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arXiv 2022年

作者： Lu, Sha Xu, Xuecheng Yin, Huan Chen, Zexi Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density. Copyright © 2022, The Authors. All rights reserved.

关键词： Radon

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A method for clustering scientific publications based on natural language for the purpose of strategic planning of partnerships in interdisciplinary research

A method for clustering scientific publications based on nat...

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Malika Shamgunova Andrii Biloshchytskyi Oleksandr Kuchanskyi Faizullin Adil Computer Science and Engineering Astana IT University Kazakhstan Technical Sciences Professor Vice-Rector for Science and Innovation Astana IT University Kazakhstan Department of Information Technologies Kyiv National University of Construction and Architecture Ukraine Department of Computational and Data Science Astana IT University Kazakhstan Department of Information Control Systems and Technologies Uzhhorod National University Ukraine Strategy and Corporate Governance Department Astana IT University Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The current research aims at examining the Kazakhstani scholars’ publications in order to reveal the topdiscussed topics and possible cooperation topics. By employing natural language processing, the research abstracts are grouped thematically to demonstrate the various clusters of themes present. The results reveal specific themes of the Kazakhstani research to provide the recommendations for the future academic development of the country and the cooperation between various fields. Thus, the approach illustrated the benefit of using thematic clustering for analysis and categorization of a vast amount of scholarly production.

关键词： Productivity Workability Data analysis Collaboration Strategic planning Natural language processing Information systems

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Explicit Time-Optimal Speed Profiles for Planar Paths with Monotone Curvature

arXiv

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arXiv 2022年

作者： Selvaratnam, Daniel Cantoni, Michael Manzie, Chris Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical and Electronic Engineering The University of Melbourne ParkvilleVIC3010 Australia

Minimum-time speed profiles are constructed for planar paths with smooth strictly-monotonic signed curvature, subject to constraints on velocity, normal acceleration and tangential acceleration. The construction is explicit and exact, and global optimality is rigorously established from first principles under mild regularity conditions on the path. Free, fixed, and inequality-constrained boundary speeds are all accommodated. Numerical implementation is straightforward. © 2022, CC BY-NC-ND.

关键词： Acceleration

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Modelling and Experimental Characteristics of Photovoltaic Modules in Typical Days at an Actual Photovoltaic Power Station 4

Modelling and Experimental Characteristics of Photovoltaic M...

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4th IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2021

作者： Lodhi, Ehtisham Yang, Pu Wang, Lina Lodhi, Zeeshan Khan, M. Adil Muhammad, Sijjad Tamir, Tariku Sinshaw Beihang University School of Automation Science and Electrical Engineering Beijing100191 China COMSATS University Islamabad Department of Electrical and Computer Engineering Abbottabad Campus 22060 Pakistan Chang'An University Department of Computer and Technology Xi'an710062 China Real Engineering Services Deans Trade Center Peshawar25000 Pakistan Institute of Automation Chinese Academy of Sciences The SKL For Management and Control of Complex Systems Beijing100190 China

ISBN: (纸本)9781665428392

Solar energy among the renewable energy sources (RES) plays an important role in fulfilling any country's energy demands. When the sunlight irradiates the photovoltaic (PV) modules, part of the light energy is directly converted into electric energy. Another part is converted into heat energy, which increases the working temperature and affects the output performance characteristics of the PV module. This paper proposes a dynamic mathematical model of PV modules working on solar irradiance and ambient temperature by calculating the PV module model parameters under different environmental conditions. The dynamic model of the PV module is suggested to simulate PV modules' output characteristics (voltage and current) under any environmental conditions. By employing MATLAB/Simulink, a PV output characteristics program is compiled. The whole day output characteristics of PV strings of a real PV power station are tested and compared with simulation results. The results depict that the established model can effectively reflect PV modules' output characteristics in actual operation and can be used for dynamic simulation of PV power generation. © 2021 IEEE.

关键词： Solar energy

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Platoon-Local Dynamic Map: Hierarchical cooperative perception for platooning vehicles

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IEEE Transactions on Vehicular Technology 2025年

作者： Carletti, Carlos Mateo Risma Casetti, Claudio Harri, Jerome Risso, Fulvio Politecnico di Torino Department of Control and Computer Engineering Turin Italy EURECOM - Communication Systems Department Sophia-Antipolis France

Onboard perception systems found on modern vehicles generate data that are incredibly rich in contextual information, and thanks to the increasing number of vehicles equipped with communication capabilities, the valuable data generated can be shared with nearby vehicles. One of the most studied vehicular applications for autonomous driving is platooning, for which all maneuvers are managed by the Platoon Leader (PL) with the aid of context information available through Vehicle-to-Vehicle (V2V) messages. However, redundant context information from nearby vehicles in the platoon creates an additional workload for the platoon leader resulting in elevated computational costs. To overcome the challenge of redundant context information, the formation of vehicular micro-clouds can offload the platoon leader by enabling vehicles in a cluster to more effectively utilize the available context data through collective data processing and aggregation. The proposed solution, called Platoon Local Dynamic Map (P-LDM), creates a single database of context information, distributing the data aggregation load among all members of the platoon. Simulation results evaluate the collective perception enhancement and the distribution of the computational load, comparing the proposed scheme with usual Cooperative Perception mechanisms. © 1967-2012 IEEE.

关键词： Data reduction

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

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2022 Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 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： Principal component analysis

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OA-ECBVC: A Cooperative Collision-free Encirclement and Capture Approach in Cluttered Environments

arXiv

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arXiv 2023年

作者： Wang, Xinyi Ding, Yulong Chen, Yizhou Han, Ruihua Xi, Lele Chen, Ben M. The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong The Department of Control Science and Engineering Tongji University Shanghai China The Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China Department of Computer Science The University of Hong Kong Hong Kong Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms. Copyright © 2023, The Authors. All rights reserved.

关键词：

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Application of Extreme Learning Machine for Shunt Faults Detection and Classification in Three-phase Transmission Line systems

Application of Extreme Learning Machine for Shunt Faults Det...

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Signal, control and Communication (SCC), International Conference on

作者： Khaoula Assadi Jihane Ben Slimane Salah Salhi University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis Tunisia Department of Computer Sciences Faculty of Computing and InformationTechnology Northern Border University Rafha Saudi Arabia

ISBN: (数字)9798350326390

ISBN: (纸本)9798350326406

The electrical power transmission lines play a crucial role in maintaining a continuous electricity supply. However, the exposed environment of these lines increases the risks of faults occurrence, necessitating prompt detection, and classification, to eliminate them within a specified time. In light of this, the present study introduces a methodology for the detection and classification of faults in a three-phase transmission line system employing the Extreme Learning Machine (ELM). A 220 kV, 300 km three-phase transmission line system was simulated in MATLAB/Simulink software, where the current and voltage signals were generated under different fault conditions including fault types, fault inception time, and fault resistance. The fundamental components of these signals were used as inputs in the ELM model. Different shunt fault types, including line-to-ground, line-to-line, double-line-to-ground, triple-line, and triple-line-to-ground were applied for fault detection and classification framework. Furthermore, the optimum number of hidden neurons in the ELM model was investigated in this study. In terms of the Mean Squared Error (MSE) and correlation coefficient, four ELM variants based on Sine, Sigmoid, Radial Basis, and Triangular Basis transfer functions were assessed for each module. The outcome of these metrics indicates that the ELM with the sigmoid model demonstrated superior performance compared to the other ELM models. Based on statistical metrics MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), and also the computational time, the efficiency of ELM-Sigmoid and state-of-the-art methods of Multi-Layer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) were compared and analyzed during training and testing phases. The results obtained revealed that the ELM-Sigmoid model outperforms the other models in the detection and classification of shunt faults with significant accuracy and fast computational speed.

关键词： Measurement Training Power transmission lines Extreme learning machines Computational modeling Fault detection Neurons

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PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

arXiv

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arXiv 2023年

作者： Feng, Chen Li, Haojia Gao, Fei Zhou, Boyu Shen, Shaojie School of Artificial Intelligence Sun Yat-Sen University Zhuhai China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Industrail Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China

Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://***/HKUST-Aerial-Robotics/PredRecon. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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