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arXiv 2022年

作者： Amanatidis, Georgios Birmpas, Georgios Filos-Ratsikas, Aris Voudouris, Alexandros A. Department of Mathematical Sciences University of Essex United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Computer Science University of Liverpool United Kingdom School of Computer Science and Electronic Engineering University of Essex United Kingdom

Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regarding various different notions of fairness for the indivisible case, like maximin share fairness (MMS) and envy-freeness up to any good (EFX). We survey the most important results in the discrete fair division literature, focusing on the case of additive valuation functions and paying particular attention to the progress made in the last 10 years. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method

On Solar Photovoltaic Parameter Estimation: Global Optimalit...

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IEEE Conference on Decision and control

作者： Shuhua Gao Cheng Xiang Ming Yu Kuan Tak Tan Tong Heng Lee School of Control Science and Engineering Shandong University Jinan China Department of Electrical and Computer Engineering National University of Singapore Singapore Power Automation Pte Ltd Singapore Engineering Cluster Singapore Institute of Technology Singapore

The literature presents a vast array of advanced metaheuristic methods for photovoltaic parameter estimation. However, the focus of this study is not to introduce another new method into this already crowded field. Instead, we examine two important but often overlooked questions: (i) are existing results globally optimal, and (ii) can a simpler method achieve comparable performance. We conduct case studies using two widely used I-V curve datasets. To address the first issue, we develop a branch and bound algorithm that, despite its sluggishness, either certifies the global minimum or provides a tight upper bound. These values serve as useful benchmarks for fair metaheuristic evaluation and further development. To answer the second question, our extensive examination and comparison surprisingly reveal that a basic differential evolution (DE) algorithm can achieve the certified global minimum or obtain the best known result. Additionally, the DE algorithm's runtime is much shorter than that of current state-of-the-art metaheuristic methods, making it a great choice for time-sensitive applications. This remarkable finding suggests that using increasingly complex metaheuristics in ordinary PV parameter estimation problems might be unnecessary. Finally, we discuss the implications of these outcomes and propose the simple DE method as the premier choice for industrial applications.

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Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism

arXiv

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arXiv 2023年

作者： Huo, Wei Tsang, Kam Fai Elvis Yan, Yamin Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively. Considering the limited energy of players and constrained bandwidths, we propose a stochastic event-triggered algorithm by triggering each player with a probability depending on certain events, which improves communication efficiency by avoiding continuous communication. We show that the distributed algorithm with the developed event-triggered communication scheme converges to the exact NE exponentially if the underlying communication graph is strongly connected. Moreover, we prove that our proposed event-triggered algorithm is free of Zeno behavior. Finally, numerical simulations for a spectrum access game are provided to illustrate the effectiveness of the proposed mechanism by comparing it with some existing event-triggered methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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Enhanced Model-Free Predictor for Latency Compensation in Remote Driving Systems

Enhanced Model-Free Predictor for Latency Compensation in Re...

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IEEE Symposium on Intelligent Vehicle

作者： Lin Zhao Mikael Nybacka Malte Rothhämel Jonas Mårtensson Department of Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower’s driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal’s peak time and uses it to address the predictor’s overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

关键词： Intelligent vehicles Simulation Predictive models Stability analysis Delays Automobiles Mechanical systems

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Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

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computers, Materials & Continua 2021年第12期69卷 4095-4111页

作者： Mohamed Esmail Karar Omar Reyad Abdel-Haleem Abdel-Aty Saud Owyed Mohd F.Hassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt Faculty of Science Sohag UniversitySohag82524Egypt Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Mathematics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Computer and Information Science CeRDaSUniversiti Teknologi PetronasMalaysia

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural ***,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm *** this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely *** sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the *** trees are labeled based on the sensor node number to identify the infested ***,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,*** proposed infestation classifier has been successfully validated on the public TreeVibes *** includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,*** to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio *** resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,*** the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

关键词： Red palm weevils smart precision agriculture internet of things artificial intelligence

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Speech Recognition of Vedic Sanskrit using Deep Learning Algorithm

Speech Recognition of Vedic Sanskrit using Deep Learning Alg...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Mrinal Pandey Rashmikiran Pandey Alexey Nazarov Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

In this study, we examined the use of computational techniques for accurately processing acoustic signals of human speech using digital media. Specifically, we focused on the Sanskrit language and applied a language modeling approach to improve recognition by machines. Our implementation of this approach for Sanskrit speech represents a novel approach in the field and has the potential to extract valuable information through automated processing. The ability to accurately process speech is crucial for effective human communication, and our research contributes to the development of more efficient and effective methods for achieving this goal..

关键词： Deep learning Computational modeling Natural languages Speech recognition Media computer networks Acoustics

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Enhanced DDoS Detection using Machine Learning

Enhanced DDoS Detection using Machine Learning

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International Conference on Information Systems and computer Networks (ISCON)

作者： Rashmikiran Pandey Mrinal Pandey Alexey Nazarov Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia

The rapid growth of internet population poses a serious challenge to the security of internet resources. The security is directly affected by the hits of Denial of Services (DoS) attack which is rampant nowadays. With this evolving threat, designing a cutting-edge method is difficult from a cyber-security perspective. In this study, we propose a deep learning-based system for detecting Distributed Denial of Service (DDoS) attacks, which utilizes Logistic Regression, K- Nearest Neighbor, and Random Forest algorithms. We assess proposed models using a recently updated NSL KDD dataset. Our research’s findings also demonstrate that proposed model is highly accurate in detecting Distributed Denial of Service (DDoS) attacks. Our results show that our proposed model significantly improves upon current state-of-the-art attack detection methods

关键词： Machine learning algorithms Computational modeling Sociology Forestry Denial-of-service attack Security computer crime

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Data-Enabled Finite control-Set Model Predictive control of a Grid-Tied Inverter

Data-Enabled Finite Control-Set Model Predictive Control of ...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Wenjie Wu Lin Qiu Hui Li Xing Liu Chao Du Youtong Fang College of Electrical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Florida State University Tallahassee Florida USA College of Electrical and Control Engineering Shaanxi University of Science & Technology Xian China

This paper presents a novel predictive control architecture for power converters that addresses the challenges of model mismatch and parameter sensitivity in the finite control-set model predictive control (FCS-MPC) framework. The proposed method employs a dynamic-linearization-based approach, which replaces the detailed model used in the FCS-MPC controller with a virtual equivalent data model, creating a data-enabled FCS-MPC architecture. By relying solely on input-output data, the proposed method exhibits considerable robustness against parameter perturbations while retaining the attractive features of the conventional FCS-MPC method. The effectiveness of the proposed design is demonstrated through comparative simulations results on a two-level grid-tied inverter.

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equivalent Permittivity

WiWm-EP: Wi-Fi CSI-based Wheat Moisture Detection Using Equi...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Hu, Pengming Yang, Weidong Wang, Xuyu Mao, Shiwen Shen, Erbo Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Knight Foundation School of Computing and Information Sciences Florida International University FL33199 United States Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States

ISBN: (纸本)9781665494274

Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper, according to the dielectric properties of wheat, we propose a wheat moisture content detection system with commercial Wi-Fi devices, termed WiWm-EP. First, we introduce the relationship between the moisture content of wheat and its dielectric constant. Then, we establish an equivalent permittivity (EP) model to characterize wheat moisture content, where the EP can be calculated from channel state information (CSI) of the dual antenna model. Besides, we build the fitting function between the EP and the moisture content as the wheat moisture detection model. Finally, we evaluate the performance of the system through different experiments. The average relative error of the detection results of five wheat samples with different moisture contents is less than 3%. © 2023 IEEE.

关键词： Permittivity

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