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SSD object detection algorithm based on knowledge map

International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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NO reduction by carbon-based gases based on coal gasification fractions:NO reduction intermediate identification and kinetic analysis

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 175-189页

作者： Xiang Zhang Jian Tian Tanghui Hu Guangyong Yue Xianlong Liu Wen Zhou Xiaohong Liu Xinye Wang Ministry-of-Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Engineering Research Center for Green Industrialization Technology of Industrial WasteSchool of Materials Science and EngineeringHubei UniversityWuhan 430062China Hubei Huda Tianshu New Energy Materials Industrial Research and Design Institute Co. Ltd.Wuhan 430062China Wuhan Tianshu Dust Removal Equipment Co. Ltd.Wuhan 430062China Engineering Laboratory for Energy System Process Conversion&Emission Control Technology of Jiangsu Province School of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing 210042China

Combining pulverized coal gasifiers with cement kiln production is promising for application in low-cost and efficient NO *** paper presents a pulverized coal gasifier catalytic denitration technique and investigates the homogeneous reduction(by CO and CH_(4))and heterogeneous catalytic reduction(by coke,CaO,MgO,and Fe_(2)O_(3))of NO.A combination of Chemkin simulations and fixed-bed experiments is used to elucidate the reaction pathways and key intermediates of NO reduction by carbon-based *** addition,the activation energies for different catalyst combinations were analyzed via reaction *** results demonstrate that the presence of small amounts of O_(2) inhibits NO reduction by CO but promotes NO reduction by CH_(4).The NCO·radical is essential for the NO reduction process,and the generation of this radical depends on the CH_(4) cleavage intermediate and O·*** and Fe_(2)O_(3) exhibit more significant catalytic effects on NO reduction by carbon-based gases than the other catalysts *** presence of a small amount of O_(2) in the reacting gas mixtures facilitates the NO reduction *** activation energy is reduced to 1.02 kJ/mol,and the NO conversion reaches 99.80%when the catalyst is C+CaO+MgO+Fe_(2)O_(3) and the gas composition is CO+CH_(4)+O_(2).This work provides theoretical support and data recommendations for the use of pulverized coal gasifiers for the denitrification of cement kilns.

关键词： Carbon-based gas Catalytic denitration Coke Kinetic analysis Reaction mechanism

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An end-to-end instance segmentation method based on improved ConvNeXt V2

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 384-392页

作者： Wang, Wenlu Sun, Ying Xu, Manman Bai, Dongxu Huang, Li Zou, Chunlong Chen, Baojia Tang, Dalai Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University Hubei Yichang China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot China

In order to improve the efficiency of indoor mobile robots in locating and segmenting environmental instances, an instance segmentation method based on RTMDet is proposed. Firstly, the more powerful ConvNeXt V2 is used as the backbone network of the model, which improves the performance of the existing RTMDet model. Subsequently, NAS-FPN is used as a converged network to detect the network at any given time. Finally, AdamW is used as the model optimiser, which effectively solves the problem of excessive memory occupation when updating parameters and improves the performance of the model. The proposed method obtains the highest evaluation index in all cases. After training and testing on the Cityscapes data set, the recognition accuracy reaches 65.0%. © 2024 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

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Chinese Journal of Chemical Engineering 2025年 81卷 241-254页

作者： Li, Zhi Xia, Yuchong Long, Jian Liu, Chensheng Zhang, Longfei State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively. © 2025 Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Frequency estimation

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Influence of contact layer on the sulphur corrosion of copper conductors in power transformers

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High Voltage 2022年第1期7卷 176-184页

作者： Lijun Yang Dong Ding Yuan Yuan Sihang Gao Yuncai Lu State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina College of Material Science and Engineering Chongqing UniversityChongqingChina Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqingChina State Grid Jiangsu Electric Power Co. Ltd.Electric Power Research InstituteNanjingChina

Owing to the corrosive sulphides in the mineral insulating oil such as dibenzyl disul-phide(DBDS),copper windings in oil‐immersed transformers may suffer sulphur *** study investigates the influence of different contact conditions on the corrosion of copper conductors in ***‐immersed accelerated corrosion experiment was conducted on copper conductors with various contact materials and different layers of insulating *** surface micromorphology,deposition compo-nent and the potentiodynamic polarization characteristics of copper conductors were *** results show that copper with different contact materials suffer sulphur corrosion;conversely,bare copper rarely *** was also verified that the existence of a particular gap between copper and contact layer is a significant factor in sulphur corrosion by the formation of a concentration cell on the copper *** insu-lating paper wrapped around copper conductors does not increase the corrosion ten-dency of copper;however,it contributes to the formation of a region concentrated with DBDS,Cu^(2+),H^(+)by adsorption and impediment,which plays a key role in facilitating the sulphur corrosion of copper ***,sulphur corrosion can also occur on other metal parts in transformers when there is a particular gap on surfaces other than copper.

关键词： corrosion copper sulphur

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Transferable Intervalization and Midpoint De-Mixing Interval Independent Component Analysis for industrial Fault Detection Under Data Uncertainty

SSRN

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SSRN 2024年

作者： Zhang, Shumei Wang, Sijia Dong, Feng Zhao, Chunhui Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin373200 China School of Information and Intelligent Engineering Tianjin Renai College Tianjin301636 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Due to noise disturbance, sensor drift, and some circumstance factors, process data are unavoidably contaminated with uncertainty, which may destroy the correlation structure of original data and make traditional process monitoring methods perform poorly. In this study, a transferable Gaussian mixture learning (TGML) method is developed to make full use of the important knowledge extracted from source domains to guide and improve the intervalization process of target domain with limited data. Then, a midpoint de-mixing interval independent component analysis (MDM-I2CA) is proposed for non-Gaussian processes with uncertainty. First, MDM-I2CA expands the traditional centering and whitening methods into interval domain to concentrate the data information on higher-order statistics. Second, a midpoint coding strategy is developed to depict the process information with the idea of focusing on the variation between intervals of a dataset, and the statistically interval independent components are further de-mixed. Finally, four monitoring statistics are constructed for fault detection. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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Automated Machine Learning-Assisted Low-Carbon Operation Optimization for Catalytic Cracking Units: A Multi-Objective Optimization Perspective

Automated Machine Learning-Assisted Low-Carbon Operation Opt...

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Intelligent Robotics and Automatic control (IRAC), International Conference on

作者： Tingwei Zhang Yurong Liu Xin Peng Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai China State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

This study proposes a low-carbon operational optimization framework for fluid catalytic cracking (FCC) units and conducts a comprehensive evaluation of their economic performance, energy utilization, and environmental impacts. First, a low-carbon energy-saving operational optimization framework was developed to enhance the economic benefits of FCC units while exploring their carbon reduction potential. Subsequently, a surrogate-assisted model based on an automated machine learning (AutoML) framework was constructed to reduce computational costs. A carbon tax scenario was then introduced, converting CO 2 emissions into carbon tax values. This approach can improve the computational efficiency of the multi-objective optimization problem. The experimental results demonstrate that optimizing operational conditions increased unit-time profits by 0.5%, reduced CO 2 emissions by 1088.92 kgCO 2 eq/h, and decreased wind energy and steam energy consumption by 627.67 m 3 /h and 324.28 m 3 /h, respectively. The scientific optimization of operational parameters enhances production efficiency while mitigating environmental impacts, providing valuable guidance for energy conservation and emissions reduction in industrial processes and supporting sustainable development.

关键词： Economics Wind energy Energy conservation Production FCC Automated machine learning Computational efficiency Optimization Carbon emissions Carbon tax

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Health Condition Monitoring of Satellite Momentum Wheel Bearing Based on Canonical Variable Analysis and Sliding Interval Variance

Health Condition Monitoring of Satellite Momentum Wheel Bear...

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industrial Cyber-Physical systems (ICPS)

作者： Sirui Du Shumei Zhang Yang Zhao Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

With the rapid development of space technology, the demand for satellite reliability is getting higher and higher. Momentum wheel is a key component of satellite attitude control system, and its reliability is an important factor affecting the life of satellite. The condition monitoring of momentum wheel bearing (MWB) is of great significance to ensure the long life and high reliable operation of the satellite. In this paper, a new monitoring method based on multivariate statistics and canonical variable analysis (CVA) is proposed, and a new health degree function is defined from both dynamic and static aspects. First, the time-frequency domain analysis technique is used to extract the features of MWB in time domain, frequency domain and time-frequency domain, and the multi-domain high-dimensional health condition feature set is constructed. Then, in order to reduce the complexity of the problem, feature reduction is realized based on CVA. On the basis of considering steady-state error and sliding interval variance (SIV), the health degree (HD) characterizing the performance condition of MWB is defined. Finally, the experimental results based on the bearing test-bed show that the proposed method is feasible and effective, and the data-driven health condition monitoring of satellite momentum wheel bearing is realized.

关键词： Condition monitoring Time-frequency analysis Analytical models Satellites Wheels Feature extraction Complexity theory

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Research on Mapping Method Between Smart Energy and Intelligent Sensing Terminal

Research on Mapping Method Between Smart Energy and Intellig...

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2022 IEEE International Conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Liu, Siyang Mao, Yuxing Long, Yunfeng Pan, Jianyu Jiang, Tianyan Nie, Yongjie School of Electrical Engineering Chongqing University State Key Laboratory of Power Equipment & System Security and New Technology Chongqing China Electric Power Measurement Digitalization Control and Protection Yunnan Power Grid Co. Ltd. Yunnan Key Laboratory of Green Energy Yunnan Kunming China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

In the process of smart city construction, each city has its characteristics, so we should implement policies according to the city. Smart energy is an important cornerstone of smart city construction. This paper proposes a mapping method between smart energy and Intelligent Sensing Terminal. Firstly, a typical application scenario design of the smart city is proposed. Then, the smart city application scenario fitness calculation model is given combined with the analysis of macro and micro scenes. Finally, Intelligent Sensing Terminal and scene mapping mechanism are proposed by a new two-dimensional coding method. This method can quickly and effectively calculate the location to be arranged and the optimal number of devices, so as to reduce resource consumption and improve the quality of perceived data. © 2022 IEEE.

关键词： Smart city

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