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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Guolong Liu Yan Bai Jinjie Liu Huan Zhao Gaoqi Liang Junhua Zhao Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China

Smart grids have rapidly developed in recent years, and a large amount of data has been collected and become the basis for decision-making of power grid operation. To ensure the safe and stable operation of the power grid, how to improve the data quality of the collected data has become an important research topic. Therefore, an attention mechanism-based autoencoder is proposed in this paper for missing data recovery to improve data quality in smart grids. The proposed method named missing data recovery autoencoder (MDRAE) contains two parts of an encoder and a decoder, and the attention mechanism is integrated into the encoder part to better extract the key information of the data. The effectiveness of the proposed method is verified by conducting experiments on two datasets including industrial load data and residential load data. The experimental results show that the model performance of the proposed method is better than that of the two benchmarks, and the proposed method can recover the missing data accurately.

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Optimization Model of Housing Construction Project Scheduling Based on Optimized Ant Colony Algorithm 2

Optimization Model of Housing Construction Project Schedulin...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasa, Gowda G.K. Krishna, G.R. Sanjay Rex, J. Kumar, K. Sambath Kumar, Madhan Mohan Manikandan, N. Department of Computer Science and Engineering ACS College of Engineering Tamilnadu Bangalore India Department: Mechanical Engineering Koneru Lakshmaiah Education foundation Green fields Andhra Pradesh Vaddeswaram India Department of Civil Engineering Malla Reddy Engineering College Tamilnadu Hyderabad India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Robotics and Automation Easwari Engineering College Bharathi salai Ramapuram Tamilnadu Chennai India Dept of Mechanical Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798350305418

Construction management is a standardized means of project management. The construction project takes a long time and has many links, and the construction workers involved are complicated. Poor quality buildings will not only affect the construction enterprises’ brand, but also threaten the safety of people's lives and property. In this paper, an optimization model of housing construction project scheduling based on ant colony algorithm (ACA) is put forward. By studying the relationship among various sub-projects in the construction system of building projects, the management of building projects can be better realized. The results show that compared with the other two algorithms, the optimized ACA has certain advantages in recall and accuracy. There are many contents involved in the building construction process, and there are many uncontrollable factors in the building process, which greatly impact the project's construction quality. The construction industry can use this technology to strengthen the building construction management, solve the problems existing in the construction management, and improve the construction quality. © 2023 IEEE.

关键词： ant colony algorithm Building engineering Construction supervision Optimization Model Project scheduling

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Dynamic Identification for a Manipulator Model based on Stribeck Friction using the Quasi-Newton Iterative Method

Dynamic Identification for a Manipulator Model based on Stri...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiao, Feng Zhang, Feilong Han, Bing Zhang, Hualiang Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Mechanical Engineering Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310027 China

ISBN: (纸本)9798350325706

The performance of dynamic control is intimately tied to modeling accuracy. However, traditional estimation methods and friction models, such as the least squares method and the Coulomb plus viscous model, fail to reflect the actual characteristics accurately. Particularly, the linear nature of the Coulomb plus viscous model overlooks the nonlinear static features of joint friction at slower velocities. To improve the rationality of the model structure, we integrate the Stribeck friction model into the Coulomb plus viscous model. However, introducing such nonlinearities compromises the applicability of the least squares method. As a countermeasure, we propose a new strategy that combines the least square method and the Quasi-Newton iterative method to identify the parameters of the modified nonlinear model. Additionally, the design of the excitation trajectory is critical to achieve high identification accuracy. We utilized the inverse of the smallest singular value of the observation matrix as the objective function. By minimizing it with the interior point method, we generate the excitation trajectory well-suited to stimulate dynamic characteristics. Then we leverage the discrepancies between the measured and estimated torques to assess the precision of the dynamic parameters of the manipulator. Remarkably, our proposed algorithm reduces the mean absolute error of the estimated torque by over 20.40%. Finally, an experiment of the industrial manipulator by hand guiding grab and drag is performed and shows that the proposed approach can provide the manipulator with comprehensive torque compensation. © 2023 IEEE.

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A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

arXiv

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arXiv 2024年

作者： Tao, Zeng Wang, Yan Lin, Junxiong Wang, Haoran Mai, Xinji Yu, Jiawen Tong, Xuan Zhou, Ziheng Yan, Shaoqi Zhao, Qing Han, Liyuan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn’t yield exceptional results as observed in other CLIP-based classification tasks. While CLIP’s primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, making label representation limited and perfect alignment difficult. To address this issue, we have designed A3lign-DFER, which introduces a new DFER labeling paradigm to comprehensively achieve alignment, thus enhancing CLIP’s suitability for the DFER task. Specifically, our A3lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional. We replace the input label text with a learnable Multi-Dimensional Alignment Token (MAT), enabling alignment of text to facial expression video samples in both affective and dynamic dimensions. After CLIP feature extraction, we introduce the Joint Dynamic Alignment Synchronizer (JAS), further facilitating synchronization and alignment in the temporal dimension. Additionally, we implement a Bidirectional Alignment Training Paradigm (BAP) to ensure gradual and steady training of parameters for both modalities. Our insightful and concise A3lign-DFER method achieves state-of-the-art results on multiple DFER datasets, including DFEW, FERV39k, and MAFW. Extensive ablation experiments and visualization studies demonstrate the effectiveness of A3lign-DFER. The code will be available in the future. © 2024, CC BY.

关键词： Alignment

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Learning to Schedule Srcpsp with Uncertain Resource Capacity Based on Graph Neural Network and Reinforcement Learning

SSRN

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SSRN 2022年

作者： Cai, Hongxia Bian, Yunqi Liu, Lilan Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai 200444 China

The resource-constrained project scheduling problem (RCPSP) is encountered in many fields, including manufacturing, supply chain, construction. Owing to dynamic behavior in real-world environments, various dynamic factors were added to deterministic RCPSP, leading to the various stochastic RCPSP (SRCPSP). Although many approaches for solving SRCPSPs have been proposed in recent years, few studies have focused on a graph neural network (GNN) method. To automatically learn the policy to schedule SRCPSP with uncertain resource availability, in this study a framework based on GNN and reinforcement learning (RL) was proposed. The scheduling process was formulated as a sequential decision-making problem, and based on that, a Markov decision process (MDP) model was formulated. To make scheduling decisions based on different status of the problem, a GNN-based structure was proposed to extract useful features from problems and map them to probability distributions of which activity to start by policy network. To optimize the scheduling policy, proximal policy optimization (PPO) was used to train the model end-to-end. Computational results on benchmark instances show that the GNN algorithm achieves competitive performance in a reasonable computation time compared with some widely used methods. © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Pipeline Detection and Reconstruction Based on Local Features

Pipeline Detection and Reconstruction Based on Local Feature...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Deng, Zhenchun Song, Yifeng Han, Hui Shenyang LiGong University School of Mechanical Engineering Liaoning Shenyang110159 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institute of Robotics and Intelligent Manufacturing Innovation Chinese Academy of Sciences Liaoning Shenyang110169 China

ISBN: (纸本)9781665481090

Aiming at the difficulty of detecting and reconstructing large-area and high-similar texture regions in pipelines, a local feature stereo matching method combined with the principle of region growing is proposed, and dense reconstruction is realized by reprojection matrix. First, features are extracted from the preprocessed image and matched, and then the exact match point pair is obtained through screening. After that, we use the obtained matching pairs as seed pairs. According to the principle of parallax continuity and the criterion of stereo vision limit constraint, the search area is delimited. For the obtained parallax, we use the WSL filter to filter the parallax. Finally, the depth and 3D coordinates are calculated according to the parallax to reconstruct the 3D model of the pipeline. Experimental results show that the proposed method has good adaptability to texture features in pipeline detection and reconstruction. Therefore, it can provide a feasible basis for pipeline inspection and maintenance. © 2022 IEEE.

关键词： Pipelines

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An Interaction Control Framework for Regular Aerial Manipulators in Push-and-slide Operations on Moving Objects

An Interaction Control Framework for Regular Aerial Manipula...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Meng, Xiangdong He, Yuqing Xi, Haoyang Han, Jianda Song, Aiguo The 58th Research Institute of China Electronics Technology Group Corporation Wuxi214035 China School of Instrument Science and Engineering Southeast University Nanjing210096 China Shenyang Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Robotics Shenyang110016 China The College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798350316308

Aerial manipulators (AMs) have evolved to fulfill various interaction tasks, such as push-and-slide tasks. However, a push-and-slide operation on a moving object is still challenging for a regular AM because its flying platform is usually a rotorcraft unmanned aerial vehicle (UAV)-a floating base with complex aerodynamics, under-actuation property, body vibration, etc. This paper proposes an easy-to-implement approach-an interaction control framework based on a remodeling idea of a closed-loop system, allowing an AM to steadily transition from a free flight to a contact flight and slide along a moving object. Then, a decoupled control idea is applied to implement this framework on an AM system. Besides, a contact model and a trajectory planning issue in application scenarios are also studied. Finally, several push-and-slide operation experiments evaluate the proposed interaction control framework. The experimental process and results validate its effectiveness. © 2023 IEEE.

关键词： Antennas

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. This work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. The method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. These results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. The success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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A Family of Deployable Single-Loop Metamorphic Mechanisms for Aerospace Manipulators: Synthesis, Network, and Analysis

SSRN

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SSRN 2024年

作者： Zhang, Yang Kang, Xi Li, Bing State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China

Guided by predetermined degrees of freedom for different desired tasks, the type synthesis of single-loop metamorphic mechanisms (SLMMs) has long posed a challenge. Owing to the complexity and coupling of their overconstraints and the variability of constraints arising from bifurcation motions, it is difficult to ensure geometric coordination and synchronous motion for a network of multiple SLMMs. Here, we propose an approach to type synthesize SLMMs using the atlas method. A novel family of SLMMs is further synthesized. The mobility analysis of a selected 7R SLMM is carried out via screw theory. The kinematics and bifurcation phenomena are analyzed and verified through numerical simulation using computer-aided design models. To evaluate the performance of the SLMMs, the mapping relationship between key design parameters and performance indicators, such as the grasping angle, folding angle, and mechanical advantage, is studied. A networking method is then proposed by considering the geometric coordination conditions and variation of the overconstraints of SLMMs. Prototypes of an SLMM and manipulator with optimal geometrical parameters are developed to verify the feasibility and correctness of the proposed method and kinematic analysis. © 2024, The Authors. All rights reserved.

关键词： Manipulators

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