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Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

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Frontiers of Structural and Civil engineering 2022年第4期16卷 401-413页

作者： Zaobao LIU Yongchen WANG Long LI Xingli FANG Junze WANG Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Institute of Deep Engineering and Intelligent TechnologyNortheastern UniversityShenyang 110819China State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan UniversityChengdu 610065China Software College Northeastern UniversityShenyang 110819China

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in *** paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big *** prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in *** TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the *** TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter *** penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next *** the contrary,the influence of the cutterhead rotation and total thrust is *** work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

关键词： hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

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Hybrid Heterogeneous Wireless Chargers Placement 18th

Hybrid Heterogeneous Wireless Chargers Placement

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Hou, Sujin Deng, Peng Ren, Meixuan Xu, Yun Zhu, Yuzhang Dai, Haipeng Wu, Libing School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9783031714665

This paper presents the first study on the problem of Hybrid heterogeneous wireless chARgers Placement (HARP), i.e., given a number of heterogeneous deployed rechargeable devices, determining deployment positions for multiple heterogeneous stationary chargers with a given cardinality of each type and partial selected devices with energy allocation for multiple mobile chargers to maximize the overall charging utility. The HARP problem is essentially NP-hard and non-submodular, which poses huge challenges. We first propose an approach to approximate the nonlinear charging power and partition the whole area into multi-power constant subareas, after that, we present a charging discretization method and construct a mapping function over the selected device set to transform the problem of deploying a single mobile charger into maximizing a monotone submodular function subject to a general routing constraint. It allows us to propose a benefit-cost ratio algorithm to solve it. Finally, we propose an efficient approximation algorithm that achieves 1-1/e5-1/e-approximation ratio to address HARP. We conducted extensive simulations and field experiments verifying that our algorithm outperforms the comparison algorithms by at least 17.49% on average. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-view Stereo 12th

Adaptive Cost Aggregation in Iterative Depth Estimation for...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Wang, Xiang Bai, Xiao Wang, Chen School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China

ISBN: (纸本)9783031463075

The deep multi-view stereo (MVS) approaches generally construct 3D cost volumes to regularize and regress the depth map. These methods are limited with high-resolution outputs since the memory and time costs grow cubically as the volume resolution increases. In this paper, we presented an multi-stage iterative depth map estimation method for MVS. In our network, the cost volume is iteratively processed by lightweight 2D convolution based GRU modules, and the multi-stage coarse-to-fine structure is adopted to speed up the depth estimation process. To further improve the 3D reconstruction quality, we make improvements from two different perspectives of adaptive cost aggregation: a view-adaptive weighting module is proposed to account for the occlusion problem in cost volume fusion, and a spatial-adaptive deformable geometric feature encoding module is introduced to the cost volume feature encoding before feeding into GRUs for stronger modeling capability. Experiments on the DTU dataset demonstrated the effectiveness of the proposed network in accuracy with remarkable efficiency performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Iterative methods

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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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Computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

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Evaluation of performance of machine learning methods in mining structure-property data of halide perovskite materials

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Chinese Physics B 2022年第5期31卷 28-35页

作者： Ruoting Zhao Bangyu Xing Huimin Mu Yuhao Fu Lijun Zhang State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEJilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy MaterialsElectron Microscopy Centerand School of Materials Science and EngineeringJilin UniversityChangchun 130012China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China International Center of Computational Method and Software Jilin UniversityChangchun 130012China

With the rapid development of artificial intelligence and machine learning(ML)methods,materials science is rapidly entering the era of data-driven materials *** models serve as the most crucial component,closely bridging material structure and material *** is a considerable difference in the prediction performance of different ML methods for material ***,we evaluated three categories(linear,kernel,and nonlinear methods)of models,with twelve ML algorithms commonly used in the materials *** addition,halide perovskite was chosen as an example to evaluate the fitting performance of different *** constructed a total dataset of 540 halide perovskites and 72 features,with formation energy and bandgap as target *** found that different categories of ML models show similar trends for different target *** them,the difference between the models is enormous for the formation energy,with the coefficient of determination(R2)range *** fitting performance between the models is closer for bandgap,with the R^(2)range *** nonlinear-ensemble model shows the best fitting performance for both the formation energy and the *** shows that the nonlinear-ensemble model,constructed by combining multiple weak learners,effectively describes the nonlinear relationship between material features and target *** addition,the extreme gradient boosting decision tree model shows the most superior results among all the models and searches for two new descriptors that are crucial for formation energy and *** work provides useful guidance for the selection of effective machine learning methods in the data-mining studies of specific material systems.

关键词： machine learning material informatics first-principles calculations halide perovskites

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The neutron transport equation in exact differential form

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Science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 87-107页

作者： Dong Liu Yang Liu Haoning Dang Kai Wang Bin Zhang Fei Wang Zhouyu Liu Yong Jiang Nuclear Power Institute of China State Key Laboratory of Advanced Nuclear Energy Technology College of Computer Science (School of Software and School of Artificial Intelligence) Sichuan University Zhejiang Institute of Modern Physics School of PhysicsZhejiang University School of Mathematics and Statistics Xi'an Jiaotong University School of Energy and Power Engineering Xi'an Jiaotong University

Derived from the Boltzmann equation,the neutron transport equation describes the motions and interactions of neutrons with nuclei in nuclear devices such as nuclear *** collision or fission effect are described as integral terms which arrive in an integro-differential neutron transport equation(IDNT).Only for mono-material or simple geometries conditions,elegant approximation can simplify the transport equation to provide analytic *** solve this integro-differential equation becomes a practical engineering *** development of deep-learning techniques provides a new approach to solve them but for some complicated conditions,it is also time *** optimize solving the integro-differential equation particularly under the deep-learning method,we propose to convert the integral terms in the integro-differential neutron transport equation into their corresponding antiderivatives,providing a set of fixed solution constraint conditions for these antiderivatives,thus yielding an exact differential neutron transport equation(EDNT).The paper elucidates the physical meaning of the antiderivatives and analyzes the continuity and computational complexity of the new transport equation *** illustrate the significant advantage of ENDT,numerical validations have been conducted using various numerical methods on typical benchmark *** numerical experiments demonstrate that the EDNT is compatible with various numerical methods,including the finite difference method(FDM),finite volume method(FVM),and *** to the IDNT,the EDNT offers significant efficiency advantages,with reductions in computational time ranging from several times to several orders of *** EDNT approach may also be applicable for other integro-differential transport theories such as radiative energy transport and has potential application in astrophysics or other fields.

关键词： neutron transport equation exact differential form integro-differential equation deep learning antiderivative transformation

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Learning real-time automata

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Science China(Information Sciences) 2021年第9期64卷 57-73页

作者： Jie AN Lingtai WANG Bohua ZHAN Naijun ZHAN Miaomiao ZHANG School of Software Engineering Tongji University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences

Real-time automata(RTAs) are a subclass of timed automata with only one clock which resets at each transition. In this paper, we present an active learning algorithm for deterministic real-time automata(DRTAs) in both continuous-time semantics and discrete-time semantics. For a target language recognized by a DRTA A, we convert the problem of learning DRTA A to the problem of learning a canonical real-time automaton A with the same recognized language, i.e., L(A) = L(A). The algorithm is inspired by existing learning algorithms for symbolic automata.

关键词： automaton learning active learning real-time automata

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Understanding and probing progression of localized corrosion on inner walls of steel pipelines:an overview

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Journal of Iron and Steel Research International 2025年第1期32卷 1-18页

作者： Ming-yu Wang Hai-yuan Yao Yong-fei Liu Ye-sen Zhu Wan-bin Chen Yun-ze Xu Yi Huang School of Naval Architecture and Ocean Engineering Dalian University of TechnologyDalian 116024LiaoningChina China National Offshore Oil Corporation Research Institute Co. Ltd.Beijing 100028China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024LiaoningChina

Corrosion poses a major threat to the safety of transportation ***,it is crucial to have an in-depth understanding of corrosion mechanisms in pipeline steels for the effective management of pipeline *** research on corrosion mechanisms relies on the use of efficient and reliable corrosion monitoring and analysis *** advancements in corrosion monitoring techniques specifically designed for the localized corrosion monitoring were aimed to be introduced,and a comprehensive overview of recent progress in understanding the localized corrosion mechanisms in pipeline steels was *** on the different corrosive environments encountered,the localized corrosion issues inside pipelines are classified into two categories:localized corrosion primarily influenced by electrochemical processes and localized corrosion controlled by both electrochemical and mechanical ***,a thorough analysis of the synergistic effects between micro-cell and macro-cell currents,as well as the interplay of mechanics and electrochemistry is ***,recommendations for future research on the mechanisms of internal localized corrosion in pipelines are provided.

关键词： Pipeline steel Localized corrosion Corrosion type Corrosion monitoring Corrosion mechanism

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Accelerated Peridynamic Computation on GPU for Quasi-static Fracture Simulations

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Journal of Peridynamics and Nonlocal Modeling 2024年第1期6卷 206-229页

作者： Zhong, Jiandong Han, Fei Zhang, Ling Department of Engineering Mechanics Dalian University of Technology Dalian 116023 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian 116023 China

Due to the adoption of integral equations instead of partial differential equations to describe the deformations of materials, the peridynamics has advantages in dealing with fracture problems. However, the non-local effect of peridynamics brings too much computation cost to make large-scale engineering applications challenging. In this paper, a parallel algorithm in the Compute Unified Device Architecture (CUDA) framework is presented to speed up the computational process of a peridynamic model for quasistatic fracture simulations on GPU, in which the peridynamic model is numerically implemented by the peridynamics-based finite element method (PeriFEM) [1]. The parallel algorithm makes crack simulations by the pure peridynamics with millions of degrees of freedom possible with one GPU. To validate the accuracy and efficiency of the parallel algorithm based on PeriFEM, several numerical benchmarks are performed, and the results are compared with those obtained by the finite element method (FEM) and the serial algorithm. The results of the comparison show that the presented parallel algorithm is effective and efficient. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Crack simulation CUDA Finite element method Parallel computing Peridynamic

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Polyzwitterionic cross-linked double network hydrogel electrolyte enabling high-stable Zn anode

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Nano Research 2024年第6期17卷 5278-5287页

作者： Mengyu Shi Junlong Zhang Guochuan Tang Ben Wang Sen Wang Xiaoxian Ren Guojie Li Weihua Chen Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China College of Chemistry and Green Catalysis Center Zhengzhou UniversityZhengzhou 450001China Research Institute of Interdisciplinary Science and School of Materials Science and Engineering Dongguan University of TechnologyDongguan 523808China

Zn metal anode suffers from dendrite issues and passive byproducts,which severely plagues the practical application of aqueous Zn metal ***,a polyzwitterionic cross-linked double network hydrogel electrolyte composed of physical crosslinking(hyaluronic acid)and chemical crosslinking(synthetic zwitterionic monomer copolymerized with acrylamide)is introduced to overcome these *** the one hand,highly hydrophilic physical network provides an energy dissipation channel to buffer stress and builds a H_(2)O-poor interface to avoid side *** the other hand,the charged groups(sulfonic and imidazolyl)in chemical crosslinking structure build anion/cation transport channels to boost ions’kinetics migration and regulate the typical solvent structure[Zn(H_(2)O)_(6)]^(2+)to R-SO_(3)^(−)[Zn(H_(2)O)_(4)]^(2+),with uniform electric field distribution and significant resistance to dendrites and parasitic *** on the above functions,the symmetric zinc cell exhibits superior cycle stability for more than 420 h at a high current density of 5 mA·cm^(−2),and Zn||MnO_(2)full cell has a reversible specific capacity of 150 mAh·g^(−1)after 1000 cycles at 2 C with this hydrogel ***,the pouch cell delivers impressive flexibility and cyclability for energy-storage applications.

关键词： Zn anode dendrites parasitic reactions polyzwitterion double network hydrogel electrolyte

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