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Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

Journal of Bionic Engineering 2024年第2期21卷 1022-1054页

作者： Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Xin Zhang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Biomedical Engineering National Engineering Research Center of Ophthalmology and OptometryEye HospitalWenzhou Medical UniversityWenzhou325027China Key Lab of Biohealth Materials and Chemistry of Wenzhou Wenzhou UniversityWenzhouChina Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhouChina

Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simulation and management of PV *** accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method,*** experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and ***,to verify the feasibility and competitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for *** addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results *** evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other *** addition,the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and *** conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV *** that the source code of INMVO is available at https://***/woniuzuioupao/INMVO.

关键词： Photovoltaic models Multi-verse optimizer Nelder-Mead simplex Iterative chaos map

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Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning

Skeleton-in-Context: Unified Skeleton Sequence Modeling with...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Xinshun Wang Zhongbin Fang Xia Li Xiangtai Li Chen Chen Mengyuan Liu Sun Yat-sen University National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Department of Computer Science ETH Zurich S-Lab Nanyang Technological University Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning. However, skeleton sequence modeling via in-context learning remains unexplored. Directly applying existing in-context models from other areas onto skeleton sequences fails due to the similarity between inter-frame and cross-task poses, which makes it exceptionally hard to perceive the task correctly from a subtle context. To address this challenge, we propose Skeleton-in-Context (SiC), an effective framework for in-context skeleton sequence modeling. Our SiC is able to handle multiple skeleton-based tasks simultaneously after a single training process and accomplish each task from context according to the given prompt. It can further generalize to new, unseen tasks according to customized prompts. To facilitate context perception, we additionally propose a task-unified prompt, which adaptively learns tasks of different natures, such as partial joint-level generation, sequence-level prediction, or 2D-to-3D motion prediction. We conduct extensive experiments to evaluate the effectiveness of our SiC on multiple tasks, including motion prediction, pose estimation, joint completion, and future pose estimation. We also evaluate its generalization capability on unseen tasks such as motion-in-between. These experiments show that our model achieves state-of-the-art multi-task performance and even outperforms single-task methods on certain tasks.

关键词： Training Solid modeling Three-dimensional displays Silicon carbide Computational modeling Pose estimation Predictive models

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Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data

arXiv

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arXiv 2024年

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract normal representations in its latent space. During test time, since AE is not trained using real anomalies, it is expected to poorly reconstruct the anomalous data. However, several researchers have observed that it is not the case. In this work, we propose to limit the reconstruction capability of AE by introducing a novel latent constriction loss, which is added to the existing reconstruction loss. By using our method, no extra computational cost is added to the AE during test time. Evaluations using three video anomaly detection benchmark datasets, i.e., Ped2, Avenue, and ShanghaiTech, demonstrate the effectiveness of our method in limiting the reconstruction capability of AE, which leads to a better anomaly detection model. © 2024, CC BY-NC-SA.

关键词： Anomaly detection

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Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems

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Journal of Bionic Engineering 2023年第2期20卷 819-843页

作者： Sushmita Sharma Nima Khodadadi Apu Kumar Saha Farhad Soleimanian Gharehchopogh Seyedali Mirjalili Department of Mathematics National Institute of Technology AgartalaTripura799046India Department of Civil and Environment Engineering Florida International University MiamiMiamiFL33199USA Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia***Iran Centre for Artificial Intelligence Research and Optimization Torrens UniversityFortitude ValleyQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea

This paper uses the Butterfly Optimization Algorithm(BOA)with dominated sorting and crowding distance mechanisms to solve multi-objective optimization *** is also an improvement to the original version of BOA to alleviate its drawbacks before extending it into a multi-objective *** to better coverage and a well-distributed Pareto front,non-dominant rankings are applied to the modified BOA using the crowding distance *** benchmark functions and eight real-world problems have been used to test the performance of multi-objective non-dominated advanced BOA(MONSBOA),including unconstrained,constrained,and real-world design multiple-objective,highly nonlinear constraint *** performance metrics,such as Generational Distance(GD),Inverted Generational Distance(IGD),Maximum Spread(MS),and Spacing(S),have been used for performance *** is demonstrated that the new MONSBOA algorithm is better than the compared algorithms in more than 80%occasions in solving problems with a variety of linear,nonlinear,continuous,and discrete characteristics based on the Pareto front when compared *** all the analysis,it may be concluded that the suggested MONSBOA is capable of producing high-quality Pareto fronts with very competitive results with rapid convergence.

关键词： Multi-objective problems Butterfly optimization algorithm Non-dominated sorting Crowding distance

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StaR Maps: Unveiling Uncertainty in Geospatial Relations

StaR Maps: Unveiling Uncertainty in Geospatial Relations

引用

International Conference on Intelligent Transportation

作者： Benedict Flade Simon Kohaut Julian Eggert Devendra Singh Dhami Kristian Kersting Honda Research Institute Europe GmbH Offenbach Germany Department of Computer Science Artificial Intelligence and Machine Learning Lab TU Darmstadt Darmstadt Germany Department of Mathematics and Computer Science Uncertainty in Artificial Intelligence Group TU Eindhoven Eindhoven MB Netherlands Hessian AI Centre for Cognitive Science German Center for Artificial Intelligence (DFKI)

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is essential to understand their inherent uncertainties, which are commonly underrepresented by the respective geographic information systems. Hence, it is critical to develop a representation that combines a statistical, probabilistic perspective with the relational nature of geospatial data. Further, such a representation should facilitate an honest view of the data's accuracy and provide an environment for high-level reasoning to obtain novel insights from task-dependent queries. Our work addresses this gap in two ways. First, we present Statistical Relational Maps (StaR Maps) as a representation of uncertain, semantic map data. Second, we demonstrate efficient computation of StaR Maps to scale the approach to wide urban spaces. Through experiments on real-world, crowd-sourced data, we underpin the application and utility of StaR Maps in terms of representing uncertain knowledge and reasoning for complex geospatial information.

关键词： Vocabulary Uncertainty Navigation Semantics Urban areas Stars Probabilistic logic Cognition Geospatial analysis Computational efficiency

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Robotic Approach Trajectory Using Reinforcement Learning with Dual Quaternions

Robotic Approach Trajectory Using Reinforcement Learning wit...

引用

Iberian Robotics Conference (ROBOT)

作者： Daniel Frau-Alfaro Santiago T. Puente Ignacio De Loyola Páez-Ubieta Edison Velasco-Sánchez AUtomatics RObotics and Artificial Vision Lab University Institute for Computer Research San Vicente Spain University Institute for Engineering Research University Miguel Hernández Elche Spain

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Manipulation tasks in robotics usually involve two phases: an approach to the object and the grasp itself. The first action allows the robot to reach a certain pose in space that is likely to allow the object to be manipulated. Reinforcement Learning (RL) techniques allow a policy to be learned through experience given a set of states and actions, so this is a powerful tool for developing controllers for specific tasks such as positioning the robot in a particular point in space. However, when manipulating an object, orientation is as relevant as position. For this reason, a method of RL for positioning the robot's end effector in a suitable position and orientation for manipulation in simulation is presented. This approach models the problem of computing the distance for the reward function using dual quaternions parameterisation, an element that can represent the pose and attitude of a rigid body in Euclidean space in a compact way without having to apply any constraints.

关键词： Training Quaternions Computational modeling Process control Reinforcement learning Aerospace electronics End effectors Trajectory Robots

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Masked AutoDecoder is Effective Multi-Task vision Generalist

Masked AutoDecoder is Effective Multi-Task Vision Generalist

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Han Qiu Jiaxing Huang Peng Gao Lewei Lu Xiaoqin Zhang Shijian Lu S-Lab Nanyang Technological University Shanghai Artificial Intelligence Laboratory Sensetime Research College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction. They apply uni-directional attention to capture sequential dependencies and generate task sequences recursively. However, such autoregressive Transformers may not fit vision tasks well, as vision task sequences usually lack the sequential dependencies typically observed in natural languages. In this work, we design Masked AutoDecoder (MAD), an effective multitask vision generalist. MAD consists of two core designs. First, we develop a parallel decoding framework that introduces bi-directional attention to capture contextual dependencies comprehensively and decode vision task sequences in parallel. Second, we design a masked sequence modeling approach that learns rich task contexts by masking and reconstructing task sequences. In this way, MAD handles all the tasks by a single network branch and a simple cross-entropy loss with minimal task-specific designs. Extensive experiments demonstrate the great potential of MAD as a new paradigm for unifying various vision tasks. MAD achieves superior performance and inference efficiency compared to autoregressive counterparts while obtaining competitive accuracy with task-specific models. Code will be released at https://***/hanqiu-hq/MAD.

关键词： Computational modeling Natural languages Transformer cores Predictive models Transformers Multitasking Decoding

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Dissecting OLMS membrane algorithms: understanding the role of communication and evolutionary operators in optimization strategies

Journal of Membrane Computing

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Journal of Membrane Computing 2025年 1-46页

作者： Andreu-Guzmán, José Antonio Orellana-Martín, David Valencia-Cabrera, Luis Research Group on Natural Computing Depart. of Computer Science and Artificial Intelligence Universidad de Sevilla Seville Spain SCORE Lab I3US Universidad de Sevilla Seville Spain

Metaheuristics are general-purpose optimization techniques designed to explore the solution space of complex problems, balancing between exploration and exploitation, trying to escape local optima. Some techniques are inspired by natural processes, such as simulated annealing, particle swarm optimization, or genetic algorithms. Membrane computing, a computational paradigm based on the behavior and the structure of living cells, has proved capable in solving computationally hard problems in an efficient way. From the intersection of both fields, the framework of membrane algorithms embeds metaheuristics as a way to evolve objects in a membrane system. A thorough study of this framework is presented in this work, deeply analyzing the mutual influence of a variety of strategies of membrane and genetic algorithms, enhancing their synergy in searching for optimal solutions. Specifically, this paper assesses the impact of aspects, such as communication rules, genetic operators or number of membranes, among others. All strategies are compared using well-known problems as Traveling Salesman Problem and Graph Coloring Problem, taken as a benchmark. The results show the best solutions are dependent on the specific problem addressed and the genetic algorithm used but, overall, a distributive send-in strategy is ideal for specializing membranes, allowing some to focus on exploration of the state space and others on exploitation of good solutions.

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Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications

arXiv

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arXiv 2024年

作者： Ibrahim, Sinan Mostafa, Mostafa Jnadi, Ali Salloum, Hadi Osinenko, Pavel Skolkovo Institute of Science and Technology Moscow121205 Russia Institute of Robotics and Computer Vision Innopolis University Innopolis420500 Russia Research Center for Artificial Intelligence Innopolis University Innopolis420500 Russia

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-making by learning through interaction with their environment. Central to this process are reward engineering and reward shaping, which are essential for enhancing the efficiency and effectiveness of RL algorithms. These techniques guide agents toward desired behaviors, improve learning stability, and accelerate convergence by addressing challenges such as sparse and delayed rewards. However, the complexity of real-world environments and the computational demands of RL algorithms remain significant obstacles to broader adoption. Recent advancements in deep learning have enabled RL to handle high-dimensional state and action spaces, facilitating applications in robotics, autonomous driving, and complex decision-making tasks. In response to these developments, this paper provides one of the first comprehensive reviews of reward design in RL, with a focus on the methodologies and techniques underpinning reward engineering and shaping. By introducing a detailed taxonomy, critically analyzing current approaches, and highlighting their limitations, this work fills an important gap in the literature, offering insights into how reward structures can be optimized to meet the growing demands of modern AI systems. © 2024, CC BY.

关键词： Adversarial machine learning

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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computer Systems Science & Engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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