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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Peng Zhao Meibao Yao Bo Zheng Xueming Xiao Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China Shanghai Aerospace Control Technology Institute Shanghai China CVIR Lab Changchun University of Science and Technology Changchun China Key Lab of Optoelectronic Measurement and Optical Information Transmission Technology

Deploying robots instead of human labor in complex and hazardous environments is a highly effective strategy. However, current fixed-configuration robot platforms struggle to meet the demanding exploration requirements in non-structured environments. In contrast, reconfigurable modular robots are able to adjust their configurations in response to environmental and task requirements, thereby offering immense potential for tackling complex exploration tasks in unstructured environments. This versatility of reconfigurable modular robots allows them to better adapt to various environmental and task demands, such as using snake or caterpillar configurations to navigate narrow holes, or quadruped configurations to traverse rugged terrain. However, the current research on modular robots primarily focuses on optimizing either their own configurations or solely the motion control model, potentially limiting their adaptability to the environment and hindering the full realization of their reconfigurability. To address this limitation, this paper improves upon traditional EA and proposes a configuration-motion co-optimization method and a new encoding method for modular robots, which jointly models and optimizes both configuration and motion. The method includes dynamic encoding of configuration-motion and the design of compatible operators, effectively enhancing the adaptive capabilities of modular robots in complex environments. The method was validated using the Webots platform, and the results demonstrate successful generation of adaptable reconfiguration schemes for various terrains. Compared to traditional EA, this method has shown superior performance in handling diverse tasks.

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Synergetic Lubrication between Self-Lubricating Ceramic Tools and Nanofluids: Advancing the Application of Self-Lubricating Ceramic Cutting Tools

SSRN

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SSRN 2023年

作者： Li, Yifei Chen, Hui Zhang, Jingjie Xiao, Guangchun Yi, Mingdong Chen, Zhaoqiang Xu, Chonghai School of Mechanical Engineering Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Key Laboratory of Equipment Manufacturing and Intelligent Measurement and Control China National Light Industry Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Machinery Design & Research Institute Jinan250031 China

In the field of ceramic tool processing, the incorporation of solid lubricants into self-lubricating ceramic tools forms a supportive lubricating film during the cutting process, effectively enhancing the cutting performance of the tools. Concurrently, Nanofluid Minimum Lubrication technology (NMQL), as an emerging lubrication method, holds the potential to improve tool performance. However, due to significant cutting forces, NMQL faces challenges in effectively reaching the machining area, thereby limiting its cooling and lubricating efficiency. To overcome this challenge, our study proposes an innovative strategy by combining self-lubricating ceramic tools with nanofluids, aiming to enhance ceramic tool performance through synergistic lubrication effects. We conducted experimental studies under various lubrication conditions, comparing the performance of self-lubricating ceramic tools with traditional ceramic tools in cutting operations. A comprehensive evaluation of parameters such as tool life, cutting temperature, and cutting forces was performed. The results demonstrate that self-lubricating ceramic tools enable more efficient penetration of nanofluids into the machining area. In comparison to sole nanofluid lubrication, the synergistic effect significantly improves tool performance while substantially reducing production costs. This research contributes to advancing the development and widespread application of self-lubricating ceramic tools in the field of mechanical processing. © 2023, The Authors. All rights reserved.

关键词： Lubrication

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A Mars Multi-terrain Simulator Using a Modular Terrain Construction Framework

A Mars Multi-terrain Simulator Using a Modular Terrain Const...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Yun Jiang Meibao Yao Bo Zheng Tao Hu Tao Cao Xueming Xiao Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China Shanghai Aerospace Control Technology Institute Shanghai China CVIR Lab Changchun University of Science and Technology Changchun China Key Lab of Optoelectronic Measurement and Optical Information Transmission Technology

Mars exploration tasks are inseparable from the support of robotic equipment. More comprehensive environmental awareness capabilities can improve the efficiency of robots on the surface of Mars. Artificial neural networks have emerged as pivotal tools in enhancing the perceptual capabilities of robots. The cornerstone of effective neural network training lies in the availability of high-quality data. Regrettably, the unstructured terrains, particularly the Martian landscape, lack real-world datasets with high-quality annotations. To overcome this challenge, using simulators to generate large amounts of high-quality data for researchers is a good option. Nonetheless, the creation of these simulators requires complex details such as ground texture characteristics and rock distribution, which increases research costs. In response, we introduce a novel approach – the modular terrain construction framework and construct a Mars multi-terrain simulator based on it. This innovative simulator not only simplifies the simulation process but also grants users the flexibility to alter it to their specific research needs. One of the standout features of our simulator is its capacity to generate rich metadata, such as semantic and depth, which can be used seamlessly for a multitude of downstream applications. To validate the reliability of the data produced by our simulator, we have assessed its performance on a semantic segmentation task.

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Federated-TD3:Joint Computing Offloading and Resource Allocation in Urban Air Mobility

Federated-TD3:Joint Computing Offloading and Resource Alloca...

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IEEE International Conference on communications in China Workshops (ICCC)

作者： Zhonghao Luo Chunyu Pan Sihua Wang Linyan Guo The Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China The Department of Electronics and Communication Engineering Hong Kong University of Science and Technology Hong Kong China School of Geophysics and Information Technology China University of Geosciences Beijing Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With rapid urban population growth and urban-ization, along with increasing demand for developing the low- altitude economy, urban air mobility (UAM) is seen as a way to alleviate ground traffic congestion and promote the low-altitude economy. Due to frequent data computation, low latency require-ments, and limited energy, effective computation offloading and resource management are urgently needed. This paper assumes UAM aircraft transmit semantic information and proposes a computation offloading and resource allocation strategy based on edge computing (EC). In case of edge server failure, relay UAV s assist in information transmission, enabling UAM aircraft to offload tasks to other edge servers. We formulated an op-timization problem to minimize delay and energy consumption and designed the Federated- Td3algorithm, combining federated learning (FL) and twin delayed deep deterministic policy gradient (TD3). Compared to two baseline algorithms, our algorithm reduced delay by 9.9% and 3.3%, and energy consumption by 14.1 % and 4.5%.

关键词： Energy consumption Semantics Delays Resource management Servers Aircraft Relays Traffic congestion Edge computing Testing

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Beat Signal Analysis for Terahertz Frequency Modulation Continuous Wave

Beat Signal Analysis for Terahertz Frequency Modulation Cont...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Chao Yang Yi Yang Defeng Liu Nai Lin Wei Liu Jie Zhuang Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute China School of Information and Communication Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China The 10th Research Institute of CETC Chengdu Sichuan China

The terahertz radar system can realize accurate detection, high-resolution imaging, and feature recognition of targets due to its small size, lightweight, and high resolution. Based on this special-shaped terahertz frequency-modulated continuous-wave radar, this paper studies the slope control of the transmitter and the local oscillator signal source and the change rule of the difference frequency signal and the effective acquisition interval. A short-time Fourier transform is used to simulate and verify the effective acquisition interval of the received intermediate frequency signal. It can be seen from the experimental results that under the special-shaped structure, controlling the FM continuous-wave slope at the transmitting and receiving ends can obtain the desired frequency-varying frequency signal, which can verify the correctness of the theoretical derivation.

关键词： Time-frequency analysis Terahertz wave imaging Frequency modulation Fourier transforms Transmitters Target recognition Radar

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Condition Monitoring and Fault Diagnosis of Coal Mill Based on Mechanism Modeling Simulation and D-S Evidence Fusion

Condition Monitoring and Fault Diagnosis of Coal Mill Based ...

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第34届中国控制与决策会议

作者： Tian Qiu Yuchen Yang Yue Guo Xiaofeng Chen Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University School of Control and Computer Engineering North China Electric Power University North China Electric Power Research Institute Co. Ltd

The coal mill is the key equipment of powdering system in coal-fired power plants,whose working state has an important influence on the combustion stability of boiler as well as the operation ***,owing to the lack of real fault data,it is difficult and not available enough to support and achieve the research of data-driven fault diagnosis ***-type medium-speed coal mill was selected as the research object in this ***,the existing mechanism model had been further improved to make it more effective and reasonable,and we used it to respectively simulate the typical operating data of the normal condition and three fault *** paper transformed the problem of fault diagnosis into the measurement of similarity between the testing and the typical fault samples,taking the numerical distance and changing trends into account,and adopted the concept of similarity function and transformed the results into the expression and calculation of probability level,which was calculated as the diagnostic evidence of individual parameters under the same time scale.D-S evidence theory was employed to fuse the individual evidence between multiple parameters evidence,getting the fusion of multiparametric evidence as to the final diagnosis ***,the experiments verified the high accuracy of the proposed diagnostic method,and the similarity function has high rationality and feasibility.

关键词： Coal Mill System Process Fault Dynamic Mathematical Model Similarity Function D-S Evidence Fusion Theory Fault Diagnosis

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Multi-objective Optimization of A CCHP System Model Integrated with P2G for Decarbonization

Multi-objective Optimization of A CCHP System Model Integrat...

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

作者： Pengyu Wei Olusola Bamisile Dongsheng Cai Jian Li Humphrey Adun Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu China Energy Systems Engineering Department Cyprus International University KKTC Mersin Turkey

To realize the dual carbon policy, the goal of energy saving, and emission reduction, it is important to develop ways to better promote the low carbon economic operation of energy systems. Therefore, this research proposes an optimal operation strategy considering the electric-gas-cooling-heat demand response. Considering the demand response of each participant, an integrated energy system model with photovoltaic, wind energy, and traditional grid as energy supply. Also, electric energy, natural gas, cooling, and heating subsystems with co-production are established. With minimizing the overall system operation cost and optimizing environmental protection, the improved particle swarm optimization algorithm is used to solve the multi-constrained objective optimization problem. Finally, the optimized model is tested. The results from this study show that the energy system under cogeneration can find a balance between economy and environmental friendliness, and provide a reference for the subsequent energy supply configuration.

关键词： Photovoltaic systems Renewable energy sources Wind energy Systems operation System integration Demand response Particle swarm optimization

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XRF Spectral Feature Extraction and Classification Method Based on the EO-CNN Deep Learning Model

XRF Spectral Feature Extraction and Classification Method Ba...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Jiajie Li Qi Zhang Ran Gao Mohai Yue Xiangyu Liu Yi Zhao Lan Rao Feng Tian Yun Teng Fu Wang Qinghua Tian Yongjun Wang State Key Laboratory of Information Photonics and Optical Communications Beijing Key Laboratory of Space-ground Interconnection and Convergence School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Beijing Key Laboratory of Space-ground Interconnection and Convergence School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Guangdong University of Technology Guangzhou China The School of Information and Electronics Beijing Institute of Technology (BIT) Beijing China Beijing Institute of Control and Electronic Technology Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Beijing Key Laboratory of Space-ground Interconnection and Convergence BUPT Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

An XRF spectral feature extraction and classification method based on an EO-CNN model is proposed to address the complexities of soil spectral data and improve classification accuracy. The proposed model is applied to soil classification, achieving an overall accuracy of 95% for five distinct soil categories. The experimental results show robustness in handling imbalanced datasets, as evidenced by a 69% classification accuracy for the category representing only 1% of the dataset.

关键词： Electrical engineering Deep learning Accuracy Automation Soil Feature extraction Robustness Data models Complexity theory Classification algorithms

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scIAC: clustering scATAC-seq data based on Student’s t-distribution similarity imputation and denoising autoencoder

scIAC: clustering scATAC-seq data based on Student’s t-dist...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Lan Jin Ye Xun Sun Xiaoshu Zhu Qingfeng Chen Yi Pan School of computer electronic and information and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of computer electronic and information Guangxi University Nanning China School of Computer Science and Engineering Yulin Normal University Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student’s t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student’s t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data.

关键词： Sequential analysis Runtime Clustering methods Noise reduction Neural networks Feature extraction Data models

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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