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Data-driven modeling and fault diagnosis for fuel cell vehicles using deep learning

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Energy and AI 2024年第2期16卷 111-124页

作者： Yangeng Chen Jingjing Zhang Shuang Zhai zhe hu College of Science University of Shanghai for Science and Technology(USST)Shanghai200093China Shanghai Refire Technology Co. Ltd.655 Jinyuanyi RoadJiadingShanghai201800China

The reliability and safety of fuel cell vehicle are crucial for the daily operation. Insulation resistance serves as a crucial index of vehicle reliability, especially when fuel cells operate at high voltages. Low insulation resistance can lead to vehicle malfunctions, exposing the operator to the risk of electric shock. In this study, long-term insulation resistance data from thirteen vehicles equipped with three different types of fuel cell systems are analyzed to diagnose possible low insulation resistance issues. For this purpose, a robust locally weighted scatterplot smoothing method is utilized to filter the original data. In this research, an insulation variation model is developed using a data-driven long short-term memory neural network to identify insulation resistance value anomalies caused by deionizer failure. The results indicate that the coefficient of determination of the failure model is 99.78 %. Moreover, current model efficiently identifies insulation faults resulting from reliability issues, such as conductivity issues of cooling pipes and erosion of vehicle wiring harnesses.

关键词： Fuel cell vehicles Insulation faults RLOWESS LSTM neural network

A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1119-1130页

作者： Haoran Zhang Weihao hu Di Cao Qi huang zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Department of Energy Technology Aalborg UniversityAalborgDenmark IEEE

Electricity prices have complex features,such as high frequency,multiple seasonality,and *** factors will make the prediction of electricity prices ***,accurate electricity price prediction is important for energy producers and consumers to develop bidding *** improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency *** frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN *** results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

关键词： Autoregressive integrated moving average model electricity price forecasting empirical mode decomposition temporal convolutional network

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

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Computers, Materials & Continua 2024年第3期78卷 4049-4069页

作者： Bing Liu zhe Zhang Shengrong hu Song Sun Dapeng Liu zhenyu Qiu zhejiang Institute of Industry and Information Technology Hangzhou310000China Digital Economy Development Center of zhejiang Hangzhou310000China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Bank of Suzhou Suzhou215000China Hangzhou Hikvision Digital Technology Co. Ltd.Hangzhou310051China

Internet of Things(IoT)is vulnerable to data-tampering(DT)*** to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT *** leads to the misreporting of normal data,which will impact the normal operation of *** mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered ***,we model the data transmission and anomaly detection in clustered ***,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT *** the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)***,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation ***,we reveal the effect of some factors on the maximum combined detection rate through theoretical *** show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

关键词： Network security Internet of Things data-tampering attack anomaly detection

Seasonal freeze-thaw processes impact microbial communities of soil aggregates associated with soil pores on the Qinghai–Tibet Plateau

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Ecological Processes 2024年第2期 279-292页

作者： Rui-zhe Wang Xia hu State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical Science Beijing Normal University School of Natural Resources Faculty of Geographical Science Beijing Normal University

Background Seasonal freeze-thaw(FT) processes alter soil formation and cause changes in soil microbial communi-ties, which regulate the decomposition of organic matter in alpine ecosystems. Soil aggregates are basic structural units and play a critical role in microbial habitation. However, the impact of seasonal FT processes on the distribution of microbial communities associated with soil pores in different aggregate fractions under climate change has been overlooked. In this study, we sampled soil aggregates from two typical alpine ecosystems(alpine meadow and alpine shrubland) during the seasonal FT processes(uFP: unstable freezing period, SFP: stable frozen period, uTP: unstable thawing period and STP: stable thawed period). The phospholipid fatty acid(PLFA) method was used to determine the biomass of living microbes in different aggregate *** The microbial biomass of 0.25–2 mm and 0.053–0.25 mm aggregates did not change significantly dur-ing the seasonal FT process while the microbial biomass of > 2 mm aggregates presented a significant difference between the STP and uTP. Bacterial communities dominated the microbes in aggregates, accounting for over 80% of the total PLFAs. The microbial communities of soil aggregates in the surface layer were more sensitive to the sea-sonal FT process than those in other soil layers. In the thawing period, Gram positive bacteria(GP) was more dominant. In the freezing period, the ratio of Gram-positive to Gram-negative bacterial PLFAs(GP/GN) was low because the enrichment of plant litter facilitated the formation of organic matter. In the freezing process, pores of 30–80 μm(mesopores) favored the habitation of fungal and actinobacterial communities while total PLFAs and bacterial PLFAs were negatively correlated with mesopores in the thawing *** The freezing process caused a greater variability in microbial biomass of different aggregate fractions. The thawing process increased the differences in

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Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading

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Advances in Manufacturing 2024年第3期12卷 576-590页

作者： Yuan-zhe hu Ru-Xue Liu Jia-Peng He Guo-Wei Zhou Da-Yong Li State Key Laboratory of Mechanical Systems and Vibration Shanghai Jiao Tong UniversityShanghai200240People’s Republic of China School of Naval Architecture Ocean and Civil EngineeringShanghai Jiao Tong UniversityShanghai200240People’s Republic of China

Roping is a severe band-like surface defect that occurs in deformed aluminum alloy *** roping prediction and rating are essential for industrial ***,the authors introduced an artificial neural network(ANN)model to efficiently forecast roping behavior across the thickness of large regions with texture *** this study,the previously proposed ANN model for roping prediction is briefly reviewed,and a few-shot learning(FSL)-based method is developed for roping grading with limited *** consider the directionality of the roping patterns,the roping dataset constructed from experimental observations is transformed into the frequency domain for more compact characterization.A transfer-based FSL method is further presented for grade roping with manifold mixup regularization and the Sinkhorn mapping algorithm.A new component-focused representation is also implemented for data-processing,exploiting the close correlation between roping and power distribution in the frequency *** ultimate FSL method achieved an optimal accuracy of 95.65%in roping classification with only five training samples per class,outperforming four typical FSL *** FSL approach can be applied to grade the roping morphologies predicted by the ANN ***,the combination of prediction and grading using deep learning provides a new paradigm for roping analysis and control.

关键词： Roping Artificial neural network(ANN) Aluminum alloys Few-shot classification Surface morphology

Copper-catalyzed C(sp)-H aryl amination enables modular synthesis of quinolines and 2-quinolinones

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Science China Chemistry 2024年第2期67卷 595-603页

作者： Yang Gao Haixia Li Simin Yang Yanping huo Qian Chen Xianwei Li zhe Wang Xiao-Qiang hu School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhou510006China Jieyang Branch of Chemistry and Chemical Engineering Guangdong Laboratory Jieyang515200China Key Laboratory of Catalysis and Energy Materials Chemistry of Ministry of Education&hubei Key Laboratory of Catalysis and Materials Science School of Chemistry and Materials ScienceSouth-Central University for NationalitiesWuhan430074China

Herein,we disclose a novel copper-catalyzed C(sp)-H aryl amination of terminal alkynes with anthranils,enabling the rapid generation of highly reactive secondary N-aryl ynamines for the modular synthesis of structurally diverse C2-substituted quinolines and *** in-situ formed carbonyl-ynamines are prone to tautomerize to carbonyl-ketenimines,which can efficiently react with a series of nucleophiles,including amines,alcohols,phenols,thiols,thiophenols,active-methylene compounds,and even water to produce various quinoline derivatives with the generation of H_(2)O as a sole and green *** method also unlocks a practical route to create various quinoline-fused heterocycles and can be successfully applied to the late-stage modification of complex molecules and the concise synthesis of bioactive *** studies reveal a coppercatalyzed inner-sphere nitrene transfer process by using anthranils as novel aryl nitrene precursors.

关键词： copper catalysis anthranils C(sp)-H aryl amination carbonyl-ketenimines quinolines

Leveraging a new thermosensor for heat-smart future agriculture

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Plant Communications 2024年第9期5卷 1-4页

作者： Ali Raza Qamar u.Zaman Zhangli hu Guangdong Key Laboratory of Plant Epigenetics College of Life Sciences and OceanographyShenzhen UniversityShenzhen 518060China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Shenzhen Engineering Laboratory for Marine Algal Biotechnology Guangdong Technology Research Center for Marine Algal BiotechnologyCollege of Life Sciences and OceanographyShenzhen UniversityShenzhen 518060China Shenzhen Collaborative Innovation Public Service Platform for Marine Algae Industry Longhua Innovation Institute for BiotechnologyCollege of Life Sciences and OceanographyShenzhen UniversityShenzhen 518060China School of Breeding and Multiplication(Sanya Institute of Breeding and Multiplication) Hainan Yazhou-Bay Seed LaboratoryHainan UniversitySanya 572025China Collaborative Innovation Center of Nanfan and High-Efficiency Tropical Agriculture School of Tropical CropsHainan UniversityHaikou 570228China

As global temperatures rise owing to climate change,crops are increasingly subjected to conditions that surpass their optimal temperature ranges,leading to heat stress(HS).HS impacts many morpho-physiological,biochemical,and molecular mechanisms in plants,ultimately affecting sustainable agricultural production worldwide(Ding et al.,2020;Kan et al.,2023;Raza et al.,2024).

关键词： smart pass crops

Biomechanical behavior of grass roots at different gauge lengths

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Journal of Mountain Science 2024年第9期21卷 3201-3214页

作者： Fu Jiangtao ZHOu zhe GuO Hong ZHAO Jimei Yu Dongmei Wu Jie hu Xiasong School of Civil Engineering and Architecture Shaanxi University of TechnologyHanzhong 723000China Crop Science postdoctoral research station Qinghai UniversityXining 810016China School of Geological Engineering Qinghai UniversityXining 810016China Qinghai institute of Salt Lakes Chinese Academy of SciencesXining 810008China Department of Geographical Science Shaanxi University of TechnologyHanzhong 723000China

Gauge length influences the biomechanical properties of herbaceous roots such as tensile resistance,tensile strength and Young’s ***,the extent to which and how these biomechanical properties of herbaceous roots are influenced remain *** better understand the behavior of roots in tension under different conditions and to illustrate these behaviors,uniaxial tensile tests were conducted on the Poa araratica roots as the gauge length increased from 20 mm to 80 ***,ANOVA was used to test the impact of the significant influences of gauge length on the biomechanical properties,nonlinear regression was applied to establish the variation in the biomechanical properties with gauge length to answer the question of the extent to which the biomechanical properties are influenced,and Weibull models were subsequently introduced to illustrate how the biomechanical properties are influenced by gauge *** results reveal that(1)the variation in biomechanical properties with root diameter depends on both the gauge length and the properties themselves;(2)the gauge length significantly impacts most of the biomechanical properties;(3)the tensile resistance,tensile strength,and tensile strain at cracks decrease as the gauge length increases,with values decreasing by 20%-300%,while Young’s modulus exhibits the opposite trend,with a corresponding increase of 30%;and(4)the Weibull distribution is suitable for describing the probability distribution of these biomechanical properties;the Weibull modulus for both tensile resistance and tensile strain at cracks linearly decrease with gauge length,whereas those for tensile strength and Young’s modulus exhibit the opposite *** tensile resistance,tensile strength,and tensile strain at the cracks linearly decrease with increasing gauge length,while the tensile strength and Young’s modulus linearly increase with increasing gauge length.

关键词： Biomechanical properties of roots Weibull distribution ANOVA uniaxial tensile test Linear regression

Manufacturing task data chain-driven production logistics trajectory analysis and optimization decision making method

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Advances in Manufacturing 2024年第1期12卷 185-206页

作者： Lin Ling zhe-Ming Song Xi Zhang Peng-Zhou Cao Xiao-Qiao Wang Cong-hu Liu Ming-Zhou Liu School of Mechanical Engineering Hefei University of TechnologyHefei230009People’s Republic of China Guobo Electronics Co.Ltd. Nanjing211111People’s Republic of China School of Mechanical and Electronic Engineering Suzhou UniversitySuzhou234000AnhuiPeople’s Republic of China Sino-uS Global Logistics Institute Shanghai Jiao Tong UniversityShanghai200030People’s Republic of China

Production logistics(PL)is considered as a critical factor that affects the efficiency and cost of production operations in discrete manufacturing *** effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits,this study proposes a PL trajectory analysis and optimization decision making method driven by a manufacturing task data chain(MTDC).First,the manufacturing task chain(MTC)is defined to characterize the discrete production process of a *** handle manufacturing big data,the MTC data paradigm is designed,and the MTDC is ***,the logistics trajectory model is presented,where the various types of logistics trajectories are extracted using the MTC as the search engine for the *** on this,a logistics efficiency evaluation indicator system is proposed to support the optimization decision making for the ***,a case study is applied to verify the proposed method,and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment,which can assist managers in implementing the optimization decisions.

关键词： Production logistics(PL) Logistics trajectory analysis Logistics optimization Data driven Manufacturing task data chain(MTDC)