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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Cao, Xunqi Li, Yan Yu, Kun Zhang, Yongchao School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (纸本)9798400710681

Today, relying solely on individual vibration signals as features, intelligent fault diagnosis of gearboxes can no longer satisfy the demands of complex systems. Based on multimodal information fusion methods, it has become the main direction for gearbox fault diagnosis. Thermal images are not only easy to capture during the production process, but also can intuitively reflect the thermal change characteristics generated by various gearbox components during mechanical operation. In order to achieve multi-source information fusion and improve the accuracy of gearbox fault detection, this paper aims to propose a multimodal gearbox fault diagnosis method based on vibration signals and thermal imaging. This method combines two data sources, vibration signals and thermal images, and utilizes the powerful feature extraction ability of convolutional neural networks (CNN) to comprehensively explore the deep features of gearbox fault information, improving the accuracy and reliability of fault diagnosis. Through experimental verification, the method proposed in this paper demonstrates excellent performance in fault diagnosis of helical gearboxes, with higher diagnostic accuracy compared to single vibration signal input and single thermal imaging input methods. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Plume Finding with Frontier-Based Planner 6

Plume Finding with Frontier-Based Planner

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Zhang, Xin-Kai Li, Ji-Gong Yang, Jing School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin Tianjin China School of Information Technology Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9798350356618

Toxic/hazardous gas leaks seriously jeopardize public safety and the ecological environment, and how to utilize drones with on-board gas sensors to quickly detect gas plumes at an early stage of a gas leak and take timely measures can effectively prevent the expansion of the accident. Traditional plume finding methods have the disadvantages of poor targeting, long search times, and repeated exploration in some areas. In view of this, this paper proposes a method of plume finding with frontier-based planner, where a heuristic function is designed to select optimal observation points and thus plan path to search plume. Here an air-mass path model is constructed to estimate the region that would likely be observed by the UAV at a candidate observation site on the frontier, and the area of the unknown part in the region is considered as the environmental exploration gain. The change in flight direction and the distance traveled by the UAV are used as environmental exploration costs when it will fly from its current pose to the candidate observation site. Simulation results show that the proposed frontier-based exploration method for plume finding has good performance in odor source localization tasks in outdoor environments. © 2024 IEEE.

关键词： Direct air capture

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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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Spatial and Temporal Evolution of Vegetation Based on Optical Flow Algorithms 15th

Spatial and Temporal Evolution of Vegetation Based on Optica...

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15th International Conference on Computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous information Technologies and Applications, CUTE 2023

作者： Mu, Ruolan Wang, Chuanlong Li, Han Liu, Chao School of Environmental and Municipal Engineering Qingdao University of Technology Qingdao266520 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9789819724468

Vegetation plays a critical role in ecological studies, and the identification of vegetation is a key focus of remote sensing vegetation monitoring, which is essential for applying remote sensing to the study of ecological environments. In order to accurately identify the vegetation, dynamically monitor changes in vegetation cover, and analyze its spatial and temporal evolution, this study uses Normalized Difference Vegetation Index (NDVI) for vegetation identification, and uses Convolutional Neural Networks (CNNs) combined with optical flow algorithms to visualize and analyze the identification results, which intuitively demonstrates the pattern of change of vegetation in different years. The results are as follows: The FlowNet2.0 model enables clear visualization of vegetation changes, and the feasibility of employing the optical flow algorithm for visualization purposes is confirmed through the analysis of vegetation increase and decrease status. This paper proposes a new method that combines CNNs and optical flow algorithm for visualizing vegetation changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical remote sensing

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A Random Fusion of Mix 3D and Polar Mix to Improve Semantic Segmentation Performance in 3D Lidar Point Cloud

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Computer Modeling in engineering & Sciences 2024年第7期140卷 845-862页

作者： Bo Liu Li Feng Yufeng Chen School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China School of Computer Science and Artificial Intelligence Chaohu UniversityChaohu238000China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive TechnologyShiyan442002China

This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network *** point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging *** augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization *** of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point ***,there has been a lack of focus on making the most of the numerous existing augmentation *** this deficiency,this research investigates the possibility of combining two fundamental data augmentation *** paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named *** of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or *** innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or *** crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data *** results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation *** is achieved without compromising computational *** examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point *** data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the ro

关键词： 3D lidar point cloud data augmentation RandomFusion semantic segmentation

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Kalman Filter Adaptive Speed Tracking control for High-speed Trains with Noise Interference 7

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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7th International Conference on Intelligent Robotics and control engineering, IRCE 2024

作者： Zhang, Chunli Xie, Yuwei Yan, Lei Qian, Fucai Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing College of Automation and Information Engineering Xi'an China

ISBN: (纸本)9798350352399

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively. © 2024 IEEE.

关键词： Kalman filters

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Unreliable phase removal and phase-unwrapping method based on modulation level 6

Unreliable phase removal and phase-unwrapping method based o...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Zhou, Cong Liu, Yang Qi, Jun Teng, Yunlong University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu China Chengdu University of Information Technology School of Control Engineering Chengdu China

ISBN: (纸本)9798350386974

In the field of structured light 3D reconstruction, unfolding the wrapped phase obtained by the phase shift method to get the absolute phase is crucial. The traditional process of unfolding phase row by row and column by column still has some limitations in terms of efficiency and accuracy. In this paper, we propose a method for unreliable phase removal and phase unwrapping based on modulation level. On one hand, it optimizes the existing modulation threshold selection method. On the other hand, it solves for the absolute phase according to the order of modulation values, effectively removing the phases of pixel points that are below the modulation threshold. © 2024 IEEE.

关键词： Phase modulation

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A Contact-Aided Model for Soft Continuum Robots Moving in Unstructured Environment

A Contact-Aided Model for Soft Continuum Robots Moving in Un...

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2024 China Automation Congress, CAC 2024

作者： Jingyu, Zhang Qin, Fang Mingjian, Sun Rong, Xiong Wang, Yue Haojian, Lu School of Information Science and Engineering Harbin Institute of Technology Weihai China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Control Science and Engineering Harbin Institute of Technology Heilongjiang China

ISBN: (纸本)9798350368604

Due to their inherent flexibility and high human-computer interaction potential, soft continuum robots have been developed and popularized in industrial and medical scenarios. However, when moving in an unstructured environment, the contact with environment will change the configuration parameters of the robot, which brings problems to the control. Aiming at improving the control performance of soft continuum robots in an unstructured environment, a contact-aided model is proposed to estimate the varying configurations of the robot. Besides, a model-based controller is proposed to control the robot in an unstructured environment. To verify the performance of the proposed method, a small-scale soft continuum robot is designed and fabricated. Through the characterization experiment, the robot shows a large workspace by changing the contact position, and the kinematic model is verified. Through the trajectory tracking experiment, the robot showed good accuracy, with an average error of 1.2mm. Through an exploration experiment in the unstructured environment, the robot showed good control effect, with an average error of 1.9mm. This work enhances the application capability of the soft continuum robots in unstructured environments. © 2024 IEEE.

关键词： Microrobots

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Coverage-Guided Testing for Automatic Speech Recognition Systems 11

Coverage-Guided Testing for Automatic Speech Recognition Sys...

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11th International Conference on Dependable Systems and Their Applications, DSA 2024

作者： Xu, Yang Yu, Tingting Chen, Rui Beijing Sunwise Information Technology Ltd. Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798331532390

Automatic Speech Recognition (ASR) systems have become essential in modern life, powering human-computer interactions and voice-activated virtual assistants. These systems, which predominantly rely on deep neural networks, exhibit a probabilistic nature that can lead to unpredictable and erroneous outputs, particularly in varied and dynamic real-world settings. Consequently, it is crucial to thoroughly test ASR systems to identify and resolve bugs that may impair functionality and user experience. Current works for ASR testing fall short in diversity and coverage. To address this, we propose a novel coverage-guided test method for ASR utilizing four neuron coverage criteria to guide the generation of diverse test data and ensure comprehensive testing across multiple dimensions. We evaluated the effectiveness of our method using three popular datasets and two DNN-based ASR systems. The results show that the generated data significantly improve coverage rates, enabling more comprehensive testing. © 2024 IEEE.

关键词： Deep neural networks

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Furnace Temperature Model Based on BO-LightGBM for Municipal Solid Waste Incineration 6

Furnace Temperature Model Based on BO-LightGBM for Municipal...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Hou, Jing Zhou, Xiuyan Pian, Jinxiang Tang, Jian School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350374315

Municipal solid waste incineration (MSWI) can realize the resource, harmlessness and reduction of municipal solid waste (MSW), which is a very effective method of MSW treatment. The furnace temperature is crucial for this process, ensuring complete breakdown of harmful substances and stable operation. The mechanism of MSWI is complex, and there are many variables and uncertain factors. In order to improve the accuracy of the furnace temperature model, the Light Gradient Boosting Machine (LightGBM) is used to model the furnace temperature with Bayesian Optimization (BO) for model's hyperparameter tuning. The results indicate a substantial enhancement in the accuracy of the furnace temperature model based on LightGBM optimized by BO. © 2024 IEEE.

关键词： Waste treatment

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