systems. Therefore, a power system topology optimization method based on maximum connectivity subgraph is proposed. Analyze the relationship between the actual coverage, coverage area, and maximum connectivity subgraph degree of the node. Based on this, a series coverage path is proposed, so that the power system topology can meet the maximum connectivity subgraph coverage requirements of all points. Set the weightless map of the power system, calculate the redundancy path of the power system, establish the power system optimization function and formulate relevant constraints, and construct the topology optimization model. The experimental results show that under the application of the proposed method, the overlapping coverage between power system network nodes is reduced and the distribution is more uniform. The time required to calculate power system connectivity is shorter than the other two methods, and its connectivity is higher than the other two methods. ©2023 IEEE.

关键词： Electric power distribution

来源：评论

学校读者我要写书评

暂无评论

Design and Application of Intelligent Search Framework for Power Grid control Cloud 3

Design and Application of Intelligent Search Framework for P...

引用

3rd International conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Zhao, Kun Lou, Tianyue Liu, Huiyong Li, Xiaoting Liu, Yongda Liang, Lijie State Grid Jibei Electric Power Company Limited Beijing China Beijing Kedong Power Control System Co. Ltd. Ltd. Dispatch Information Business Department Beijing China State Grid Xinyuan Group CO. LTD. Audit department Beijing China

ISBN: (纸本)9798331528386

As the electricity grid industry continues to grow rapidly, more demands are being placed on the dispatching system’s intelligence and digitization. Under these circumstances, this paper deeply integrates intelligent Internet of Things technology, and systematically investigates the cloud platform’s intelligent search structure for power grid control in view of the data density and business complexity of power grid control cloud platform. This framework not only integrates the professional vocabulary modeling and algorithm for term segmentation in the power domain using the power feature, effectively improving the professionalism and accuracy of the search, but also innovatively introduces the multi-factor relevance sorting algorithm to achieve efficient optimization of search results. Furthermore, this paper uses the data processing and analysis capabilities of the intelligent Internet of Things to construct a power knowledge graph association model, transforming the scattered power grid data into a structured knowledge network, greatly enhancing the semantic understanding and intelligent reasoning capabilities of the search system. Considering the essential technologies mentioned above, the paper successfully constructed a power grid intelligent search application for the control cloud, which can achieve comprehensive coverage and intelligent retrieval of power grid models, operating status, historical events, technical information and service resources, providing unprecedented data insights and decision support capabilities for dispatching and operation business. © 2024 IEEE.

关键词： Correlation sequencing Intelligent search Internet of Things Power field thesaurus Power knowledge map

来源：评论

学校读者我要写书评

暂无评论

Research on optimization Algorithm and Decision System for Autonomous Operation of New Power System 3

Research on Optimization Algorithm and Decision System for A...

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Gao, Jiameng Chen, Ziwei Xiang, Li China Electric Power Research Institute State Grid Corporation of China Beijing100192 China Northeast Electric Power University School Jilin Jilin132000 China

ISBN: (纸本)9798331519032

This paper focuses on the autonomous operation optimization algorithm and decision-making system of new power system (NPS), and proposes an optimization algorithm framework based on reinforcement learning (RL) to improve the operation efficiency and security of power system. In this paper, the framework of autonomous operation optimization algorithm based on RL is constructed, the power system is abstracted as markov decision processes (MDP), and an agent is constructed through deep neural network (DNN) to learn the optimal strategy. The algorithm framework can sense the state of power system in real time, realize the optimal control decision through the learning of agents, and comprehensively consider economy, safety and new energy consumption ability. In the simulation experiment, this paper uses MATLAB/Simulink platform to build NPS model, which verifies the remarkable advantages of RL algorithm in reducing system operation cost and improving decision efficiency. Compared with traditional linear programming and dynamic programming algorithms, RL algorithm performs better in cost reduction rate and decision-making time, with the cost reduction rate as high as 15.24% and the distribution of decision-making time more centralized and stable. In addition, this paper also discusses the evaluation and analysis method of key technologies based on patent indicators, identifies the "stuck-neck" technology in the power industry, and designs a decision support system (DSS) architecture for NPS. DSS architecture includes four core modules: data acquisition and processing, RL algorithm, decision analysis and user interaction. Through modular design, information can be transmitted and shared efficiently, which provides a strong guarantee for intelligent optimization and decision support of power system. The research results of this paper provide new ideas and technical support for the autonomous operation optimization of NPS, which is of great significance to promoting energy t

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Automated Tomato Leaf Disease Identification via Transfer Learning Approach on Convolutional Neural network 3

Automated Tomato Leaf Disease Identification via Transfer Le...

引用

3rd IEEE International conference on Intelligent Techniques in control, optimization and Signal Processing, INCOS 2024

作者： Vini, S. Ledbin Rathika, P. Psn College of Engineering and Technology Ece Department Tirunelveli India

ISBN: (纸本)9798350361186

Tomatoes are the most nutrient-dense, most cultivated, and most consumed vegetables in the world, and they are attractive due to their therapeutic properties. On the other hand, tomato crops are susceptible to numerous diseases that can lower or completely destroy tomato production;hence, the fast and precise diagnosis of tomato infections plays a vital role in crop protection. As a result, researchers have created numerous deep learning models for the automated classification of tomato leaf diseases. Transfer learning has made it possible to apply solutions from one area to another, enabling the development of stronger and more effective solutions for the diagnosis of tomato leaf diseases. This article utilized transfer learning on different forms of standard CNN architecture, like DarkNet-19, ShuffleNet, and ResNet-18, to diagnose tomato plant diseases from its leaf images. Also, this article assesses the model's performance with stochastic gradient descent (SGD), RMSProp, and adaptive moment estimation (Adam). The outcome of the experiment shows how successful the model is classifying tomato leaf diseases automatically with the transfer learning technique. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

LSTM-based MPC Method for Stochastic Energy Management of Microgrid 3

LSTM-based MPC Method for Stochastic Energy Management of Mi...

引用

3rd International conference on New Energy and Power Engineering, ICNEPE 2023

作者： An, Na Liu, Tingxiang Chen, Ke Huang, Haojiang Yang, Libin Li, Zhengxi Gao, Jin Zhu, Jianquan Clean Energy Development Research Institute of State Grid Qinghai Electric Power Company Economic and Technological Research Institute of State Grid Qinghai Electric Power Company Xining China South China University of Technology Guangzhou China

ISBN: (纸本)9798350393224

The intermittency and randomness of renewable energy sources (RESs) bring great challenge to the stochastic energy management (SEM) problem of microgrids (MGs). To solve this problem, we embed long short-term memory (LSTM) networks into model predictive control (MPC). The performance of the MPC method relies on the efficiency of real-time prediction and rolling optimization. Therefore, during the real-time prediction stage, a prediction network is constructed to improve the accuracy of prediction for random variables. Furthermore, during the rolling optimization stage, since commercial solvers achieve better performance when solving nonlinear programming (NLP) problems compared to mixed-integer nonlinear programming (MINLP) problems, a policy network is constructed to convert the MINLP into an NLP problem, reducing the complexity of the problem. In this paper, a LSTM-based MPC method is proposed for SEM of MG. Compared to traditional MPC methods, the proposed method demonstrates superior computational efficiency in addressing the SEM problem. Finally, case studies on the given test system fully verifies the accuracy and effectiveness of the proposed method. © 2023 IEEE.

关键词： long short-term memory network microgrid MINL model predictive control stochastic energy management

来源：评论

学校读者我要写书评

暂无评论

Reinventing Grocery Shopping with Reinforcement Learning 3

Reinventing Grocery Shopping with Reinforcement Learning

引用

3rd International conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Choppa, Anvitha Kesavarshini, Appikatla Bhavishya, Mallampati Rao, Yamarthi Narasimha School of Computer Science and Engineering VIT-AP University Amaravathi Guntur522237 India

ISBN: (纸本)9798331532420

Grocery shopping stands as a quintessential task in everyday life, yet it often involves complex decision-making processes influenced by factors such as individual preferences, budget constraints, and time limitations. In response to the intricacies of this routine chore, the integration of Artificial Intelligence (AI), particularly Reinforcement Learning (RL), has emerged as a promising avenue for optimization. RL empowers autonomous agents to navigate the grocery shopping landscape by learning optimal strategies through iterative interactions with the environment. By employing RL algorithms, these agents can adapt and refine their decision-making processes over time, aiming to maximize cumulative rewards such as cost savings, time efficiency, and customer satisfaction. Such personalized and dynamic approaches hold significant potential for revolutionizing traditional grocery shopping experiences, offering tailored recommendations, mitigating decision fatigue, and ultimately streamlining the overall shopping journey for consumers. In this context, the application of RL techniques serves as a powerful tool to optimize the process of grocery shopping. By formulating the problem within a framework of states, actions, transition probabilities, and rewards, RL algorithms enable an autonomous agent to learn an optimal strategy for navigating through a network of shops to fulfill a given shopping list efficiently. In this work, through the implementation of value iteration, Q-learning, and representation policy iteration methods, the agent can learn and refine its decision-making policy, ultimately simplifying and enhancing the grocery shopping experience for consumers. © 2024 IEEE.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Photovoltaic Short-term Output Forecasting 3

The Bayesian CNN-LSTM Mixed Hybrid Algorithm Model of the Ph...

引用

3rd International conference on New Energy and Power Engineering, ICNEPE 2023

作者： Dong, Enchong Guo, Yuxi North China Electric Power University School of Control and Computer Engineering Beijing China Shenzhen College of International Education Shenzhen China

ISBN: (纸本)9798350393224

Due to the volatility, randomness, and intermittency of photovoltaic power generation, it is difficult to accurately forecast its output. This paper proposes a Bayesian-optimized CNN-LSTM mixed neural model for a short-term photovoltaic output prediction model. First of all, the Xinjiang photovoltaic power station dataset is selected and preprocessed in this study. Secondly, the CNN method is applied for data feature extraction. And then the Bayesian optimization algorithm is used to analyze the LSTM super parameter optimization. Finally, the data, after undergoing feature extraction, are input into the Bayes theorem-LSTM model for forecast generation. The results show that the proposed algorithm has realized the accurate prediction of photovoltaic power, getting better than most other single benchmark models. These findings not only highlight the improved efficacy of the proposed algorithm but also affirm its potential applicability and value for practical implementation. © 2023 IEEE.

关键词： Bayesian algorithm Convolutional Neural network algorithm Long Short-Term Memory algorithm Photovoltaic power output prediction

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共165页 << < 17 18 19 20 21 22 23 24 25 26 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：