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Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

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Computers, Materials & Continua 2025年第1期82卷 843-862页

作者： Feifei Yu Yongxian huang Guoyan Chen Xiaoqing Yang Canyi Du Yongkang Gong School of Mechatronic Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China School of Automotive and Transportation Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire *** the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different *** collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,*** collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two *** results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,*** underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other ***,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline *** analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire *** delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)*** findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant ***,in the SENET model,data points of various types were more widely and evenly dispersed,demonstra

关键词： Channel attention SENET model engine misfire fault fault detection

All-optical neuromorphic devices: materials, working mechanism,and applications

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Science China Chemistry 2025年

作者： Song Wang Hao Chen Hui huang College of Materials Science and Opto-Electronic Technology Center of Materials Science and Optoelectronics Engineering CAS Center for Excellence in Topological Quantum Computation CAS Key Laboratory of Vacuum Physics University of Chinese Academy of Sciences College of Chemistry and Molecular Engineering Peking University

Traditional von Neumann computing, with physical separation of memory and processing units, cannot satisfy the development of artificial intelligence and cloud computing. Neuromorphic computing, inspired by the human brain, has drawn much ***-optical neuromorphic (AON) devices employ optical signals as information carriers and leverage the neuromorphic functions to implement fast operation speed, low energy consumption, and high bandwidth of neuromorphic computing. Here, we discuss the recent progress in AON devices, including materials, device performance, working mechanisms, and applications. Moreover,the advantages and limitations of AON are presented and discussed. Finally, the perspective of AON devices points out the future research direction of neuromorphic computing.

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When AI meets sustainable 6G

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Science China(Information Sciences) 2025年第1期68卷 4-22页

作者： Xiaohu YOU Yongming huang Cheng ZHANG Jiaheng WANG Hao YIN Hequan WU National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Chinese Academy of Military Science China Information Communication Technologies Group

Sixth-generation(6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence(AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts6G's green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence,starting with knowledge graph(KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins(DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level(PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management,lightweight AI mode

关键词： artificial intelligence sustainable 6G green real-time controllable

Effects of soil organic matter components and iron aluminum oxides on aggregate stability during vegetation succession in granite red soil eroded areas

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Journal of Mountain Science 2022年第9期19卷 2634-2650页

作者： LIN Zhe huang zhi-gang LIAO Da-lan huang Wan-xia huang Juan DENG Yu-song Guangxi Key Laboratory of Forest Ecology and Conservation College of ForestryGuangxi UniversityNanningGuangxi 530004China College of Agriculture Guangxi UniversityNanningGuangxi 530004China

Soil aggregates determine the basic structure of soil,and their composition and stability are influenced by the various types of cementitious substances occurring in *** explore the main limiting factors of soil aggregation in the process of vegetation succession with granite as the parent material,five stages of vegetation succession in an eroded area were selected:bare land(BL),grassland(GL),grassland shrub transition land(GS),shrubland(SL)and secondary forest(SF).Soil samples were collected to determine the composition and stability of *** contents of organic and inorganic cementitious substances,including organic matter components and iron aluminum oxides,were determined at five soil aggregate grain *** results indicated that the stability of soil aggregates and the>0.25 mm water-stable aggregate content(WR_(0.25))increased with vegetation *** on the Le Bissonnais(LB)method,the mean weight diameter(MWD)of soil aggregates increased,and the relative dissipation index(RSI)and relative mechanical crushing index(RMI)*** humic acid(HA)and fulvic acid(FA)contents in soil aggregates increased with vegetation succession,and the soil humus content at the SF stage increased by more than 13.54%over the BL *** different vegetation succession stage,the iron and aluminum oxides for the SL and the SF were at a high level,and the contents of free-form iron oxide(Fe_(d))and amorphous iron oxide(Fe_(o))for BL were *** analysis indicated that the soil humic degree(PQ)and the contents of amorphous alumina(Al_(o))were positively correlated with aggregate stability to varying *** analysis(RDA)revealed that PQ values of 1-2 mm(PQ_(2))and 0.25-0.5 mm(PQ_(4))aggregates,the contents of Fe_(o) of bulk soil(Fe_(oB)),>2 mm(Fe_(o1)),1-2 mm(Fe_(o2)),and2 mm(Al_(o1))aggregates could explain 99.4%of the changes in soil aggregate stability at different vegetati

关键词： Soil aggregate stability Vegetation succession Soil organic matter components Iron aluminum oxides

A Novel Deterministic Quantum Communication Scheme Using Stabilizer Quantum Code

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Communications in Theoretical Physics 2008年第1期49卷 93-99页

作者： GUO Ying CHEN zhi-gang huang Da-Zu ZENG Gui-Hua Department of Communication Engineering Central South University Changsha 410063 China State Key Laboratory on Fiber-Optic Local Area Communication Networks and Advanced Optical Communication System Department of Electronic Engineering Shanghai Jia~tong University Shanghai 200030 China Department of Information Management Hunan Finance and Economics College Changsha 410205 China

Exploiting the encoding process of the stabilizer quantum code [[n, k, d]], a deterministic quantum communication scheme, in which n - 1 photons are distributed forward and backward in two-way channel, is proposed to transmit the secret messages with unconditional security. The present scheme can be implemented to distribute the secret quantum （or classical） messages with great capacity in imperfect quantum channel since the utilized code encodes k-qubit messages for each scheme run.

关键词： quantum key distribution quantum error-correction code quantum information

Investigation of maximum proton energy for qualified ground based evaluation of single-event effects in SRAM devices

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Nuclear Science and Techniques 2019年第3期30卷 97-104页

作者： Zhan-gang Zhang Yun huang Yun-Fei En zhi-Feng Lei Science and Technology on Reliability Physics and Application of Electronic Component Laboratory China Electronic Product Reliability and Environmental Testing Research Institute

Existing standards show a clear discrepancy in the specification of the maximum proton energy for qualified ground-based evaluation of single-event effects,which can range from 180 to 500 MeV. This work finds that the threshold linear energy transfer of a tested device is a critical parameter for determining the maximum proton energy. The inner mechanisms are further revealed. Highenergy deposition events(>10 MeV) in sensitive volumes are attributed to the interaction between protons and the tungsten vias in the metallization layers.

关键词： Proton Single-event effect Threshold LET Monte Carlo simulation

直接Z-型BAs/Ga_(2)SO范德华异质结光催化分解水研究

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原子与分子物理学报 2025年第6期42卷 10-16页

作者：许康黄欣杨志红南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院南京210023 南京邮电大学理学院南京210023

基于第一性原理计算,系统研究了BAs/Ga_(2)SO异质结的光催化分解水性能.结果表明:BAs/Ga_(2)SO异质结具有良好的热力学稳定性并表现出交错的能带排列方式,带隙值为0.73 eV,界面处存在从BAs指向Ga_(2)SO的内建电场使其形成了直接Z-型异质... 详细信息

基于第一性原理计算,系统研究了BAs/Ga_(2)SO异质结的光催化分解水性能.结果表明:BAs/Ga_(2)SO异质结具有良好的热力学稳定性并表现出交错的能带排列方式,带隙值为0.73 eV,界面处存在从BAs指向Ga_(2)SO的内建电场使其形成了直接Z-型异质结,能够有效分离光生电子-空穴对.另外,异质结的带边位置可以跨越水的氧化还原电位,析氢反应发生在BAs侧而析氧反应发生在Ga_(2)SO侧.最后,基于吉布斯自由能变化计算,发现在有光照时,析氢和析氧反应在BAs/Ga_(2)SO异质结表面可自发进行.以上结果表明BAs/Ga_(2)SO异质结是有潜力的光催化分解水材料.

关键词：第一性原理计算直接Z型异质结光催化分解水吉布斯自由能

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Application of machine learning to the Vs-based soil liquefaction potential assessment

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Journal of Mountain Science 2023年第8期20卷 2197-2213页

作者： SUI Qi-ru CHEN Qin-huang WANG Dan-dan TAO zhi-gang State Key Laboratory for GeoMechanics and Deep Underground Engineering China University of Mining and Technology(Beijing)Beijing 100083China School of Mechanics and Civil Engineering China University of Mining and Technology(Beijing)Beijing 100083China

Earthquakes can cause violent liquefaction of the soil, resulting in unstable foundations that can cause serious damage to facilities such as buildings, roads, and dikes. This is a primary cause of major earthquake disasters. Therefore, the discrimination and prediction of earthquake-induced soil liquefaction has been a hot issue in geohazard research. The soil liquefaction assessment is an integral part of engineering practice. This paper evaluated a dataset of 435 seismic sand liquefaction events using machine learning algorithms. The dataset was analyzed using seven potential assessment parameters. Ten machine learning algorithms are evaluated for their ability to assess seismic sand liquefaction potential, including Linear Discriminant Analysis(LDA), Quadratic Discriminant Analysis(QDA), Naive Bayes(NB), KNearest Neighbor(KNN), Artificial Neural Network(ANN), Classification Tree(CT), Support Vector Machine(SVM), Random Forest(RF), e Xtreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine(Light GBM). A 10-fold cross-validation(CV) method was used in the modeling process to verify the predictive performance of the machine learning models. The final percentages of significant parameters that influenced the prediction results were obtained as Cyclic Stress Ratio(CSR) and Shear-Wave Velocity( VS1) with 56% and 38%, respectively. The final machine learning algorithms identified as suitable for seismic sand liquefaction assessment were the CT, RF, XGBoost algorithms, with the RF algorithm performing best.

关键词： Seismic soil liquefaction Machine learning Assessment Liquefaction potential shear wave velocity

High Fe‑Loading Single‑Atom Catalyst Boosts ROS Production by Density Effect for Efficient Antibacterial Therapy

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Nano-Micro Letters 2025年第2期17卷 187-203页

作者： Si Chen Fang huang Lijie Mao zhimin Zhang Han Lin Qixin Yan Xiangyu Lu Jianlin Shi Department of Cardiology Shanghai Tenth People’s HospitalSchool of MedicineTongji UniversityShanghai 200092People’s Republic of China Shanghai Institute of Ceramics Chinese Academy of SciencesShanghai 200050People’s Republic of China Shanghai Frontiers Science Center of Nanocatalytic Medicine School of MedicineTongji UniversityShanghai 200092People’s Republic of China

The current single-atom catalysts(SACs)for medicine still suffer from the limited active site ***,we develop a synthetic method capable of increasing both the metal loading and mass-specific activity of SACs by exchanging zinc with *** constructed iron SACs(h^(3)-FNC)with a high metal loading of 6.27 wt%and an optimized adjacent Fe distance of~4 A exhibit excellent oxidase-like catalytic performance without significant activity decay after being stored for six months and promising antibacterial ***,a“density effect”has been found at a high-enough metal doping amount,at which individual active sites become close enough to interact with each other and alter the electronic structure,resulting in significantly boosted intrinsic activity of single-atomic iron sites in h^(3)-FNCs by 2.3 times compared to low-and medium-loading ***,the overall catalytic activity of h^(3)-FNC is highly improved,with mass activity and metal mass-specific activity that are,respectively,66 and 315 times higher than those of commercial Pt/*** addition,h^(3)-FNCs demonstrate efficiently enhanced capability in catalyzing oxygen reduction into superoxide anion(O_(2)·^(−))and glutathione(GSH)*** in vitro and in vivo assays demonstrate the superior antibacterial efficacy of h^(3)-FNCs in promoting wound *** work presents an intriguing activity-enhancement effect in catalysts and exhibits impressive therapeutic efficacy in combating bacterial infections.

关键词： Nanocatalytic medicine Single-atom catalysts Reactive oxygen species(ROS) High metal loading Oxidase catalysis