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Doped-nitrogen enhanced the performance of Nb_(2)CT_(x)on the electrocatalytic synthesis of H_(2)O_(2)

Nano Research 2023年第5期16卷 6120-6127页

作者： Shijie Zhang Jingnan Zheng Junming Lin Yuanan Li Zhikang Bao Xiaoge Peng Wenkai Ji Lei Ding Zaixiang Xu Guoliang Wang Xing Zhong Jianguo Wang Institute of Industrial Catalysis State Key Laboratory Breeding Base of Green-Chemical Synthesis TechnologyCollege of Chemical EngineeringZhejiang University of TechnologyHangzhou 310014China Zhejiang Center for Disease Control and Prevention Hangzhou 310051China

Electrochemical oxygen reduction reaction(ORR)for preparing hydrogen peroxide(H_(2)O_(2))is a promising way to replace the anthraquinone *** key to H_(2)O_(2)production is the development of catalysts to regulate the oxygen reduction reaction ***,nitrogen-doped Nb_(2)CT_(x)was prepared by NH3 annealing *** with precursor Nb_(2)AlC(67.01%)and pure Nb_(2)CT_(x)(75.70%),nitrogen-doped Nb_(2)CT_(x)exhibited excellent performance for 2e−ORR with>90%H_(2)O_(2)selectivity(at 0.4 V *** hydrogen electrode(RHE)).Faradic efficiency of nitrogen-doped Nb_(2)CT_(x)reached 80.75%,whereas those for Nb2AlC and Nb_(2)CT_(x)were 60.35%and 39.27%,respectively.A desirable catalytic stability for 50 h was *** functional theory(DFT)calculations indicated excellent activity of the nitrogen-doped Nb_(2)CT_(x)was attributed to the introduction of *** excellent activity was conducive to the adsorption of oxygen and promoted the formation of the OOH *** work can serve as an important reference for regulating the electronic structure of MXene to expand the application area in the electrochemical field.

关键词： Nb_(2)CT_(x) nitrogen-doped electrocatalysis oxygen reduction reaction hydrogen peroxide

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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SSD object detection algorithm based on knowledge map

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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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Object-Guided and Motion-Refined Attention Network for Video Anomaly Detection

Object-Guided and Motion-Refined Attention Network for Video...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhou, Wenhao Li, Yingxuan Zhao, Chunhui College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Video anomaly detection is challenging due to the lack of abnormal videos and ambiguity of anomaly definition. Context information is important to identify anomalous events and can be reflected from the salient objects and related background. In this paper, we propose a new multi-level attention network consisting of an Object-Guided Attention Module (OGAM) and a Motion-Refined Attention Module (MRAM) to fully exploit context by leveraging both frame-level and object-level semantics. Specifically, OGAM highlights features of salient objects on frame features for future frame prediction. MRAM further leverages global positions and optical flow of objects to improve the prediction loss. By multi-level attention, objects are highlighted in the predicted frame and necessary context information is preserved at both feature level and pixel level to identify anomalies. Experiments demonstrate our method achieves state-of-the-art performance on benchmark datasets including Avenue, UCSD Ped2 and ShanghaiTech, specifically obtaining a frame-level AUC of 92.6% on Avenue. © 2022 IEEE.

关键词： Anomaly detection

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Interaction Quantification of MTDC Systems Connected with Weak AC Grids

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2088-2099页

作者： Wanning Zheng Jiabing Hu Li Chai Bing Liu Zixia Sang Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and TechnologyWuhan 430081China State Key Laboratory of Advanced Electromagnetic Engineering and Technology and School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China State Grid Hubei Economic Research Institute Wuhan 430077China

The small-signal stability of multi-terminal high voltage direct current(HVDC)systems has become one of the vital issues in modern power *** among voltage source converters(VSCs)have a significant impact on the stability of the *** paper proposes an interaction quantification method based on the self-/en-stabilizing coefficients of the general N-terminal HVDC system with a weak AC network ***,we derive the explicit formulae of self-/en-stabilizing coefficients for any N-terminal HVDC system,which can quantify the interactions through different paths *** relation between the self-/en-stabilizing coefficients and the poles of the system can be used to evaluate the impact of the interactions on the system stability ***,we employ the obtained formulae to analyze the parameter sensitivity and explain how a parameter affects the stability of the system through different paths of ***,extensive examples are given to demonstrate the effectiveness of the proposed method.

关键词： Interactions multi-terminal HVDC self-/enstabilizing coefficients small-signal stability

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Underwater images enhancement using contrast limited adaptive parameter settings histogram equalization

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Multimedia Tools and Applications 2024年 1-15页

作者： Chen, Yahui Liang, Yitao College of Information Science and Engineering Henan University of Technology Henan Zhengzhou China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan Zhengzhou China

CLAHE is widely used in underwater image processing because of its excellent performance in contrast enhancement. The selection of the clip point formula is the core problem of the CLAHE methods, and the selection of suitable clipping value has become the focus of some extended methods. In this paper, an automatic CLAHE underwater image enhancement algorithm is proposed. The method determines the clipping value according to the high-order moment dynamic features of each block of the underwater image. By quantifying the dynamic features of each block in the image more precisely, and then adding it to the clipping value formula, the contrast and details of the underwater image can be effectively enhanced. In order to effectively improve the saturation and brightness of underwater images, this paper chooses a more accurate and intuitive HSV model. Experimental results show that our methods enhance the contrast subjectively, while suppressing the amplification of noise very well, and also increase the saturation of underwater images. In objective metrics, our method obtains the best values in underwater quality assessment (UIQM), SSIM, and PSNR. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Underwater imaging

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Dynamic behavior of outburst two-phase flow in a coal mine T-shaped roadway:The formation of impact airflow and its disaster-causing effect

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International Journal of Mining science and technology 2023年第8期33卷 1001-1017页

作者： Liang Cheng Jiang Xu Shoujian Peng Hailin Yang Feng Jiao Bin Zhou Fazhi Yan State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing UniversityChongqing 400030China College of Safety Science and Engineering Xi’an University of Science and TechnologyXi’an 710054China College of Safety and Emergency Management Engineering Taiyuan University of TechnologyTaiyuan 030024China

The study of the dynamic disaster mechanism of coal and gas outburst two-phase flow is crucial for improving disaster reduction and rescue ability of coal mine outburst *** outburst test in a T-shaped roadway was conducted using a self-developed large-scale outburst dynamic disaster test *** investigated the release characteristics of main energy sources in coal seam,and obtained the dynamic characteristics of outburst two-phase flow in a ***,we established a formation model for outburst impact flow and a model for its flow in a bifurcated *** results indicate that the outburst process exhibits pulse characteristics,and the rapid destruction process of coal seam and the blocking state of gas flow are the main causes of the pulse *** outburst energy is released in stages,and the elastic potential energy is released in the vertical direction before the horizontal *** a straight roadway,the impact force oscillates along the *** an increase in the solid–gas ratio,the two-phase flow impact force gradually increases,and the disaster range extends from the middle of the roadway to the coal *** the area near the coal seam,the disaster caused by the two-phase flow impact is characterized by intermittent *** a bifurcated roadway,the effect of impact airflow on impact dynamic disaster is much higher than that of two-phase flow,and the impact force tends to weaken with increasing solid-gas *** impact force is asymmetrically distributed;it is higher on the left of the bifurcated *** an increase in the solid-gas ratio,the static pressure rapidly decreases,and the bifurcated structure accelerates the attenuation of static ***,secondary acceleration is observed when the shock wave moves along the T-shaped roadway,indicating that the bifurcated structure increases the shock wave velocity.

关键词： Multiphase flow Coal and gas outburst Dynamic disaster Impact airflow T-shaped bifurcated roadway Coal seam

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Mesoporous molecular sieve-based materials for catalytic oxidation of VOC: A review

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Journal of Environmental sciences 2023年第3期125卷 112-134页

作者： Wei Gao Xiaolong Tang Honghong Yi Shanxue Jiang Qingjun Yu Xizhou Xie Ruijie Zhuang Department of Environmental Science and Engineering School of Energy and Environmental EngineeringUniversity of Science and Technology BeijingBeijing 100083China State Environmental Protection Key Laboratory of Food Chain Pollution Control Beijing Technology and Business UniversityBeijing 100048China Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants Beijing 100083China

As the main contributor of the formation of particulate matter as well as ozone, volatile organic compounds(VOCs) greatly affect human health and the environmental quality. Catalytic combustion/oxidation has been viewed as an efficient, economically feasible and environmentally friendly way for the elimination of VOCs. Supported metal catalyst is the preferred type of catalysts applied for VOCs catalytic combustion because of the synergy between active components and support as well as its flexibility in the composition. The presence of support not only plays the role of keeping the catalyst with good stability and mechanical strength, but also provides a large specific surface for the good dispersion of active components, which could effectively improve the performance of catalyst as well as decrease the usage of active components, especially the noble metal amount. Mesoporous molecular sieves, owing to their large surface area, unique porous structures, large pore size as well as uniform pore-size distribution, were viewed as superior support for dispersing active components. This review focuses on the recent development of mesoporous molecular sieve supported metal catalysts and their application in catalytic oxidation of VOCs. The effect of active component types, support structure, preparation method, precursors, etc. on the valence state, dispersion as well as the loading of active species were also discussed and summarized. Moreover, the corresponding conversion route of VOCs was also *** review aims to provide some enlightment for designing the supported metal catalysts with superior activity and stability for VOCs removal.

关键词： Volatile organic compound Mesoporous molecular sieves Supported catalyst Catalytic oxidation Reaction mechanism Influencing factors

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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex *** studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data *** paper presents a model based on these nanowire networks,with an improved conductance variation *** suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage *** nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction *** experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction *** results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Dual-branch deep learning architecture enabling miner behavior recognition

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Multimedia Tools and Applications 2024年第37期83卷 84523-84538页

作者： Wang, Zheng Liu, Yan Yang, Yi Duan, Siyuan Xi’an Key Laboratory of Electrical Equipment Condition Mornitoring and Power Supply Security Xi’an University of Science and Technology Xi’an710054 China College of Electrical and Control Engineering Xi’an University of Science and Technology Xi’an710054 China

Nonstandard miner behavior can have adverse effects on coal mine safety production. Therefore, accurately capturing miner behavior in complex environments is particularly important. In the intelligent mine monitoring system, using visual perception to detect miner behavior is a challenging task due to high behavioral similarity and difficult temporal relationships. In this paper, a new deep learning framework is proposed to construct a coal miner behavior recognition model with a spatio-temporal dual-branch structure and transposed attention representation mechanism. The spatio-temporal dual-branch structure extracts rich spatial semantic information from intrinsic safety video sensor input video sequences while ensuring effective capture of rapidly changing human behavior. Subsequently, considering the discrimination of miner behavior similarity, a merged transposed weighted representation mechanism (TWR) is introduced to guide the model in extracting feature information more strongly related to the classification target, thereby effectively improving the model’s ability to classify highly similar behaviors. Experiments were conducted on UCF101, HMDB51, and a self-built miner behavior dataset, achieving significant improvements compared to other state-of-the-art methods. This collaborative structure further creates a more discriminative behavior detection model, contributing to the reliability of miner behavior detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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