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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Gong, Cheng Ding, Zhaoyi Lam, Chi-Seng University of Macau State Key Laboratory of Analog and Mixed-Signal Vlsi China University of Macau Institute of Microelectronics China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350316445

Mathematical model of the inductive power transfer (IPT) systems is essential for stability analysis and control design. Conventional modeling approaches for IPT systems result in high-order models, as each resonant voltage or current is represented by two slowly changing real-valued variables. Alternative modeling methods may decrease the model order, but they are often plagued by drawbacks such as the absence of analytical expressions, reduced generality, and incomplete modeling. Moreover, the harmonic AC components of the IPT system are not well modeled in the existing methods, which affects the analysis accuracy and control optimality. In this paper, a harmonic transfer function (HTF) modeling of the IPT system is proposed, which has good mathematical properties and improved accuracy. First, the circuit configuration and the linear time-varying (LTV) large-signal model are proposed. Then, the derivation of the proposed HTF is comprehensively discussed. Finally, the accuracy of the proposed HTF is verified by simulation and experimental results in comparison with other representative modeling methods, which shows an improved accuracy. © 2023 IEEE.

关键词： harmonics transfer function inductive power transfer system modeling

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Electrochemical Carbon Dioxide Reduction to Ethylene:From Mechanistic Understanding to Catalyst Surface engineering

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Nano-Micro Letters 2023年第10期15卷 382-415页

作者： Junpeng Qu Xianjun Cao Li Gao Jiayi Li Lu Li Yuhan Xie Yufei Zhao Jinqiang Zhang Minghong Wu Hao Liu Joint International Laboratory on Environmental and Energy Frontier Materials School of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444People’s Republic of China Centre for Clean Energy Technology Faculty of ScienceUniversity of Technology SydneyBroadwaySydneyNSW 2007Australia Department of Electrical and Computer Engineering University of Toronto35 St George StreetTorontoON M5S 1A4Canada

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to *** multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial ***,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing ***,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)*** this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to *** the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene *** strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are ***,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.

关键词： Key steps in CO_(2)RR-ethylene Preferable reaction pathways Mechanism understanding Surface engineering strategies of Cu-based catalysts

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Filtering Balun Design using Hairpin-Ring Resonator and SSPP Metamaterial with Wide Notched band and Wide Stopband

Filtering Balun Design using Hairpin-Ring Resonator and SSPP...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Qi, Huanhuan Liu, Haiwen Huang, Mo School of Information and Communications Engineering Xi' An Jiaotong University Xi'an China Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350336535

In this paper, a novel filtering balun based on microstrip-to-slotline transition is proposed. Utilizing the inherent out-of-phase electric field distributions on the adjacent grounds of slotline, the balun function is realized. In order to realize the wide notched-band and stopband performance for radio astronomical applications, spoof surface plasmon polariton (SSPP) metamaterial and multi-mode hairpin ring resonators (HRRs) are adopted. With these novel structures loaded, the proposed filtering balun exhibits a wide band from 1GHz to 6 GHz. The notched band is from 3.2 GHz to 4.4 GHz, within the notched band, the rejection level is better than 15 dB. Besides, the out-of-band suppression is below 35 dB in the wide frequency range from 6 GHz to 8 GHz. © 2023 IEEE.

关键词： Metamaterials

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Visual recognition of wheel hubs with convolutional neural network 3

Visual recognition of wheel hubs with convolutional neural n...

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Dai, Yining Fang, Zaojun Zhong, Caiming Ningbo University Faculty of Electrical Engineering and Computer Science NingBo Zhejiang China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology NingBo Zhejiang China

ISBN: (纸本)9798350331578

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels. © 2023 IEEE.

关键词： Wheels

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Implementation of a low-code monitoring system for solid oxide fuel cells 4

Implementation of a low-code monitoring system for solid oxi...

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, BenBen Guan, WanBing Wang, JianXin Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Advanced Fuel Cells and Electrolyzers Cell Technology of Zhejiang Province Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9781510666412

Solid oxide fuel cells (SOFCs) have the advantages of high conversion efficiency, low working noise, and environmental protection. Thus, they are widely used in large-scale power generation, combined heat power, peak load-regulating energy storage, and other fields. In the actual working process of SOFCs, the battery performance will decline sharply due to extreme conditions such as overtemperature and peroxide, and the service life will be terminated. Remote monitoring of fuel cells can detect problems in time and restore the battery’s performance by restoring the battery, thereby extending its service life. The data generated by fuel cells during operation have a strong temporal correlation. To discover the future state of fuel cells in time and solve the problem that a large number of codes are needed for monitoring, this paper designs a remote monitoring system of fuel cells with low-code based on Node-RED and Ali Cloud. The output current of fuel cells is predicted by using the improved back propagation neural network based on Levenberg-Marguardt (LM-BPNN) algorithm. The test result of LM-BPNN shows that the test samples have a root mean square error of 0.172, a mean absolute percentage error of 3.85%, and a coefficient of determination of 0.998. © 2023 SPIE.

关键词： Digital storage

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Energy and Reserve Scheduling Based on Security Constrained Convex Optimal Power Flow Considering Wind Power Generation in Europe

Energy and Reserve Scheduling Based on Security Constrained ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Yuan, Zhao University of Iceland Electrical Power Systems Laboratory Faculty of Electrical and Computer Engineering Reykjavik Iceland

ISBN: (纸本)9781665414395

Based on security constrained convex optimal power flow (SC-COPF), this paper proposes an energy and operational reserve scheduling approach for the power system. The proposed approach is capable of scheduling energy and operational reserve dynamically according to the power load forecast and the power generation from renewable energy. Reserve requirements to keep the power system in the acceptable security level is equivalently formulated as the linear constraints in the SC-COPF model. The reserve requirements are considered in a statistical way which is a common practice in the power system operations in Europe. For example, the practice in Switzerland of reserve scheduling is to ensure that the maximum probability of power insufficiency should be less than 0.2%. The objective is to minimize the economic cost of the energy production and the reserve provision. The convex optimal power flow (COPF) is used to model the AC power flows of the power network. The capacity constraints of transmission lines are considered in our SC-COPF model by equivalently constraining the power loss variables. The proposed approach is validated by using the modified European transmission network 1354pegase test case. The power load forecast and power generation from renewable energy data from Réseau de Transport d'EÉlectricite (RTE) in France are used for the validation. Numerical results show efficiency of our approach to dynamically schedule the energy and reserve. © 2021 IEEE.

关键词： Scheduling

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Predicting the friction angle of clays using a multi-layer perceptron neural network enhanced by yeo-johnson transformation and coral reefs optimization

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Journal of Rock Mechanics and Geotechnical engineering 2024年第10期16卷 3982-4003页

作者： Libing Yang Trung Nguyen-Thoi Trung-Tin Tran College of Civil Engineering and Architecture Hunan Institute of Science and TechnologyYueyang414006China Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh CityViet Nam Department of Information Technology Greenwich VietnamFPT UniversityDa Nang550000Viet Nam

The accurate prediction of the friction angle of clays is crucial for assessing slope stability in engineering *** study addresses the importance of estimating the friction angle and presents the development of four soft computing models:YJ-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** of all,the Yeo-Johnson(YJ)transformation technique was used to stabilize the variance of data and make it more suitable for parametric statistical models that assume normality and equal *** technique is expected to improve the accuracy of friction angle prediction *** friction angle prediction models then utilized multi-layer perceptron neural networks(MLPnet)and metaheuristic optimization algorithms to further enhance performance,including flower pollination algorithm(FPA),coral reefs optimization(CRO),ant colony optimization continuous(ACOC),and cuckoo search algorithm(CSA).The prediction models without the YJ technique,***-MLPnet,CRO-MLPnet,ACOC-MLPnet,and CSA-MLPnet,were then compared to those with the YJ technique,***-FPA-MLPnet,YJ-CRO-MLPnet,YJ-ACOC-MLPnet,and *** these,the YJ-CRO-MLPnet model demonstrated superior reliability,achieving an accuracy of up to 83%in predicting the friction angle of clay in practical engineering *** improvement is significant,as it represents an increase from 1.3%to approximately 20%compared to the models that did not utilize the YJ transformation technique.

关键词： Natural hazards Slope stability Friction angle Clay Soft computing models Geotechnical engineering

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Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

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Light(Science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

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