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arXiv 2024年

作者： Selvaraj, Dinesh Cyril Vitale, Christian Panayiotou, Tania Kolios, Panayiotis Chiasserini, Carla Fabiana Ellinas, Georgios CARS@Polito Politecnico di Torino Torino Italy The KIOS Research and Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus The KIOS Research and Innovation Center of Excellence The Department of Computer Science University of Cyprus Nicosia Cyprus

In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier;(ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles;and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for human-like driving across styles. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

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computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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A New 8/14 Two-Phase Switched Reluctance Motor with Improved Performance

arXiv

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arXiv 2024年

作者： Davarpanah, Gholamreza Shirzad, Hossein Faiz, Jawad Electrical Engineering Department Amirkabir University of Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering University of Tehran Tehran Iran

Despite their simple and robust structure, low cost, and simple cooling system, switched reluctance motors (SRMs) face the challenge of low mean torque. A possible solution is to change the structure of SRMs. This article introduces an innovative combination of the number of rotor teeth and stator teeth of a two-phase switch reluctance motor (TPSRM) with eight teeth for the stator and fourteen teeth for the rotor. As a result of its unique design, which has a short path for passing the main flux, it requires less magnetomotive force. This leads to less core and copper loss, resulting in increased efficiency. Each tooth of the stator in a phase develops a positive torque during the rotation of the rotor, which increases the torque and consequently increases the mean torque of the proposed TPSRM. A current hysteresis control (CHC) is simulated by 2D FEM for the proposed 8/14 TPSRM and the conventional 8/12 TPSRM under the same mechanical load on the shaft to get a current hysteresis reference of 15A at the nominal speed of 600 rpm. To verify the novelty and advantages of the suggested TPSRM, it is compared with the conventional 8/12 TPSRM in terms of mean and peak torque, torque density, and core and copper losses were compared. Lastly, the proposed 8/14 TPSRM is shown to have better performance than the conventional 8/12 TPSRM. Copyright © 2024, The Authors. All rights reserved.

关键词： Stators

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Dual efficient reversible data hiding using Hamming code and OPAP

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Journal of Information Security and Applications 2023年 76卷

作者： Kim, Cheonshik Yang, Ching-Nung Zhou, Zhili Jung, Ki-Hyun Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Computer Science and Information Engineering National Dong Hwa University Taiwan School of Computer and Software & Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210000 China Department of Software Convergence Andong National University Gyeongbuk 36729 Korea Republic of

Dual image based RDH is a separate research area of the traditional RDH method, which has been continuously studied by several researchers over the past decade. In the research of dual RDH, extension of the traditional RDH algorithm or methods of improving the existing DH algorithm to dual RDH have been proposed. Hamming code (HC) is a method used as a DH method, but it is not applied to RDH. In this paper, we propose a dual RDH method using Hamming codes. Previously, Jana et al. proposed HC(7,4)-based dual RDH, but in terms of performance, it showed a weakness that did not reach the performance of the previously proposed RDH. In this paper, we propose a dual RDH scheme using Hamming code and OPAP. As previously suggested by Jana et al. The performance of dual RDH of HC(7,4) has been greatly improved. In this paper, HC(3,1) is used to significantly improve data hiding capacity and image quality, i.e., since this method conceals 2-bit secret data in 3 pixels using the OPAP method and the Hamming code, the embedding rate is 1.0 bpp and the average of the two marked images is measured as 51 dB. With the efficiency of the proposed coding method, it is possible to maintain high image quality. As a result of the experiment, our proposed data hiding capacity greatly outperformed the method proposed by Jana et al., and compared to the existing methods, it was almost similar in capacity and showed good performance in terms of image quality. © 2023 Elsevier Ltd

关键词： Pixels

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Modelling Sensors Degradation for Water Quality Monitoring: A Data-Driven Approach

Modelling Sensors Degradation for Water Quality Monitoring: ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Marino Pavone Nicola Epicoco Giordano Pola Andrea Manno Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L’Aquila L’Aquila Italy Department of Engineering University LUM Giuseppe Degennaro Casamassima Bari Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Sensors fault is a critical issue for any kind of application. Abrupt sensor fault are frequent, but the most common situation shows sensors that slowly vary their sensing capabilities along time, generating the so-called Concept Drift (CD). CD handling techniques are based on the possibility to collect new supervised data and estimate the drift effect according to their time variation. The Water Quality Monitoring (WQM) field is affected by the issue since electrochemical sensors degradation is common due to corrosion agents and the difficulties in periodically reaching each node of the WQM sensor network for proper maintenance. This paper discusses a possible approach for CD handling in WQM by using Machine Learning (ML) classifiers as "fake-supervisors" for labeling incoming new data. To this aim, the classifier must be reliable inside a certain neighborhood of the original training dataset centroids. Hence, by collecting data through a commercial WQM sensor, we simulate the aging process by means of an electrochemical corrosion model. Different types of corrosion trends are considered. The performances drop of three popular ML classifiers (namely Multy-Layer Perceptron Neural Networks, K-Nearest Neighbors, and Decision Trees) are compared when both raw data and data processed with PCA transformation are evaluated. Experimental results show that Neural Networks appear more robust to data perturbation, moreover the use of a PCA pre-processing step have a considerable positive effect in the model performance degradation trend.

关键词： Degradation Accuracy Perturbation methods Corrosion Neural networks Water quality Feature extraction Data models Sensors Principal component analysis

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Learning Job Title Representation from Job Description Aggregation Network

arXiv

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arXiv 2024年

作者： Laosaengpha, Napat Tativannarat, Thanit Piansaddhayanon, Chawan Rutherford, Attapol Chuangsuwanich, Ekapol Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Center of Excellence in Computational Molecular Biology Faculty of Medicine Chulalongkorn University Thailand Department of Linguistics Faculty of Arts Chulalongkorn University Thailand

Learning job title representation is a vital process for developing automatic human resource tools. To do so, existing methods primarily rely on learning the title representation through skills extracted from the job description, neglecting the rich and diverse content within. Thus, we propose an alternative framework for learning job titles through their respective job description (JD) and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss to account for the bidirectional relationship between the job title and its description. We evaluated the performance of our method on both in-domain and out-of-domain settings, achieving a superior performance over the skill-based approach. © 2024, CC BY.

关键词：

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Numerical Simulation of Full-Scale Square Concrete Filled Steel Tubular (CFST) Columns Under Seismic Loading

Numerical Simulation of Full-Scale Square Concrete Filled St...

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International Conference on Modern Mechanics and Applications, ICOMMA 2020

作者： Phan, Hao D. Lin, Ker-Chun Phan, Hieu T. Faculty of Civil Engineering The University of Danang – University of Science and Technology Danang Viet Nam Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan National Center for Research on Earthquake Engineering Taipei Taiwan Department of Computer Science and Software Engineering Miami University OxfordOH United States

ISBN: (纸本)9789811632389

This paper presents numerical investigations on the seismic behavior of full-scale square concrete filled steel tubular (CFST) columns. The main objective is to understand the seismic behavior and evaluate the seismic performance of these composite columns under high levels of axial compression. Finite element analysis (FEA) models in ABAQUS software were used to simulate a series of columns subjected to axial compression and cyclic lateral loading. The CFST columns were modeled using eight-node reduced integration brick elements (C3D8R) for the infilled concrete with confinement effect, and four-node reduced integration shell elements (S4R) for the steel tube with consideration of steel-concrete interaction and steel wall’s buckling. The feasibility of the FEA models has been validated by published experimental results. The validated FEA model was further extended to conduct parametric studies with various parameters including width-to-thickness ratio (B/t), concrete strength, and axial compression level. The numerical analysis results reveal that with the same B/t and constituent materials, the higher the axial compression was, the lower the shear strength and the deformation capacity were. Also, the higher axial compression led to earlier local buckling of the steel tube, especially, in the case of the thinner steel wall (B/t of 41.7). The thicker steel wall (B/t of 20.8) resulted in higher strength and larger deformation capacity of the column. Increasing concrete material strength significantly improved the column’s shear strength for both thinner and thicker steel walls, but it led to significant development in deformation for the column having thicker steel walls. This study also reveals that only the square CFST columns with B/t of 20.8 using medium material strengths satisfy the seismic performance demand for the building columns in high seismic zones (ultimate interstory drift ratio (IDRu) not less than 3% radian) under high axial compression (up to 55% of

关键词： Concrete confinement effect Finite element analysis (FEA) model High axial compression Local buckling Seismic performance Square CFST columns Width-to-thickness ratio (B/t)

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Collision-Cones-Based Nonlinear-Controller for Obstacle-Avoidance with Improved Control Gains

Collision-Cones-Based Nonlinear-Controller for Obstacle-Avoi...

引用

Electromobility (ISEM), International Symposium on

作者： Riccardo Cespi Stefano Di Gennaro School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L’Aquila L’Aquila Italy

ISBN: (数字)9798350354676

ISBN: (纸本)9798350354683

Recent studies on autonomous vehicles have shown that collision avoidance control strategies, based on collision cones techniques, present favorable results when ensuring driving safety in controlled scenarios. In this paper, authors present a collision avoidance control method, based on the strategy of collision cones, in which the control gains of the nonlinear Lyapunov candidate functions are dynamically determined according to the ‘time-till-collision-occurs’, ensuring the avoiding maneuver to converge in a specific laps time. Moreover, the nonlinear vehicle plant model, introduced making use of a rotational transformation from inertial to non-inertial reference frame, considers the non-negligible gyroscopic term. Quality and performances of both braking and steering avoidance maneuvers are tested by using a vehicle dynamics simulator, CarSim ® , with an extended ADAS sensors package, to validate the proposed control approach on a full-scale and unknown mathematical model, even in presence of unmodelled dynamics.

关键词： Optimal control Mathematical models Sensors Safety Vehicle dynamics Collision avoidance Autonomous vehicles

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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