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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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Fast resistivity imaging of transient electromagnetic using an extreme learning machine

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Soft Computing 2025年第2期29卷 1121-1131页

作者： Li, Ruiyou Zhang, Yong Li, Guang Li, Ruiheng Hu, Jia Li, Min School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics NanChang330013 China School of Geophysics and Measurement Control Technology East China University of Technology NanChang330013 China School of Information Engineering Hubei University of Economics Wuhan430205 China The State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China

The transient electromagnetic method (TEM) is widely used in geophysical exploration. In TEM data interpretation, nonlinear inversion plays an important role. However, traditional TEM nonlinear inversion adopts the OCCAM imaging method, which merely presents the approximate shape of the stratum model, with poor inversion accuracy and much iteration time. To solve the above problems, a novel nonlinear inversion approach based on extreme learning machine (ELM) is proposed in this paper. The ELM is required to establish the input–output mapping relationship of the inversion network only once through the analytical method, which is different from the traditional neural network method that demands iterative gradient learning and is prone to fall into the local optimum. Moreover, the ELM inversion network by randomly assigning the hidden layer parameters is capable of mapping the observed TEM data and quickly producing resistivity images, which avoids time-consuming iteration and inversion calculations. The presented approach is applied to both synthetic and field examples. The results show that compared with the traditional nonlinear inversion algorithms (BP and OCCAM), the proposed method achieves better inversion accuracy and significantly reduces the calculation time, which verifies the effectiveness of the ELM algorithm for TEM data interpretation. Additionally, the research provides a new method and technology for TEM data inversion. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Electromagnetic transients

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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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A Flexible Antenna Array Design for Microwave Energy Transmission 17th

A Flexible Antenna Array Design for Microwave Energy Transmi...

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17th Annual Conference of China Electrotechnical Society, CES 2022

作者： Chen, Jinghan Guo, Siwei Nie, Yongjie Liu, Siyang Wang, Ping State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing China Yunnan Key Laboratory of Green Energy Electric Power Measurement Digitalization Control and Protection Yunnan Power Grid Co. Ltd. Kunming China

ISBN: (纸本)9789819904075

Microstrip antenna as a kind of receiver front-end equipment of microwave energy transmission system, which plays an important role on the overall transmission efficiency of the system. Conventional solid metal antennas have poor mechanical properties and are susceptible to plastic deformation and cracks under external forces, which will degrade their performance. Gallium-indium alloy (EGaIn), which is liquid at room temperature, has similar electrical properties to copper and can recover itself after bending, which can make up for the shortcoming of solid-state antennas;Polyethylene naphthalate two formic acid glycol ester (PEN) is an organic polymer material, which has similar electrical properties and superior mechanical properties to conventional materials. Based on the above two materials, this paper proposes a microstrip antenna array based on flexible materials, which can endure a wide range of mechanical deformation and has better ductility and mechanical toughness. The simulation results indicate that the proposed antenna array can operate at 2.45 Hz and achieve 90% efficiency of conventional antennas, which can better meet the demand of microwave energy transmission. © 2023, Beijing Paike Culture Commu. Co., Ltd.

关键词： Microstrip antennas

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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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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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