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Curiosity and Affect-Driven Cognitive Architecture for HRI

IEEE Transactions on Affective computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

arXiv

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arXiv 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin School of Informatics Xiamen University Fujian China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Department of Electrical Engineering University at Buffalo-SUNY NY United States Department of Electrical and Computer Engineering Western University ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Optimal power flow solution using novel optimization technique: A case study

引用

Expert Systems with Applications 2025年 287卷

作者： Gupta, Saket Bilal, Mohd Zuhaib, Mohd Srivastava, Laxmi Malik, Hasmat García Márquez, Fausto Pedro Afthanorhan, Asyraf Instrumnetation and Control Engineering Department Bharati Vidyapeeth's College of Engineering New Delhi110063 India SND College of Engineering and Research Center Yeola Pune India Department of Electrical Engineering GLA University Mathura India Department of Electrical Engineering MITS Gwalior India Johor Skudai81310 Malaysia Ingenium Research Group Department Universidad Castilla-La Mancha Ciudad Real13071 Spain Terengganu Kuala Nerus21300 Malaysia Dehradun248002 India Faculty of Computing and Information Technology Sohar University Oman

The Jaya algorithm is a novel and effective global optimization technique, yet it faces challenges such as slow convergence, limited exploitation capabilities, and suboptimal balance between exploration and exploitation. This research proposes a hybrid Jaya algorithm, designated HMJDE, that integrates a modified Jaya algorithm with Differential Evolution (DE) to address the Optimal Power Flow (OPF) problem. The modified Jaya algorithm facilitates global exploration of the search space, while DE enhances local search by refining solutions within the neighbourhoods identified by Jaya. The performance of HMJDE is first assessed using eight well-established benchmark functions. Subsequently, its effectiveness is validated through application to real-world OPF problems on the Algerian 59-bus and standard IEEE 57-bus systems. The HMJDE algorithm achieves minimum fuel costs of 1688.1210 $/h and 41656.0438 $/h, respectively, with standard deviations of 0.3295 and 0.2412, representing cost reductions of 13.1317% and 18.8753% compared to the base case. These translate to annual savings of approximately $2,235,922.55 and $84,903,463.50, respectively. Compared to other recent optimization techniques, HMJDE exhibits superior solution quality and computational efficiency for both benchmark functions and OPF applications. Statistical analysis, including best, worst, mean, and standard deviation metrics, further confirms that HMJDE is a robust and reliable optimization approach. © 2025 The Author(s)

关键词： Evolutionary algorithms

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A new model to correlate solubility of drug compounds in supercritical carbon dioxide

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Chemical Thermodynamics and Thermal Analysis 2025年 18卷

作者： Raghavan, Amrithaa Alwi, Ratna Surya M, Geetha Devi Garlapati, Chandrasekhar Centre for Pollution Control and Environmental Engineering Pondicherry University Puducherry 605014 India Research Center for Computing National Research and Innovation Agency (BRIN) Jl. Raya Jakarta-Bogor KM 46 Cibinong Indonesia Department of Mechanical & Industrial Engineering College of Engineering National University of Science & Technology Oman Department of Chemical Engineering Puducherry Technological University Puducherry 605014 India

A new solubility model based on enhancement factor concept is proposed to correlate solubility of drug compounds in supercritical carbon dioxide (SCCO2). The correlating ability of the new model is compared with exist... 详细信息

A new solubility model based on enhancement factor concept is proposed to correlate solubility of drug compounds in supercritical carbon dioxide (SCCO₂). The correlating ability of the new model is compared with existing solubility model. The proposed model and existing models were tested with twenty nine drug-supercritical carbon dioxide systems reported in the literature. The proposed model correlating performance is indicated in terms of absolute relative deviation percentage. Additionally, Akaike's Information Criterion (AIC) was used to assess the model's performance compared with existing solubility model. The results show the compared to existing solubility model, the proposed model accurately correlates the *** global absolute average relative deviation percentage for the existing model and proposed new model are 22.17 % and 16.11 % respectively. © 2025

关键词： Akaike's information criterion Enhancement factor Solubility Supercritical carbon dioxide

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PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport

arXiv

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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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Robust Maximum Power Point Tracking in PV Generation System: A Hybrid ANN-Backstepping Approach With PSO-GA Optimization

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Hussan, Umair Waheed, Ahsan Bilal, Hazrat Wang, Huaizhi Hassan, Mudasser Ullah, Inam Peng, Jianchun Hosseinzadeh, Mehdi Shenzhen University College of Mechatronics and the Control Engineering Shenzhen518060 China The University of Faisalabad Department of Electrical Engineering Faisalabad38000 Pakistan University of Tasmania School of Engineering HobartTAS7005 Australia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Macquarie University School of Computing Sydney Australia Duy Tan University School of Computer Science Da Nang Viet Nam

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions. © 1975-2011 IEEE.

关键词： Genetic algorithms

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud computing: An Algorithm-Level Review

arXiv

引用

arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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A domain decomposition strategy for natural imposition of mixed boundary conditions in port-Hamiltonian systems

arXiv

引用

arXiv 2025年

作者： de Jong, Sjoerd D.M. Brugnoli, Andrea Rashad, Ramy Zhang, Yi Stramigioli, Stefano Department of Microelectronics Delft University of Technology Delft Netherlands ICA Université de Toulouse ISAE–SUPAERO INSA CNRS MINES ALBI UPS Toulouse France Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China Robotics and Mechatronics Department University of Twente Netherlands

[Abstract] In this contribution, a finite element scheme to impose mixed boundary conditions without introducing Lagrange multipliers is presented for wave propagation phenomena described as port-Hamiltonian systems. The strategy relies on finite element exterior calculus and a domain decomposition to interconnect two systems with different causalities. The spatial domain is split into two parts by introducing an arbitrary interface. Each subdomain is discretized with a mixed finite element formulation that introduces a uniform boundary condition in a natural way as the input. In each subdomain the spaces are selected from a finite element subcomplex to obtain a stable discretization. The two systems are then interconnected together by making use of a feedback interconnection. This is achieved by discretizing the boundary inputs using appropriate spaces that couple the two formulations. The final systems includes all boundary conditions explicitly and does not contain any Lagrange multiplier. Each subdomain is integrated using an implicit midpoint scheme in an uncoupled way from the other by means of a leapfrog scheme. The proposed strategy is tested on three different examples: the Euler-Bernoulli beam, the wave equation and the Maxwell equations. Numerical tests assess the conservation properties of the scheme and the effectiveness of the methodology. © 2025, CC BY.

关键词： Lagrange multipliers

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Air Traffic Management and Communication over ATN/IPS for Future Datalink Communication

Air Traffic Management and Communication over ATN/IPS for Fu...

引用

2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Aydogan, Emre Ozmen, Sergun Cetek, Fulya Aybek Valdes, Rosa Maria Arnaldo Jurado, Raquel Delgado-Aguilera Fernandez, Angel Ernesto Carmona Miralles, Adrian Martinez Vendruscolo, Tommaso Bonelli, Stefano Delahaye, Daniel Chaimatanan, Supatcha Chen, Feng Hamzaoui, Raouf Eskişehir Technical University Faculty of Aeronautics and Astronautics Air Traffic Control Department Eskişehir Turkey Turkish Airlines Îstanbul Turkey Universidad Politėcnica de Madrid Department of Aerospace Systems Air Transport and Airports Madrid Spain Servicios Aeronáuticos Control y Navegación Madrid Spain Deep Blue srl Rome Italy OPTIM Lab Toulouse France De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9798331534738

The growing demand for air traffic presents challenges in air traffic management, making seamless gate-to-gate communication essential. Traditional radio frequency communication faces limitations such as weather dependency and frequency restrictions. To address these issues, data link communications have gained importance, using VHF channels, satellite systems, and ATN/IPS-based networks. This study introduces the ATMACA (Air Traffic Management and Communication Over ATN/IPS) protocol, an advanced context management framework for ATN/IPS, designed to enhance aviation communications. ATMACA integrates instant messaging and software-defined nodes to improve connectivity, session continuity, and mobility management across networks and devices. It ensures seamless user interaction, reduces pilot workload, and enhances flight safety through automated Air Traffic control (ATC) sector handoff in controller-Pilot Data Link Communications (CPDLC) and Data Link Initiation Capability (DLIC) applications. Another key innovation of the ATMACA framework is Green Route Operations (GRO), which enables real-time trajectory prediction and optimization. © 2025 IEEE.

关键词： Air traffic control

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POWER SYSTEM STATE ESTIMATION USING LINEAR-PROGRAMMING

引用

PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1978年第9期125卷 879-885页

作者： IRVING, MR OWEN, RC STERLING, MJH Department of Control Engineering G.M.S. Computing Ltd. Sheffield UK Department of Control Engineering University of Sheffield Sheffield UK

The problem of estimating the state variables from measurements in an electric-power system is considered. The conventional linearised least-squares solution is shown to be ineffective in the presence of gross measurement errors. Reformulating the problem as a linear program leads to a state estimator that combines the advantages of noise filtering and bad-data elimination, and may be implemented straightforwardly by application of the simplex method. The solution of various examples based on three test networks confirms the advantages of the method especially where the data are corrupted by a number of gross errors. Depending on the degree of redundancy in the measurement set, the computational requirements of the method are comparable with conventional least-squares solution. For real-time power-system monitoring and control where process variables have unknown statistics, the linear-programming method is believed to be more efficient than conventional algorithms.

关键词： simplex method Network and transmission line calculations Optimisation linear programming power system control Optimisation techniques state estimation Simulation, modelling and identification Power engineering computing control of electric power systems power system analysis computing

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