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International Conference on Electrical Machines and systems (ICEMS)

作者： Yue Ma Shuangxia Niu Weinong Fu Jiaqi Fei Jiahui Huang Electrical and Electronics Engineering The Hong Kong Polytechnic University Hongkong China Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Han’s Robot Algorithm Team Han’s Laser Smart Equipment Group Co Ltd Shenzhen China

The Dual permanent magnet machine (DPMM) possesses the characteristics of high torque and low speed which is suitable for direct-drive equipment. Because of the low magnetic reluctance of this motor, the torque is very sensitive to dimensional fluctuations. As a synchronous motor, it also suffers from noise and vibration caused by torque ripple. This research analyzes the torque ripple in several different scenarios under the same DPMM electromagnetic design and finds a predictable influence of mechanical tolerances on torque ripple. In order to reduce the torque ripples, this paper proposes a harmonic current injection (HCI) method. A DPMM with a split-tooth structure is simulated and tested in this paper to demonstrate the analysis of torque fluctuations and the effectiveness of HCI.

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Analysis of a Data Set to Determine the Dependence of Airline Passenger Satisfaction 7th

Analysis of a Data Set to Determine the Dependence of Airlin...

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7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Tynchenko, V.S. Borodulin Kleshko, I.I. Nelyub, V.A. Rukosueva Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia

ISBN: (纸本)9783031535512

This paper presents an analysis of a data set to determine the factors influencing airline passenger satisfaction. The study examines various criteria such as gender, customer type, age, travel type, class, and range to assess their impact on passenger satisfaction. The dataset consists of 25 columns, including attributes like Wi-Fi availability, convenience of online booking, seat comfort, in-flight entertainment, baggage handling, and overall satisfaction. The sample is relatively balanced, with equal representation of men and women, predominantly repeat customers, and a majority flying for business purposes. Key findings include a strong correlation between departure and arrival delays, higher satisfaction among passengers in business class, and positive ratings for Wi-Fi service correlating with overall satisfaction. Correlation analysis reveals interdependencies between different attributes, such as the influence of cleanliness on seat comfort and food and beverage ratings. In addition, a neural network forecasting model is used to estimate the average ratings of passengers, although with low accuracy, which was later excluded. Finally, a decision tree algorithm is utilized to identify the most significant attributes affecting passenger satisfaction words. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Baggage handling

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Correlation Analysis and Predictive Factors for Building a Mathematical Model 1

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7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Nelyub, V.A. Tynchenko, V.S. Gantimurov, A.P. Degtyareva, K.V. Kukartseva, O.I. Artificial Intelligence Technology Scientific and Education Center Bauman Moscow State Technical University Moscow105005 Russia Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russia Information-Control Systems Department Institute of Computer Science and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Technological Machines and Equipment of Oil and Gas Complex School of Petroleum and Natural Gas Engineering Siberian Federal University Krasnoyarsk660041 Russia Department of Information Economic System Institute of Engineering and Economics Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia Department of Systems Analysis and Operations Research Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk660037 Russia

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

The study, published in the journal Nature Medicine, looked at data on 1,000 people from China who were tracked over an average period of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution. The study analyzed data on patients with lung cancer, including their age, gender, exposure to air pollution, alcohol consumption, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoking, chest pain, cough, hemoptysis, fatigue, weight loss, shortness of breath, wheezing, difficulty swallowing, nail thickening and snoring. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Air pollution

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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第42届中国控制会议

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Computer Science and Informatics De Montfort University

Aiming at the prediction of truck travel time in open pit mines,we established a prediction model based on long short-term memory(LSTM).This model fully accounts for 11 factors,including the nature of trucks,weather,road conditions,and driver's behaviors,as well as the influence of neighbor road segments in the route on the current predicted road *** experiment shows that the error of the LSTM prediction model is significantly reduced compared with S VR and BP *** addition,the maximum absolute mean error under different conditions is less than 12 seconds.

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction

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Science China(Information Sciences) 2021年第1期64卷 188-200页

作者： Luefeng CHEN Wanjuan SU Min LI Min WU Witold PEDRYCZ Kaoru HIROTA School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and *** view of these problems, we propose a population randomization-based multi-objective genetic *** gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time.

关键词： multi-objective genetic algorithm gesture adaptation human-robot interaction

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization

Franklin Open

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Franklin Open 2024年 7卷

作者： Mokhtar, Ahmed Ibrahim, Mohamed Hanafy, Mohamed E. ElTohamy, Fawzy H. Amer Murtada, Wael A. Elhalwagy, Yehia Z. Aircraft Electric Equipment Department Military Technical College Cairo Egypt Automatic Control Systems Department Military Technical College Cairo Egypt National Authority for Remote Sensing & Space Sciences Cairo Egypt Electrical and Computer Engineering Military Technical College Cairo Egypt

The successful execution of spacecraft missions relies heavily on the performance and reliability of their power supply subsystems. Achieving optimal component size and configuration is crucial for efficient power generation, distribution, and storage. Designing an efficient power supply subsystem for spacecraft missions is a complex and multi-objective optimization problem. Traditional optimization techniques, such as genetic algorithm and particle swarm optimization, have been employed, but their limitations in handling discrete variables and achieving global optima necessitate the exploration of alternative methods. In this context, this research paper explores the application of discrete water cycle algorithm (DWCA) optimization as a novel approach for optimizing the component size and configuration of spacecraft power supply subsystems. By emulating the dynamic flow and distribution of water, DWCA seeks to efficiently explore the design space and identify optimal solutions. Through comprehensive experimentation and comparative analysis, this research demonstrates that the proposed DWCA outperforms/surpasses other traditional optimization techniques in optimizing component size and configuration for spacecraft power supply subsystems. It consistently determines designs that are more efficient in power generation and storage. Moreover, DWCA enhances the efficiency and reliability of spacecraft power supply systems, especially in mission-critical scenarios. The successful optimization of spacecraft power supply subsystems is paramount to the overall success of space missions. Therefore, this advancement contributes significantly to the ongoing efforts to ensure the success of future space missions. © 2024 The Author(s)

关键词： Collaborative optimization Component-based system analysis Design performance Discrete Water Cycle Algorithm (DWCA) Multi-objective design Multidisciplinary design optimization Space electric power system Trade-off analysis

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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ROSIE: A ROS Adapter for a Modular Digital Twinning Framework

ROSIE: A ROS Adapter for a Modular Digital Twinning Framewor...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Gianmarco Pisanelli Mariusz Tymczuk James A Douthwaite Jonathan M. Aitken James Law Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK Department of Computer Science Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

As robotic systems become more interactive and complex, there is a need to standardise interfaces and simplify development processes. This is particularly pertinent in the field of manufacturing, where human-robot collaboration is on the increase, but where standards and proprietary software are key barriers to deployment and *** this article we present the ROSIE Adapter, a general-purpose, modular adapter developed in ROS designed to support the creation and connection of industry-ready digital twins. Together with our previous work on the modular CSI digital-twin framework, we demonstrate how the ROSIE Adapter creates a versatile "plug-and-play" interface that simplifies the development of new robotic processes, and improves accessibility to novice users. Furthermore, the adaptor supports integration of intuitive interface devices, such as speech and augmented reality interfaces, which enable more natural collaboration. We describe the adaptor and its use in two real-world applications, demonstrate the ease of use via a three-day hackathon event, and provide results showing the faithfulness of the arising digital twins to their connected physical systems.

关键词： Adaptation models Collaboration Process control Data models Digital twins Safety Speech processing

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