检索结果-内蒙古大学图书馆

IEEE International Conference on Communications (ICC)

作者： Muthukrishnan Senthil Kumar Aresh Dadlani Masoumeh Moradian Behrouz Maham Theodoros A. Tsiftsis Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore India Department of Electrical and Computer Engineering Nazarbayev University Astana Kazakhstan School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Informatics and Telecommunications University of Thessaly Lamia Greece

Concurrent with the rise of real-time wireless systems enabled by the Internet of Things, the age of information (AoI) has been widely perceived as a crucial destination-centric performance metric to quantify the timeliness of data delivery. This paper deals with the analysis of information freshness in a multi-source M/G/1 queueing system with utilization of idle server time, referred to as server vacation, in an effective manner. In particular, the status update packets in our model are generated independently by a finite set of source nodes and according to a Poisson process, while their service time follows a general random variable. Using stochastic decomposition and the Laplace-Stieltjes transform, we derive the average AoI (AAoI) expression for the multi-source M/G/1 queueing model with generally-distributed vacation time in closed form. Our numerical simulations validate the accuracy of the derived AAoI expression and assess the impact of different parameters on the system performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of Wind Turbine Defects based on the SqueezeNet Neural Network

Classification of Wind Turbine Defects based on the SqueezeN...

引用

International computer Sciences and Information Technologies (CSIT)

作者： Lesia Dubchak Anatoliy Sachenko Carsten Wolff Nadiia Vasylkiv Zenovii Bernas Department of Computer Engineering West Ukrainian National University Ternopil Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Department of Tele-Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland Dortmund University of Applied Science and Arts Dortmund Germany Department of Information Computer Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significantly reduce their efficiency. The study proposes using image classification techniques with neural networks, particularly SqueezeNet, to automate defect detection. The compact nature of SqueezeNet makes it suitable for real-time applications with limited computing resources. Through training and testing, the network achieved an accuracy of 89%, demonstrating its potential to improve wind turbine maintenance and reduce operational costs.

关键词： Training Accuracy Costs Neural networks Real-time systems Wind turbines Maintenance Defect detection Testing Image classification

来源：评论

学校读者我要写书评

暂无评论

Federated learning: Applications, challenges and future directions

引用

International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

来源：评论

学校读者我要写书评

暂无评论

BDDoS: Blocking Distributed Denial of Service Flooding Attacks With Dynamic Path Detectors

BDDoS: Blocking Distributed Denial of Service Flooding Attac...

引用

International Conference on computer Communication and informatics (ICCCI)

作者： P. Chinnasamy Sai Devika V Balaji S. Dhanasekaran B. Jency A Jebamani Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai India Department of Information Technology Kalasalingam Academy of Research and Education (Deemed to be University) Srivilliputtur India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

A distributed environment's version of a huge Denial of Services (DoS) attack against a potential service is referred to as a Distributed DoS (DDoS) attack. Adversaries aiming for this kind of attack make a consistent effort to exploit software bugs in computers that are utilized to attack a target server. Zombies are computers that unwittingly help an attacker, and the attacker retains a malicious application known as an agent on all these zombies. While occasionally solutions are developed, the attackers are advancing their methods for launching DDoS attacks. Having defenses against both the present and future DDoS variations is essential in this circumstance. However, managing such massive threats calls for a thorough understanding of range and detection systems. It provides information about DDoS attack terminology, attack kinds, and potential defenses. Assaults on and defenses against cloud computing are also covered. If a DDoS attacks & its extent are understood, new measures for the well and impending DDoS attacks can be created.

关键词： computers Cloud computing Machine learning algorithms Terminology Static VAr compensators Machine learning Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV Autonomous Navigation in Indoor Environments Using Reinforcement Learning and Convolutional Neural Networks 22

Enhancing UAV Autonomous Navigation in Indoor Environments U...

引用

22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Safarpour Motealegh Mahalegi, Homayoun Farhadi, Amirfarhad Molnar, Gyorgy Nagy, Eniko Obuda University Institute of Mechatronics and Vehicle Engineering Budapest Hungary Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Obuda University Hungary Szechenyi University Apaczai Csere János Faculty of Humanities Education and Social Sciences Kando Kalman Faculty of Electrical Engineering Budapest Gyor Hungary Obuda University Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350385601

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

引用

computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

来源：评论

学校读者我要写书评

暂无评论

Improving Wideband Massive MIMO Channel Estimation with UAV State-Space Information

引用

IEEE Transactions on Vehicular technology 2025年

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Alexandropoulos, George C. ATHENA Research and Innovation Centre Industrial Systems Institute Rio-Patras 26504 Greece University of Patras Department of Computer Engineering and Informatics Rio-Patras 26504 Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilissia Athens15784 Greece

In this paper, we focus on the estimation of massive Multiple-Input Multiple-Output (mMIMO) channels in high-frequency point-to-point wireless communications between a terrestrial multi-antenna base station, realizing fully digital beamforming, and a multi-antenna Unmanned Aerial Vehicle (UAV), capable of only analog combining via a single reception Radio-Frequency (RF) chain. A wideband channel model, incorporating selectivity which is usually referred to as beam squint and is particularly relevant in high frequency bands, such as millimeter waves and terahertz, is considered. To account for the UAV's limited capability to collect long training sequences, due to its single-RF reception architecture, and the beam-squint effect at both communication ends, we present a novel channel estimation approach that exploits information provided by the control module of the UAV, namely its state-space vector. The proposed approach comprises a hybrid parametric and non-parametric estimation optimization framework that is efficiently solved through an iterative algorithm using the alternating direction method of multipliers. Our extensive performance investigations, including comparisons with benchmark schemes, showcase that considerable gains can be achieved if state-space information from the UAV's control module is appropriately incorporated in the channel estimation process. © 1967-2012 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Cooperative and Competitive Multi-Agent Systems:From Optimization to Games

引用

IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 763-783页

作者： Jianrui Wang Yitian Hong Jiali Wang Jiapeng Xu Yang Tang Qing-Long Han Jürgen Kurths Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Potsdam Institute for Climate Impact Research 14473 Potsdam Institute of Physics Humboldt University of Berlin12489 BerlinGermany

Multi-agent systems can solve scientific issues related to complex systems that are difficult or impossible for a single agent to solve through mutual collaboration and cooperation *** a multi-agent system,agents with a certain degree of autonomy generate complex interactions due to the correlation and coordination,which is manifested as cooperative/competitive *** survey focuses on multi-agent cooperative optimization and cooperative/non-cooperative *** from cooperative optimization,the studies on distributed optimization and federated optimization are *** survey mainly focuses on distributed online optimization and its application in privacy protection,and overviews federated optimization from the perspective of privacy protection ***,cooperative games and non-cooperative games are introduced to expand the cooperative optimization problems from two aspects of minimizing global costs and minimizing individual costs,***-agent cooperative and non-cooperative behaviors are modeled by games from both static and dynamic aspects,according to whether each player can make decisions based on the information of other ***,future directions for cooperative optimization,cooperative/non-cooperative games,and their applications are discussed.

关键词： Cooperative games counterfactual regret minimization distributed optimization federated optimization fictitious

来源：评论

学校读者我要写书评

暂无评论

Improved Distance (Sensitivity) Oracles with Subquadratic Space

Improved Distance (Sensitivity) Oracles with Subquadratic Sp...

引用

Annual IEEE Symposium on Foundations of computer Science

作者： Davide Bilò Shiri Chechik Keerti Choudhary Sarel Cohen Tobias Friedrich Martin Schirneck Dep. of Inf. Eng. CS and Mathematics University of L'Aquila Department of Computer Science Tel Aviv University Dep. of CS and Engineering Indian Institute of Technology Delhi School of Computer Science The Academic College of Tel Aviv-Yaffo Hasso Plattner Institute University of Potsdam Faculty of Computer Science University of Vienna

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

A distance oracle (DO) for a graph $G$ is a data structure that, when queried with vertices $s,t$ , returns an estimate $\widehat{d}(s,t)$ of their distance in $G$ . The oracle has stretch $(\alpha, \beta)$ if the estimate satisfies $d(s,t)\leqslant \widehat{d}(s,t)\leqslant \alpha\cdot d(s,t)+\beta$ . An $f-\mathbf{edge}$ fault-tolerant distance sensitivity oracle $(f-\mathbf{DSO})$ additionally receives a set $F$ of up to $f$ edges and estimates the distance in $G-F$ . Our first contribution is the design of new distance oracles with subquadratic space for undirected graphs. We show that introducing a small additive stretch $\beta > 0$ allows one to make the multiplicative stretch $\alpha$ arbitrarily small. This sidesteps a known lower bound of $\alpha\geqslant 3$ (for $\beta=0$ and subquadratic space) [Thorup & Zwick, JACM 2005]. We present a DO for graphs with edge weights in $[0, W]$ that, for any positive integer $\ell$ and any $c\in(0,\ell/2]$ , has stretch $(1+\frac{1}{\ell},2W)$ , space $\widetilde{O}(n^{2-\frac{c}{\ell}})$ , and query time $O(n^{c})$ , generalizing results by Agarwal and Godfrey [SODA 2013] to arbitrarily dense graphs. Our second contribution is a framework that turns an $(\alpha,\beta)- \mathbf{stretch}$ DO for unweighted graphs into an $(\alpha(1+\varepsilon),\beta)-\mathbf{stretch}. f-\mathbf{DSO}$ with sensitivity $f=o(\log(n)/\log\log n)$ retaining sub-quadratic space. This generalizes a result by Bilò, Chechik, Choudhary, Cohen, Friedrich, Krogmann, and Schirneck [TheoretiCS 2024]. Combining the framework with our new DO gives an $f-\mathbf{DSO}$ that, for any $\gamma\in(0, (\ell+1)/2]$ , has stretch $((1+\frac{1}{\ell})(1+\varepsilon), 2)$ , space $n^{2-\frac{\gamma}{(t+1)(f+1)}+o(1)}/\varepsilon^{f+2}$ , and query time $\widetilde{O}(n^{\gamma}/\varepsilon^{2})$ . This is the first $f-\mathbf{DSO}$ with subquadratic space, n

关键词： computer science Fault tolerance Sensitivity Additives Fault tolerant systems Data structures

来源：评论

学校读者我要写书评

暂无评论

MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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：