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SSRN 2024年

作者： Zhao, Jingyu Liu, Shaojie Xue, Wenchao Ding, Yanjun Li, Donghai State Key Laboratory of Power Systems Department of Energy and Power Engineering Tsinghua University Beijing100084 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

This paper presents a hierarchical control strategy for a broad class of multi-input multi-output (MIMO) uncertain systems without a nominal identified model. The core idea is to utilize Active Disturbance Rejection control (ADRC) to construct an equivalent desired dynamic model, which avoids the complex model identification for MIMO systems and improves the uncertainty rejection capability of the predictive controller. At the foundational layer, the extended state observer is constructed to timely estimate the uncertainties in the coupling of subsystems, enforcing these into an equivalent desired plant through dynamic compensation linearization. The modified input-output pair models depend solely on compensation law parameter and can directly serve as the prediction model for multi-step prediction and centralized receding optimization, ensuring optimal control of the MIMO system. The strategy is validated on typical MIMO systems of various sizes (2×2, 3×3, 8×8), including minimum phase, unstable, higher-order, and time-delay subsystems. Monte Carlo simulations quantitatively assess the enhanced robustness. Additionally, a 2-DOF helicopter experiment system, characterized by nonlinear time-varying coupling between attitude angles is used to evaluate the servo-regulatory performance of the proposed strategy. Results demonstrate the effectiveness of our controller in both nominal and mismatch model scenarios. © 2024, The Authors. All rights reserved.

关键词： Disturbance rejection

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MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding

MarineVRS: Marine Video Retrieval System with Explainability...

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OCEANS

作者： Tan-Sang Ha Hai Nguyen-Truong Tuan-Anh Vu Sai-Kit Yeung Department of Computer Science and Engineering The Hong Kong University of Science and Technology Division of Integrative Systems and Design (ISD) The Hong Kong University of Science and Technology

Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we present MarineVRS, a novel and flexible video retrieval system designed explicitly for the marine domain. MarineVRS integrates state-of-the-art methods for visual and linguistic object representation to enable efficient and accurate search and analysis of vast volumes of underwater video data. In addition, unlike the conventional video retrieval system, which only permits users to index a collection of images or videos and search using a freeform natural language sentence, our retrieval system includes an additional Explainability module that outputs the segmentation masks of the objects that the input query referred to. This feature allows users to identify and isolate specific objects in the video footage, leading to more detailed analysis and understanding of their behavior and movements. Finally, with its adaptability, explainability, accuracy, and scalability, MarineVRS is a powerful tool for marine researchers and scientists to efficiently and accurately process vast amounts of data and gain deeper insights into the behavior and movements of marine species.

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A Universal Multi-Vehicle Cooperative Decision-Making Approach in Structured Roads by Mixed-Integer Potential Game

arXiv

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arXiv 2024年

作者： Meng, Chengzhen Huang, Zhenmin Ma, Jun The Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Due to the intricate of real-world road topologies and the inherent complexity of autonomous vehicles, cooperative decision-making for multiple connected autonomous vehicles (CAVs) remains a significant challenge. Currently, most methods are tailored to specific scenarios, and the efficiency of existing optimization and learning methods applicable to diverse scenarios is hindered by the complexity of modeling and data dependency, which limit their real-world applicability. To address these issues, this paper proposes a universal multi-vehicle cooperative decision-making method in structured roads with game theory. We transform the decision-making problem into a graph path searching problem within a way-point graph framework. The problem is formulated as a mixed-integer linear programming problem (MILP) first and transformed into a mixed-integer potential game (MIPG), which reduces the scope of problem and ensures that no player needs to sacrifice for the overall cost. Two Gauss-Seidel algorithms for cooperative decision-making are presented to solve the MIPG problem and obtain the Nash equilibrium solutions. Specifically, the sequential Gauss-Seidel algorithm for cooperative decision-making considers the varying degrees of CAV interactions and flexibility in adjustment strategies to determine optimization priorities, which reduces the frequency of ineffective optimizations. Experimental evaluations across various urban traffic scenarios with different topological structures demonstrate the effectiveness and efficiency of the proposed method compared with MILP and comparisons of different optimization sequences validate the efficiency of the sequential Gauss-Seidel algorithm for cooperative decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Energy-Efficient Operation of Train Platoons in Urban Rail Transit systems: An MIP Approach 43

Energy-Efficient Operation of Train Platoons in Urban Rail T...

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43rd Chinese control Conference, CCC 2024

作者： Li, Haodong Yin, Jiateng Tang, Tao D'Ariano, Andrea You, Mengxing Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Systems Science Beijing100044 China RomaTre Universily Computer Science and Aeronautical Technologies Engineering Department of Civi Rome0018511 Italy Nanchang Rail Transit Group CO. Ltd Jiangxi330038 China

ISBN: (纸本)9789887581581

Train platooning, which allows multiple train units to be virtually coupled into a platoon with very short following distances, has become an emerging technology in railway industry. Our study investigates the energy-efficient operation of train platoons to reduce the energy consumption of trains while satisfying time-varying passenger demands in practice. We develop a mixed integer programming (MIP) model to optimize the timetable, running speed and coupling strategies of trains. Considering that the small-size train platoons consume much less energy but can serve fewer passengers, our model involves bi-objectives, i.e., to minimize the passenger waiting time and to minimize the overall energy consumption of trains. We also construct a series of model constraints to limit the movement of trains as well as the time-dependent flows of passengers, since a part of passengers during the peak period may have to wait for a longer time due to the capacity limitation of different sizes of trains. Finally, we conduct real-world case studies on the Beijing subway Yizhuang line to verify the effectiveness of our approach. The results indicate that the energy-efficient operation of train platoons can achieve a better trade-off between service quality and system energy consumption. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Energy efficiency

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials Convex functions Distortion measurement Information theory

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Distributed Generalized Nash Equilibrium Seeking for Constrained N-Cluster Games with Second-Order Dynamics

Distributed Generalized Nash Equilibrium Seeking for Constra...

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IEEE Conference on Decision and control

作者： Yan Zhao Min Meng Xiuxian Li Jia Xu Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies N-cluster games with secondorder dynamics, wherein the players’ decisions are restricted by local set constraints and nonlinear coupled inequality constraints. The presence of second-order dynamics coupled with constraints leads to difficulties in the design and analysis of generalized Nash equilibrium (GNE) seeking algorithms, since it may be impossible to directly determine the decisions of players based on their control inputs. To facilitate the autonomous execution of N-cluster game tasks through secondorder players, by employing state feedback, projection, primaldual, dynamic average consensus, and passivity methods, a distributed algorithm is proposed to find the variational GNE of the studied games, under which the players’ decisions can satisfy the set constraints all the time. Additionally, the algorithm’s convergence is rigorously analyzed, and its efficacy is validated by a simulation example.

关键词： State feedback Heuristic algorithms Games Nash equilibrium Time factors Distributed algorithms Convergence

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Answer Code Validation Program with Test Data Generation for Code Writing Problem in Java Programming Learning Assistant System

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engineering Letters 2024年第5期32卷 981-994页

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Lu, Xiqin Jing, Yanhui Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

In order to support the learning of novice students in Java programming, the web-based Java Programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher’s workload during the marking process, we present the answer code validation program that verifies all the source codes from a large number of students for each assignment and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedures in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness. © 2024, International Association of Engineers. All rights reserved.

关键词： Students

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Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier in Cloud Environment 1

Effective Intrusion Detection and Classification using Fuzzy...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Veena, C. Ramalakshmi, S. Bhoopathy, V. Bhosale, Minakshi Dattatraya Magadum, C.G. Abirami, S.K. Ghatkesar Telangana Hyderabad India Puducherry Karaikal India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India Faculty of MCA Yashoda Technical Campus Maharashtra Satara India Smt. Kasturbai Walchand College Maharashtra Sangli India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9781665460842

Cloud computing (CC) is the demonstration of the technology that makes use of the substructure for computing in a proficient fashion. This sort of computing offers great quantity of consequences in augmenting the productivity that verifies the risk handling management and decreases the cost. Intrusion detection system (IDS) is widely applied for detecting malicious actions in its host and the communication network. IDS is a procedure of discovering activities that take place in a network and attempts to fulfill the confidentiality, network, or security accessibility to smear the trust procedure. This article introduces an Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier (EIDC-FRC) model in Cloud Environment. The goal of the presented EIDC-FRC model is to determine the occurrence of intrusions and normal data traffic in the cloud environment. In order to achieve this, the presented EIDC-FRC model applies FRC for data classification process. The parameter tuning of the FRC model is performed using enhanced bird swarm algorithm (EBSA). The FRC is an effective model in pattern recognition that offers effective outcomes by the use of linguistic labels in the antecedents of the rules. The simulation analysis of the EIDC-FRC model ensured the enhancements of the presented approach compared to recent state of art approaches. © 2022 IEEE

关键词： Machine learning

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

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arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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A polynomial quantum computing algorithm for solving the dualization problem for positive boolean functions

A polynomial quantum computing algorithm for solving the dua...

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2023 International Workshop on AI for Quantum and Quantum for AI, AIQxQIA 2023

作者： Mezzini, Mauro Gomez, Fernando Cuartero Lopez Pelayo, Fernando Paulet Gonzalez, Jose Javier de la Cruz Calvo, Hernan Indibil Pascual, Vicente Department of Education Roma Tre University Rome00185 Italy Computing Systems Department Faculty of Computer Science Engineering University of Castilla-La Mancha Albacete02071 Spain Quantum Computing Department Qsimov Quantum Computing S.L Talavera de la Reina Toledo45600 Spain

Given two positive Boolean functions ∱ : {0, 1}n → {0, 1} and g : {0, 1}n → {0, 1} expressed in their positive irredundant DNF Boolean formulas, the dualization problem consists in determining if g is the dual of ∱, that is if ∱(x1, . . ., xn) = g(x1, . . . xn) for all (x1, . . . xn) ∈ {0, 1}n. In this paper we present a quantum computing algorithm that solves the dualization problem in polynomial time with respect to the dimensions of the DNF expressions. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Boolean Functions Computational complexity Dualization Quantum Algorithm

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