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arXiv 2024年

作者： Wang, Xinming Yang, Jun Mao, Jianliang Liang, Jinzhuo Li, Shihua Yan, Yunda The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China The Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The College of Automation Engineering Shanghai University of Electric Power Shanghai201306 China The Department of Computer Science University College London LondonWC1E 6BT United Kingdom

In this study, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with unknown environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF) by exploiting the generalized contact model and the prior information of the environment, i.e., the prior stiffness and rest position, for robot kinematics. The difference between the real environment and the generalized contact model is approximated by constructing a tracking differentiator, and its estimation error is quantified based on Lyapunov theory. By interpreting strict interaction safety specification as a dynamic constraint, restricting the desired joint angular rates in kinematics, the proposed approach modifies nominal compliant controllers using quadratic programming, ensuring adherence to interaction force constraints in unknown environments. The strict force constraint and the stability of the closed-loop system are rigorously analyzed. Experimental tests using a UR3e industrial robot with different environments verify the effectiveness of the proposed method in achieving the force constraints in unknown environments. Note to Practitioners—Robotic manipulators are increasingly involved in complex interaction missions with humans and various environments. As these interactions become more prevalent, ensuring the safety of both robots and their human and environmental counterparts has become imperative. Although advanced controllers with safety constraints have been explored, ensuring strict interaction force guarantees remains challenging, particularly in unknown environments. To address this challenge, this paper proposes a new safety-critical compliant control scheme using the control barrier function (CBF) technique. By formulating the approach as quadratic programming and dynamically adjusting the nominal compliant controller online, the propose

关键词： controllers

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Simple Multiuser Twin-Field Quantum Key Distribution Network

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Physical Review Applied 2022年第1期17卷 014025-014025页

作者： Xiaoqing Zhong Wenyuan Wang Reem Mandil Hoi-Kwong Lo Li Qian Center for Quantum Information and Quantum Control Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Center for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Road Hong Kong

Twin-field quantum key distribution (TFQKD) systems have shown great promise for implementing practical long-distance secure quantum communication due to its measurement-device-independent nature and its ability to offer fundamentally superior rate-loss scaling than point-to-point QKD systems. A surge of research and development effort in the last two years has produced many variants of protocols and experimental demonstrations. In terms of hardware topology, TFQKD systems interfering quantum signals from two remotely phase-locked laser sources are in essence giant Mach-Zehnder interferometers (MZIs) requiring active phase stabilization. Such configurations are inherently unsuitable for a TFQKD network, where more than one user pair share the common quantum measurement station, because it is practically extremely difficult, if not impossible, to stabilize MZIs of largely disparate path lengths, a situation that is inevitable in a multi-user-pair TFQKD network. On the other hand, Sagnac interferometer-based TFQKD systems exploiting the inherent phase stability of the Sagnac ring can implement asymmetric TFQKD, and are therefore eminently suitable for implementing a TFQKD network. In this work, we experimentally demonstrate a proof-of-principle multi-user-pair Sagnac TFQKD network where three user pairs sharing the same measurement station can perform pairwise TFQKD through time multiplexing, with channel losses up to 58.00 dB, and channel loss asymmetry up to 15.00 dB. In some cases, the secure key rates still beat the rate-loss bound for point-to-point repeaterless QKD systems, even in this network configuration. Our demonstration of this multi-user-pair TFQKD network is a step in advancing quantum-communication network technologies.

关键词： Optical quantum information processing Quantum communication Quantum cryptography Quantum networks Quantum optics Communication networks Optical fibers Optical interferometry

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Credit card transaction data analysis and performance evaluation of machine learning algorithms for credit card fraud detection

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AIP Conference Proceedings 2022年第1期2555卷

作者： Vivek Ghai Sandeep Singh Kang 1Department of Computer Science & Engineering Chandigarh University Punjab India 140413

Fraud is defined as any malicious behavior with the intent of causing financial loss to a third party[1]. Due to the unprecedented pandemic scenario prevailing over the world in the past 2 years, there has been rapid participation in online-based transactional activities using credit cards and it has coerced many people into the digital world[2].This increased utilization of advanced digital cash or plastic cash even in new nations has highlighted increased fraud cases related to credit cards[3]. Obviously, this elevated activity has resulted in criminals getting more attracted to it[4]. As part of comprehensive fraud prevention, fraud detection helps to reduce the manual aspects of the screening/checking process and makes the process automatic. This is now one of the most well-known industry/government issues[5]. Detection of potentially fraudulent transactions is the main goal, so as the customers are not charged for the products which were not purchased by them. Additionally, we have to reduce the business costs and make it profitable for credit cards companies. The many strategies used to identify credit card frauds are investigated in this study. This paper examines the algorithms used for detecting fraud related to credit cards, as well as their advantages and disadvantages, along with performance comparisons that will aid in the evaluation of their usability and accuracy.

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Performance evaluation of classifiers for non communicable diseases

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AIP Conference Proceedings 2023年第1期2523卷

作者： S. Srinivasan S. Selvakumar Pacha Shobha Rani D. JayaKumar 1Department of Computer Science and Engineering R.M.D Engineering College Kavaraipettai Tamil Nadu India 2Department of Computer Science and Engineering Visvesvaraya College of Engineering and Technology Hyderabad Telangana India

Non-Communicable Diseases (NCD) has been drastically increased across the world in the recent years. Diseases that are not transmissible from one person to another are known as Non-Communicable Diseases (NCDs). Parkinson's disease, Autoimmune Disorders, Strokes, Heart Diseases, Cancers, Diabetes are examples of NCDs. It is found that certain Non-Communicable Diseases are believed to be exacerbated by lifestyle and climate which cause of death in the world. Many Machine Learning methods such as Nave Bayes, Support Vector Machine have been applied to predict NCDs. The aim of this paper is to predict the Non-Communicable Diseases (NCDs) and evaluate the performance of the key classifiers in terms of Prediction accuracy, Kappa and F-Measure. The progress in computer training and AI can remarkably help the analysis of Non-Communicable Diseases (NCDs). Nevertheless, the intrinsic complication of black-box samples controls the understanding of the sample. Consequently, likely regulative concerns arise. Furthermore, the lack of faith inside the therapeutic society is obvious due to the absence of understanding of how and why a sample prophesies. This research illustrates how sample-cynical ways of explainable AI (XAI) can assist give descriptions to assume black-box samples on NCDs information collection better.

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Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

SSRN

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SSRN 2023年

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Cao, Junwei Automatic Control Laboratory EPFL Lausanne1015 Switzerland Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Department of Electrical Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. In a simplified scenario, centralized training, the most common paradigm for MARL, precludes large-scale deployment of MARL in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-life application scenarios compared with its centralized counterpart. However, distributed training still faces the challenge of system scalability, e.g., the high communication burden during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. The proposed solution is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of residences. In this way, the private data of residences is preserved completely and the communication cost is reduced significantly. The simulation experiments demonstrate that the proposed framework can achieve comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints. © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Plant disease recognition using deep learning

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AIP Conference Proceedings 2022年第1期2555卷

作者： Damandeep Kaur Pritesh Ranjan Neeraj Vaidya 1Department of computer science and Engineering Chandigarh University Ghauran Mohali India

Detection of disease in plants is a paramount issue in the agricultural sector of any country. Crop failure adversely affects the farmer and has an enormous impact on the economy. Modern technology has developed various tools using deep learning techniques to perform errorless predictions and to originate a rapid treatment. The purpose of this paper is to propose a deep learning based method to detect disease in plants using leaf images. Our main goal is to develop an efficient methodology to provide reliable predictions. We have considered CNN (Convolution Neural Network) algorithm as our principal detector. The proposed system can approach complex scenarios from the plant’s body. The result achieved is very promising of 98.33 accuracy and 0.0047 loss on validation data.

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Short-term load forecasting of building electricity consumption using NARX neural networks model 6

Short-term load forecasting of building electricity consumpt...

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6th International Conference on Smart and Sustainable Technologies, SpliTech 2021

作者： Zuazo, Irati Zapirain Boussaada, Zina Aginako, Naiara Curea, Octavian Camblong, Haritza Sierra, Basilio Faculty of Engeneering of Gipuzkoa Department of Systems Engineering and Control Plaza de Europa 1 Donostia-San Sebastián20018 Spain Univ. Bordeaux ESTIA INSTITUTE of TECHNOLOGY BidartF-64210 France Faculty of Engeneering of Gipuzkoa Department of Computer Science and Artificial Intelligence Plaza de Europa 1 Donostia-San Sebastián20018 Spain

ISBN: (纸本)9789532901122

Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumption-forecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17, 6% MAPE in NARX and 25, 19% with LSTM model, both evaluated during a regular week on winter in adverse conditions. © 2021 University of Split, FESB.

关键词： Distributed power generation

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GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

arXiv

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arXiv 2023年

作者： Li, Yuhang Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer and Information Technology Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety the School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station. By modeling the MU-MISO network as a graph, a GNN-based architecture named CRGAT is proposed to directly map the channel state information to the beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and avoid the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the base station. The CRGAT is trained in an unsupervised learning manner with two proposed loss functions. An evaluation method is proposed for the learning-based approach, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering

arXiv

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arXiv 2021年

作者： Piccialli, Veronica Russo, Anna Russo Sudoso, Antonio M. Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 00185 Italy Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Via del Politecnico 1 00133 Italy

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task. In recent years, the use of background knowledge to improve the cluster quality and promote interpretability of the clustering process has become a hot research topic at the intersection of mathematical optimization and machine learning research. The problem of taking advantage of background information in data clustering is called semi-supervised or constrained clustering. In this paper, we present branch-and-cut algorithm for semi-supervised MSSC, where background knowledge is incorporated as pairwise must-link and cannot-link constraints. For the lower bound procedure, we solve the semidefinite programming relaxation of the MSSC discrete optimization model, and we use a cutting-plane procedure for strengthening the bound. For the upper bound, instead, by using integer programming tools, we use an adaptation of the k-means algorithm to the constrained case. For the first time, the proposed global optimization algorithm efficiently manages to solve real-world instances up to 800 data points with different combinations of must-link and cannot-link constraints and with a generic number of features. This problem size is about four times larger than the one of the instances solved by state-of-the-art exact algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Integer programming

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Secrecy Outage Probability Analysis for Downlink RIS-NOMA Networks with On-Off control

arXiv

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arXiv 2024年

作者： Pei, Yingjie Yue, Xinwei Yi, Wenqiang Liu, Yuanwei Li, Xuehua Ding, Zhiguo Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering Princeton University Princeton United States School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

Reconfigurable intelligent surface (RIS) has been regarded as a promising technology since it has ability to create the favorable channel conditions. This paper investigates the secure communications of RIS assisted non-orthogonal multiple access (NOMA) networks, where both external and internal eavesdropping scenarios are taken into consideration. More specifically, novel approximate and asymptotic expressions of secrecy outage probability (SOP) for the k-th legitimate user (LU) are derived by invoking imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC). To characterize the secrecy performance of RIS-NOMA networks, the diversity order of the k-th LU with ipSIC/pSIC is obtained in the high signal-to-noise ratio region. The secrecy system throughput of RIS-NOMA networks is discussed in delay-limited transmission mode. Numerical results are presented to verify theoretical analysis that: i) The SOP of RIS-NOMA networks is superior to that of RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication schemes;ii) As the number of reflecting elements increases, the RIS-NOMA networks are capable of achieving the enhanced secrecy performance;and iii) The RIS-NOMA networks have better secrecy system throughput than that of RIS-OMA networks and conventional cooperative communication schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Cooperative communication

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