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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Li, Kuan Su, Weijie Li, Xingyong He, Yang Huang, Minchang Luo, Hao Shanghai Aerospace Control Technology Institute Shanghai China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331527471

This paper presents the parameter identification based on generalized orthonormal basis function (GOBF) without persistent excitation via two learning-based paradigms. The problem is formulated as a two-stage identification of poles and weight coefficients of GOBFs. The GOBF is designed based on the two-parameter Kautz basis function for the compatiblity with complex poles. The two learning-based paradigms are established on the nature-inspired meta-heuristic optimization and deep reinforcement learning, respectively. The ability of identification without strict persistent excitation is discussed as well. The effectiveness of the two paradigms are verified through a simulation study on a 4-order transfer function model. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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Computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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High-entropy(La_(0.2)Nd_(0.2)Sm_(0.2)Gd_(0.2)Yb_(0.2))_(2)(Zr_(0.75)Ce_(0.25))_(2)O_(7)thermal barrier coating material with significantly enhanced fracture toughness

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Chinese Journal of Aeronautics 2023年第4期36卷 556-564页

作者： Donghui GUO Feifei ZHOU Baosheng XU Yiguang WANG You WANG Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijing 100081China Department of Materials Science School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China

Poor fracture toughness leads to premature failure of La_(2)(Zr_(0.75)Ce_(0.25))_(2)O_(7)(LCZ)ther-mal barrier coatings in an elevated temperature service environment.A novel coating material,namely(La_(0.2)Nd_(0.2)Sm_(0.2)Gd_(0.2)Yb_(0.2))_(2)(Zr_(0.75)Ce_(0.25))_(2)O_(7)(LNSGY)based on the high-entropy con-cept,was successfully fabricated by solid-state *** microstructure of LCZ and LNSGY was investigated by X-Ray Diffraction(XRD),Raman Spectrometer(RS),Transmission Electronic Microscopy(TEM)and Scanning Electron Microscopy(SEM).The fracture toughness of the LCZ and LNSGY ceramics was *** LNSGY has excellent high-temperature phase stability,and the grain size of LNSGY ceramic is smaller than that of LCZ ceramic at an elevated temper-ature due to the sluggish diffusion *** with LCZ(fracture toughness is(1.4±0.1)MPa·m^(1/2)),the fracture toughness of LNSGY is significantly enhanced(fracture toughness is(2.0±0.3)MPa·m^(1/2)).Therefore,the LNSGY can be a promising advanced thermal barrier coat-ing material in the future.

关键词： Fracture toughness High-entropy ceramics High-temperature phase sta-bility Rare-earth zirconate Thermal barrier coatings

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Electronic structure regulation of Fe-doped Ni2P nanocrystals towards durable electrocatalytic oxygen evolution

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Rare Metals 2024年第12期43卷 6405-6415页

作者： Ya Liu Xing Cao Jia-Jia Liu Mei-Sheng Han Gao-Wei Zhang Yu-Bin Zhao Huan-Hui Chen Liang Yu Jun-Rong Zeng Zhi-Kai Cheng Liu-Biao Zhong Li-Juan Song Ye-Jun Qiu Shenzhen Engineering Lab of Flexible Transparent Conductive Films School of Materials Science and EngineeringHarbin Institute of TechnologyShenzhen518055China School of Physics Harbin Institute of TechnologyHarbin150001China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen518055China School of Science Harbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Harbin Institute of TechnologyShenzhen518055China

The inherent electrocatalytic potential of transition metal phosphides(TMPs)for oxygen evolution is influenced by the reduced efficiency of electron transfer resulting from the interaction between electronegative phosphorus atoms and transition ***,we introduce Fe into Ni_(2)P nanocrystals by thermal injection synthesis method,and anchor them on nickel foam(NF)by facile spraying to prepare self-supporting oxygen evolution reaction(OER)***,the optimized electrode of Ni_(2)P-Fe-2/NF demonstrates low overpotentials of 212 mV with 10 mA·cm^(-2)and a 0.9%decay within300 h test of 50 mA·cm^(-2).Notably,when electrode size was expanded to 600 cm^(2)and applied to a larger electrolyzer,its 9 h decay rate at 6 A current was only 1.69%.Characterization results show that Fe doped NiOOH is generated during OER reaction as actual catalyst,Results from density functional theory(DFT)computations suggest that Fe doping shifts NiOOH d-band center to Fermi level,lowering critical *OOH intermediates formation energy barrier during the OER *** findings inform the large-scale industrial application of TMPs as robust electrocatalysts.

关键词： Electronic structure regulation Fe doping Colloidal nanocrystals Ni2P Oxygen evolution reaction

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A Shape-Memory Deployable Subsystem with a Large Folding Ratio in China’s Tianwen-1 Mars Exploration Mission

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engineering 2023年第9期28卷 49-57页

作者： Chengjun Zeng Liwu Liu Yang Du Miao Yu Xiaozhou Xin Tianzhen Liu Peilei Xu Yu Yan Dou Zhang Wenxu Dai Xin Lan Fenghua Zhang Linlin Wang Xue Wan Wenfeng Bian Yanju Liu Jinsong Leng Department of Astronautical Science and Mechanics Harbin Institute of TechnologyHarbin 150001China Shanghai Institute of Satellite Engineering Shanghai 201109China Center for Composite Materials and Structures Harbin Institute of TechnologyHarbin 150080China Department of Civil Engineering Harbin Institute of TechnologyWeihai 264209China

Once China’s Tianwen-1 Mars probe arrived in a Mars orbit after a seven-month flight in the deep cold space environment,it would be urgently necessary to monitor its state and the surrounding *** address this issue,we developed a flexible deployable subsystem based on shape memory polymer composites(SMPC-FDS)with a large folding ratio,which incorporates a camera and two temperature telemetry points for monitoring the local state of the Mars orbiter and the deep space ***,we report on the development,testing,and successful application of the *** reaching its Mars remote-sensing orbit,the SMPC-FDS is designed to be in a folded state with high stiffness;after reaching orbit,it is in a deployed state with a large *** transition from the folded state to the deployed state is achieved by electrically heating the shape memory polymer composites(SMPCs);during this process,the camera on the SMPC-FDS can capture the local state of the orbiter from multiple ***,temperature telemetry points on the SMPC-FDS provide feedback on the environment temperature and the temperature change of the SMPCs during the energization *** simulating a Mars on-orbit space environment,the engineering reliability of the SMPC-FDS was comprehensively verified in terms of the material properties,structural dynamic performance,and thermal vacuum deployment *** the launch of Tianwen-1 on 23 July 2020,scientific data on the temperature environment around Tianwen-1 has been successfully acquired from the telemetry points on the SMPCFDS,and the local state of the orbiter has been photographed in orbit,showing the national flag of China fixed on the orbiter.

关键词： Flexible deployable structure Shape memory polymer composite Mars exploration Temperature telemetry On-orbit deployment

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Research on temperature distribution characteristics of oilimmersed power transformers based on fluid network decoupling

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High Voltage 2024年第5期9卷 1136-1148页

作者： Yongming Xu Ziyi Xu Congrui Ren Yaodong Wang School of Electrical and Information Engineering Changzhou Institute of TechnologyChangzhouChina School of Electrical and Electronic Engineering Harbin University of Science and TechnologyHarbinChina Department of Engineering Durham UniversityDurhamUK

Due to the complex structure and large size of large-capacity oil-immersed power transformers,it is difficult to predict the winding temperature distribution directly by numerical analysis.A 180 MVA,220 kV oil-immersed self-cooling power transformer is used as the research *** authors decouple the internal fluid domain of the power transformer into four regions:high voltage windings,medium voltage windings,low voltage windings,and radiators through fluid networks and establish the 3D fluidtemperature field numerical analysis model of the four regions,*** results of the fluid network model are used as the inlet boundary conditions for the 3D fluidtemperature numerical analysis *** turn,the fluid resistance of the fluid network model is corrected according to the results of the 3D fluid-temperature field numerical analysis *** prediction of the temperature distribution of windings is realised by the coupling calculation between the fluid network model and the 3D fluid-temperature field numerical analysis *** on this,the effect of the loading method of the heat source is also investigated using the proposed *** hotspot temperatures of the high-voltage,medium-voltage,and low-voltage windings are 89.43,86.33,and 80.96°C,***,an experimental platform is built to verify the *** maximum relative error between calculated and measured values is 4.42%,which meets the engineering accuracy requirement.

关键词： winding network distribution

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Fabrication of dense SiBCN monolith at a lower temperature and its high-temperature performance

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Journal of Advanced Ceramics 2024年第8期13卷 1198-1211页

作者： Zi-Bo Niu Daxin Li Dechang Jia Zhihua Yang Kunpeng Lin Yan Wang Paolo Colombo Ralf Riedel Yu Zhou Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150080China Key Laboratory of Advanced Structural-Function Integrated Materials and Green Manufacturing Technology Ministry of Industry and Information TechnologyHarbin 150080China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150080China Department of Industrial Engineering University of PadovaPadova 3513iItaly Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity Park 16802USA Institute of Materials Science Darmstadt University of TechnologyDarmstadt 64287Germany School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

In this study,a crack-free pyrolysis process of partially cured precursor powder compacts was developed to prepare dense silicon boron carbonitride(SiBCN)monoliths at much lower temperatures(1300℃),thereby circumventing the challenges of sintering densification(>1800℃).Unlike the elastic fracture in over-cured precursors or the viscoelastic deformation in under-cured precursors,the partially cured precursor,exhibiting elastic-plastic deformation behavior,facilitates limited nanoscale pore formation in a dense structure,achieving a balance between crack-free pyrolysis and *** to SiBCN derived from the over-cured precursor(σ=~159 MPa,K_(IC)=1.9 MPa:m^(1/2),Vickers hardness(HV)=7.8 GPa,and E=122 GPa),the resulting SiBCN monolith exhibited significantly improved mechanical properties(σ=~304 MPa,K_(IC)=3.7 MPa-m12,HV=10.6 GPa,and E=161 GPa)and oxidation *** addition,this study investigated the high-temperature performance of SiBCN monoliths,including crystallization and oxidation,and determined the oxidation kinetics induced by pore structure healing and the different oxidation mechanisms of Si-C-N and B-C-N clusters in the amorphous *** to its unique composition and structure,the SiBCN ceramic oxide layer exhibits exceptional self-healing effects on repairing the nanoporous system in the initial stage and shows outstanding high-temperature stability during prolonged oxidation,mitigating adverse effects from bubble formation and *** to the nanoporous structure,the oxidation rate is initially controlled by gas diffusion following a linear law before transitioning to oxide layer diffusion characterized by a parabolic ***,due to different valence bond configurations,Si-C-N transforms into an amorphous SiCNO structure after phase separation,unlike the nucleation and growth of residual B-N-C.

关键词： silicon boron carbonitride(SiBCN)ceramics crack-free pyrolysis amorphous structure oxidation mechanisms kinetic law

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Novel C_(sf)/SiBCN composites prepared by densifying C_(sf)/MA-SiBCN with the PIP process:Oxidation behavior and damage mechanism

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Journal of Advanced Ceramics 2024年第5期13卷 666-678页

作者： Wenhao Dou Daxin Li Bingzhu Wang Zhihua Yang Jun Chen Dechang Jia Ralf Riedel Yu Zhou Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Key Laboratory of Advanced Structural-Functional Integration Materials and Green Manufacturing Technology Harbin Institute of TechnologyHarbin 150001China Chongqing Research Institute of HIT Chongqing 401135China Beijing Institute of Control Engineering Beijing 100094China Institut für Materialwissenschaft Technische Universität DarmstadtDarmstadt D-64287Germany School of Materials Science and Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

To improve the oxidation resistance of short carbon fiber(C_(sf))-reinforced mechanically alloyed SiBCN(MA-SiBCN)(C_(sf)/MA-SiBCN)composites,dense amorphous C_(sf)/SiBCN composites containing both MA-SiBCN and polymer-derived ceramics SiBCN(PDCs-SiBCN)were prepared by repeated polymer infiltration and pyrolysis(PIP)of layered C_(sf)/MA-SiBCN composites at 1100℃,and the oxidation behavior and damage mechanism of the as-prepared C_(sf)/SiBCN at 1300–1600℃ were compared and discussed with those of C_(sf)/*** C_(sf)/MA-SiBCN composites resist oxidation attack up to 1400℃ but fail at 1500℃ due to the collapse of the porous framework,while the PIP-densified C_(sf)/SiBCN composites are resistant to static air up to 1600℃.During oxidation,oxygen diffuses through preexisting pores and the pores left by oxidation of carbon fibers and pyrolytic carbon(PyC)to the interior of the *** to the oxidative coupling effect of the MA-SiBCN and PDCs-SiBCN matrices,a relatively continuous and dense oxide layer is formed on the sample surface,and the interfacial region between the oxide layer and the matrix of the as-prepared composite contains an amorphous glassy structure mainly consisting of Si and O and an incompletely oxidized but partially crystallized matrix,which is primarily responsible for improving the oxidation resistance.

关键词： mechanically alloyed SiBCN(MA-SiBCN) polymer-derived ceramics SiBCN(PDCs-SiBCN) short carbon fiber-reinforced SiBCN(C_(sf)/SiBCN)composites polymer infiltration and pyrolysis(PIP) oxidation damage mechanism

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Deep attention convolutional neural network-based adaptive multi-source information fusion for accurate short-term photovoltaic power forecast

Deep attention convolutional neural network-based adaptive m...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Bai, Mingliang Chen, Yunxiao Zhou, Zhihao Long, Zhenhua Liu, Jinfu Yu, Daren Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Energy Science and Engineering Harbin Institute of Technology Harbin China Department of Control Science and Engineering School of Energy Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350396782

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) power generation is currently one of the most important ways to utilize solar energy. PV power has strong uncertainties and is adverse to the stability of the electricity grid. PV power forecast is an important method to tackle this issue. Conventional methods only use historical PV power data. This paper proposes a new method that uses deep Convolutional Neural Networks (CNN)-based multivariable information fusions for PV forecast. Multiple variables including global horizontal irradiation (GHI), relative humidity, air temperature, cloud thickness, wind speed, etc. are used for PV forecast. Meanwhile, clear-sky GHI estimated by McClear clear-sky models are used as physical prior knowledge. Through the attention mechanism, these variables are adaptively fused with the historical PV power data to realize an accurate PV power forecast. Forecast experiments in three-year (2017-2019) actual data of Brussels PV power stations verify the significant superiority of the proposed method over conventional forecast methods. © 2023 IEEE.

关键词： attention mechanism convolutional neural network photovoltaic power forecast solar energy

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An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

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Journal of Energy Chemistry 2023年第3期78卷 232-239,I0007页

作者： Jingzi Zhang Ke Zhang Shaomeng Xu Yi Li Chengquan Zhong Mengkun Zhao Hua-Jun Qiu Mingyang Qin X.-D.Xiang Kailong Hu Xi Lin School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055GuangdongChina Blockchain Development and Research Institute Harbin Institute of TechnologyShenzhen 518055GuangdongChina State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina Department of Materials Science and Engineering&Department of Physics Southern University of Science and TechnologyShenzhen 518055GuangdongChina

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still *** rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear *** this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting *** further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 *** average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction *** results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element ***,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 *** study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

关键词： Superconductors Integrated machine learning Superconducting critical temperature

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