检索结果-内蒙古大学图书馆

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： David Carrasco-González Raúl Sarrias-Mena Pablo Horrillo-Quintero Francisco Llorens Iborra Luis M. Fernández-Ramírez Department of Electrical Engineering SURET Research Group University of Cadiz (UCA) ETSI Algeciras Spain Dept. of Engineering in Automation Electronics and Computer Arch. and Networks SURET Reseach Group UCA ETSI Algeciras Spain

Microgrid clusters are becoming increasingly popular owing to their many benefits and their ability to meet human needs in terms of energy flexibility, security, and integration of renewable sources. They are composed of interconnected microgrids with a variety of power generation sources, loads, and storage devices. Microgrid clusters can also combine DC and AC technologies, even within a single microgrid. In this work, a new energy management system is developed for a microgrid cluster consisting of two microgrids connected to the main grid. The proposed energy management system, acting as the centralized control layer in the microgrid cluster, is based on a fuzzy-logic algorithm. The microgrid cluster consists of an AC microgrid integrating a photovoltaic generator, a battery bank and AC loads; and a DC microgrid composed of a wind turbine, electrolyzer, ultracapacitor, fuel cell and DC loads. The microgrid cluster is tested under different working conditions, such as variable wind speed and solar irradiance, and connection and disconnection of the loads. Finally, the energy management system implemented in the microgrid cluster exhibits a correct behaviour under different conditions, according to the results obtained.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

引用

International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Designing a Distributed Multi-agent System for Compiler Optimization

Designing a Distributed Multi-agent System for Compiler Opti...

引用

2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Sahin, Cagri Butun, Ismail University of Delaware Electrical and Computer Engineering Computer Systems and Networking NewarkDE United States Gazi University Computer Engineering Software Research Group Ankara Turkey Chalmers University of Technology Computer Science and Engineering Network and Systems Division Gothenburg Sweden

ISBN: (纸本)9781665404174

This paper explores the run time performance improvements using different GCC optimization flags in program compilation. As multi-core microprocessor systems replacing legacy single-core ones, tremendous effort is needed to address to optimize the associated compilers for newly designed architectures in order to suit them for running parallel programming on multiple cores. Therefore, the aim of this paper is to address this challenge by designing an optimum distributed multi-agent system to perform compiler optimization. A multi-agent framework is adopted to utilize random and genetic algorithm-based search algorithm to find the best GCC optimization flags for a given program. The framework is highly scalable and can be extended with distributed system concept to perform code compilation in parallel to find the best-optimized code sequence in a short amount of time. The initial performance results have promising indicators which clearly show that the performance improvement is achieved. © 2021 IEEE.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

MOKD: cross-domain finetuning for few-shot classification via maximizing optimized kernel dependence 24

MOKD: cross-domain finetuning for few-shot classification vi...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Hongduan Tian Feng Liu Tongliang Liu Bo Du Yiu-ming Cheung Bo Han TMLR Group and Department of Computer Science Hong Kong Baptist University TMLR Group University of Melbourne Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Department of Computer Science Hong Kong Baptist University

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EABC: Energy-aware Centrality-based Caching for Named Data Networking in the IoT

EABC: Energy-aware Centrality-based Caching for Named Data N...

引用

IEEE International Symposium on World of Wireless Mobile and Multimedia networks (WoWMoM)

作者： Xingyun He Hu Liu Wuyungerile Li Alvin Valera Winston K.G. Seah Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China Wireless Networks Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Named Data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availability, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

关键词： Wireless communication Energy consumption Network topology Memory management Topology Delays Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Collaborative Game Theory and Reinforcement Learning Improvements for Radar Tracking

Collaborative Game Theory and Reinforcement Learning Improve...

引用

IEEE International Conference on Radar

作者： Geoffrey Dolinger Alexander Stringer Timothy Sharp Joseph Karch Justin G. Metcalf Adam Bowersox School of Electrical and Computer Engineering and the Advanced Radar Research Center at the University of Oklahoma US Air Force’s Oklahoma City Air Logistics Complex and the 76 Software Engineering Group

This paper considers a variation of the search and evade challenge where radar search/track agents collaboratively attempt to detect and track an evasive agent in a limited information game. The evasive agent utilizes heuristic behaviors to navigate to objective points without being detected by the search agents. In this paper, we compare the success of target detection by training two variations of track agents and implement three game theory (GT) methods. A search and evade game is designed to enable non-collaborative agents a win rate between 30%-50%, then the increase in win rate is recorded given the different track agents and GT methods used. The track methods explored in this paper include a Gaussian search and reinforcement learning (RL) track agent. The GT methods tested include non-collaborative best policy (NCBP), collaborative best policy (CBP), and new leader-follower (LF) consensus method. The results show the RL agent outperformed the basic Gaussian method. In addition, both collaborative GT methods increase the win rate, with the greatest gain from the proposed leader-follower method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Intelligent Surfaces: A Physical Layer Security Perspective 4

Reconfigurable Intelligent Surfaces: A Physical Layer Securi...

引用

4th International Conference on Data Intelligence and Security, ICDIS 2022

作者： Lipps, Christoph Herbst, Jan Reddy, Rekha Franke, Lars Becker, Sven Rahm, Marco Dieter Schotten, Hans German Research Center for Artificial Intelligence Intelligent Networks Research Group KaiserslauternD-67663 Germany Institute for Wireless Communication and Radio Positioning Technische Universitt Kaiserslautern KaiserslauternD-67663 Germany Research Center OPTIMAS Technische Universit Kaiserslautern Department of Electrical and Computer Engineering KaiserslauternD-67663 Germany

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

The conceptual design of the Sixth Generation (6G) wireless systems include, among others, Reconfigurable Intelligent Surfaces (RISs), arrays of elements that are capable of manipulating and reconfiguring the wireless propagation environment. Thereby, they are claimed to have advantages in different subjects, such as coverage, energy efficiency and localization. Therefore, this work discusses five use-cases of how RISs could contribute to Physical Layer Security (PhySec) concepts. By actively influencing the scattering, reflection and refraction characteristics of electromagnetic waves, beamforming and - splitting, as well as by adding individual information to the transmitted signals, they can increase the environmental wireless security. In addition to the description and discussion of the applicability of conceivable use-cases the RIS behaviour is demonstrated by full-wave simulations of the electric far-field. © 2022 IEEE.

关键词： Wireless networks

来源：评论

学校读者我要写书评

暂无评论

A Method for Person Re-Identification and Trajectory Tracking in Cross-Device Edge Environments

A Method for Person Re-Identification and Trajectory Trackin...

引用

International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Jian An Yunxiang Zhao Feifei Wang Xin He Xiaolin Gui School of Computer Science and Technology Shaanxi Provincial Key Laboratory of Computer Networks Xi’an Jiaotong University Xi’an China Henan Jiaoyuan Engineering Technology Group Co. LTD Henan China School of Software Henan University Henan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Addressing expanded surveillance scopes, cross-device person re-identification emerges as a crucial issue. Traditional approaches to re-identification and tracking confront challenges in feature matching challenges, and fail to harness the potential of edge computing environments, which offer viable solutions to bandwidth and latency constraints. The paper introduces a novel, edge computing and shared cache-based method for person re-identification and tracking. In this framework, edge nodes exploit deep learning method to execute image recognition for person re-identification and trajectory tracking. Edge nodes are deployed within a tri-layered architecture, encompassing Hierarchical Edge Cloud (HEC), Mobile Edge Computing (MEC), and Central Cloud (CC). Emphasizing part feature significance for person re-identification, the approach employs a refined Part-based Convolutional Baseline model guided by the triplet loss function. Experimental outcomes validate the high re-identification precision and efficacious load balancing capability of this method in person re-identification and tracking operations. Achieving a harmonized trade-off between task accuracy and response time, the proposed framework offers a robust solution for person re-identification and tracking within edge computing contexts.

关键词： Accuracy Trajectory tracking Image edge detection Computational modeling computer architecture Trajectory Time factors

来源：评论

学校读者我要写书评

暂无评论

Improving the Performance of Network Intrusion Detection Systems in IoT Using Grid Search & Feature Selection by Decision Tree

Improving the Performance of Network Intrusion Detection Sys...

引用

International Conference on Web research (ICWR)

作者： Saeed Doostali Zahra Tanha Behzad Soleimani Neysiani Ali Saghaei Mirnima Mousavi Department of Computer Engineering University of Kashan Kashan Isfahan Iran Department of Computer Science Luxembourg University Luxembourg Department of Software Engineering Oil Design and Construction Company Isfahan Iran Department of Research & Development Ava Aria Information Co. Demis Group Isfahan Iran Department of Computer Engineering Ardebil Branch Islamic Azad University Ardebil Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Neural networks like deep learning have become effective solutions for NIDS. Their ability to learn intricate patterns and behaviors makes them well-suited for distinguishing between normal network traffic and attacks. However, a significant drawback lies in the resource-intensive training process. Many network gateways and routers lack the necessary memory and processing power to train and execute such models. This study leverages the Botnet Malware attacks of the Kitsune dataset. Our analysis focuses on evaluating this dataset using various algorithms in an offline mode, which proves to be more efficient and exact for detecting attacks within local networks. The proposed method for feature selection uses the efficiency detector value (EDV). The decision tree has the best results between deep learning, linear regression, and random forest algorithms with 99.97 % recall, which has 0.28 % improvement versus deep federated learning. The proposed feature selection method improves the runtime by about 67 % using the proposed feature selection method.

关键词： Deep learning Training Runtime Machine learning algorithms Botnet Network intrusion detection Evolutionary computation Feature extraction Decision trees Optimization

来源：评论

学校读者我要写书评

暂无评论

Improving Predictive Navigation Through the Optimization of Counterfactual Track Evaluation

Improving Predictive Navigation Through the Optimization of ...

引用

IEEE Symposium on Position Location and Navigation (PLANS)

作者： Alexander Stringer Geoffrey Dolinger Timothy Sharp Debra Hogue Joseph Karch Lesya Borowska Justin G. Metcalf US Air Force's Oklahoma City Air Logistics Complex and the 76 Software Engineering Group School of Electrical and Computer Engineering and the Advanced Radar Research Center at the University of Oklahoma Department of Computer Science University of Oklahoma

Modern navigation applications are confronting the challenge of path identification and prediction. Because of the inherent noisiness of measurement techniques, positional data often contains errors. Over time, when attempting to predict future positions, these errors can compound, causing drift in the target's path. Filtering or regression techniques can use periodic high-fidelity measurements, like those from GPS sensors, to correct positional data and reduce historic errors. These techniques have been much less effective in specialized environments where GPS is intermittent or denied. At the same time, the development of semi- and fully-autonomous systems has increased the need for accurate predictive navigation. To address this problem, previous methods were proposed for repairing tracking data using causality-aware machine learning (ML). It combined a long short-term memory (LSTM) network that predicted target paths with the non-dominated sorting genetic algorithm II (NSGA-II) which identified and evaluated counterfactual paths. Here te authors expand on that work by improving the simulated data used to train and test the system, expanding the GA by adding additional first principles-based objective functions, improving upon the LSTM implementation, and utilizing Extended Kalman Filter (EKF) pre-processing. System testing is conducted on Matlab-generated navigation scenarios, and results are compared to both EKF correction and spline interpolation. The proposed counterfactual track repair (CTR) tool produces paths with less repeatability than traditional approaches. However, it is shown to consistently generate more realistic path corrections, lower error in predictions based on its corrected paths, and is highly configurable, demonstrating the value and utility of this approach.

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：