ISA transactions最新文献_第3页

The fast bearing diagnosis based on adaptive GSR of fault feature amplification in scale-transformed fractional oscillator 基于尺度变换分数阶振荡器故障特征放大的自适应GSR快速轴承诊断。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.11.044

Kehan Chen , Ruoqi Zhang , Lin Meng , Xingyuan Zheng , Kun Wang , Huiqi Wang

{"title":"The fast bearing diagnosis based on adaptive GSR of fault feature amplification in scale-transformed fractional oscillator","authors":"Kehan Chen , Ruoqi Zhang , Lin Meng , Xingyuan Zheng , Kun Wang , Huiqi Wang","doi":"10.1016/j.isatra.2024.11.044","DOIUrl":"10.1016/j.isatra.2024.11.044","url":null,"abstract":"<div><div>From the noise-assisted perspective of stochastic resonance (SR), fractional system has been adopted to enhance the diagnostic performance of mechanical faults by utilizing the previous state information in mechanical degradation process, but the computation is extremely time-consuming. To address this challenge, we develop a fast diagnosis method leveraging the mechanism of generalized SR (GSR)-based active energy conversion in fluctuating-damping fractional oscillator (FDFO). Through the analysis of system stationary response, we propose a theoretical index known as fault feature amplification (FFA), which effectively replaces the time-consuming numerical solution in multi-parameter optimization, leading to a remarkable reduction in the time complexity of the adaptive diagnosis algorithm. This improvement brings about significant benefits, notably simplifying the diagnosis flow. Based on the results of performance evaluation in diagnosing simulated bearing signals, the proposed method exhibits a comprehensive superiority in identifying ability and diagnosis efficiency. Finally, this method has been further validated in experimental diagnosis, especially for some challenging cases, providing strong support for engineering applications, particularly in the fast diagnosis of complex operating environments.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 124-141"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy 结合多空间变尺度自适应滤波和前馈混合策略的跨速主轴电机轴承故障诊断。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.11.045

Hao Zhou, Jianzhong Yang, Qian Zhu, Jihong Chen

{"title":"Cross-speed spindle motor bearings fault diagnosis combined with multi-space variable scale adaptive filter and feedforward hybrid strategy","authors":"Hao Zhou, Jianzhong Yang, Qian Zhu, Jihong Chen","doi":"10.1016/j.isatra.2024.11.045","DOIUrl":"10.1016/j.isatra.2024.11.045","url":null,"abstract":"<div><div>The vibration signal of the spindle motor contains complicated mixed modulation harmonics and background noise when computer numerical control (CNC) machine tools perform machining tasks. Additionally, frequent changes in the running speed of the spindle motor cause significant variations in the signal feature distribution, making fault diagnosis challenging. The adaptive sinusoidal fusion convolutional neural networks (ASFCNN) is proposed to achieve cross-speed spindle motor bearings fault diagnosis. The ASFCNN extracts multi-spatial and variable-scale fault features through the multi-spatial variable-scale adaptive sinusoidal filter (MVASF) for noise reduction. And a multi-level feedforward hybrid strategy (MFHS) is designed to fuse multi-layer features of the convolutional neural network (CNN) and time sequence information for fault feature enhancement. The proposed method is evaluated on a multi-source spindle motor dataset under real working conditions. Experimental results show that the ASFCNN model significantly outperforms the compared classical models in terms of diagnosis accuracy, the effectiveness and interpretability are validated through the visualization methods.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 368-380"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible payload transportation using cooperative space manipulators with statics compensation 带静力补偿的协同空间机械臂柔性载荷运输。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.001

Mingyan Xie, Ti Chen, Shihao Ni, Chenlu Feng

引用次数: 0

Design of trot gait parameters planning system for parallel quadruped robot based on virtual model controller and fuzzy neural network 基于虚拟模型控制器和模糊神经网络的并联四足机器人小跑步态参数规划系统设计

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.018

Yuhang Ying , Xin Li , Zhikai Xu , Yang Yu , Junming Xu , Feiyun Xiao

{"title":"Design of trot gait parameters planning system for parallel quadruped robot based on virtual model controller and fuzzy neural network","authors":"Yuhang Ying , Xin Li , Zhikai Xu , Yang Yu , Junming Xu , Feiyun Xiao","doi":"10.1016/j.isatra.2024.12.018","DOIUrl":"10.1016/j.isatra.2024.12.018","url":null,"abstract":"<div><div>The capability to achieve fast motion in varying road conditions is a crucial research aspect in the dynamic control of quadruped robot. In this study, a gait parameters planning system for quadruped robot based on virtual model controller (VMC) and fuzzy neural network controller (FNNC) is proposed. According to the expert knowledge, the FNNC is designed to help optimize the parameters in the central pattern generator and virtual model controller (CPG-VMC). This affect the performance of the fast motion indicated by the attitude plantar force and a weight adaptive law is designed and implemented to improve the capability of traversing unprecedented road conditions. To better analyze controller efficiency, the concept called cost of transport (CoT) is introduced to serve as the evaluation criteria for the performance of controller. Both the simulation and prototype test are implemented to validate the effect of the proposed method. Experimental results show that the FNNC-based gait parameters planning system can accurately detect the flaws in the parameters, help adjusting the parameters in real-time regarding the different road conditions, and reducing the CoT and the vibration.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 510-529"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of remaining useful life (RUL) for pneumatic actuator without apriori RUL history: A hybrid prognostic approach 无先验RUL历史的气动执行器剩余使用寿命（RUL）的估计：一种混合预测方法。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.002

Priyadarshini Mahalingam , D. Kalpana , T. Thyagarajan

{"title":"Estimation of remaining useful life (RUL) for pneumatic actuator without apriori RUL history: A hybrid prognostic approach","authors":"Priyadarshini Mahalingam , D. Kalpana , T. Thyagarajan","doi":"10.1016/j.isatra.2024.12.002","DOIUrl":"10.1016/j.isatra.2024.12.002","url":null,"abstract":"<div><div>Predicting the Remaining Useful Life (RUL) of an industrial pneumatic actuator is crucial for enhancing maintenance strategies, reducing downtime and optimizing resource allocation. However, estimation becomes challenging when no historical RUL data is available for modeling. In this paper, a novel hybrid prognostic approach that combines Dynamic Time Warping (DTW), Exponential Degradation Model (EDM) and Random Forest Regressor (RFR) is proposed to estimate the RUL of pneumatic actuators under the absence of apriori RUL history. The DTW technique is employed to identify the onset of potential degradation. By aligning the healthy and faulty data, DTW provides a robust measure of distance and time at the point of deviation as the threshold value. Subsequently, the EDM is introduced to capture the degradation pattern in the actuator behavior. The EDM accounts for the relationship between threshold value, operating conditions, degradation rate and exponential coefficients through curve fitting methods. To further enhance prediction accuracy, RFR is employed to predict the RUL based on input features of aligned data from DTW and the derived degradation rates from EDM. In the simulation studies, the proposed methodology is applied to a synthetic dataset and benchmark DAMADICS dataset of the industrial pneumatic actuator in sugar processing unit to estimate RUL. The estimated RUL for each health indicator is quantified and the severity of each fault is discussed. The proposed method is implemented on a real time laboratory setup. The results are also validated on the benchmark NASA turbo-engine dataset by comparing the actual and estimated RULs, achieving 82.5 % range-based accuracy.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 434-450"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disturbance rejection-based adaptive non-singular fast terminal sliding mode control for a quadrotor under severe turbulent wind 基于扰动抑制的四旋翼自适应非奇异快终端滑模控制。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.003

Armando Miranda-Moya , Herman Castañeda , Hesheng Wang

{"title":"Disturbance rejection-based adaptive non-singular fast terminal sliding mode control for a quadrotor under severe turbulent wind","authors":"Armando Miranda-Moya , Herman Castañeda , Hesheng Wang","doi":"10.1016/j.isatra.2024.12.003","DOIUrl":"10.1016/j.isatra.2024.12.003","url":null,"abstract":"<div><div>This paper presents the design of a disturbance rejection-based control strategy for a quadrotor unmanned aerial vehicle subject to model uncertainties and external disturbances described by turbulent wind gusts of severe intensity. First, an extended state observer is introduced to supply full-state and total disturbance estimations within a fixed time regardless of initial estimation errors. Then, an adaptive non-singular fast terminal sliding mode controller with a single-gain structure is proposed to reduce the tuning complexity and drive the pose of the rotorcraft while providing practical finite-time convergence, robustness to bounded external disturbances, non-overestimation of its control gain, and chattering attenuation. Furthermore, the stability of the closed-loop system is guaranteed through homogeneity and Lyapunov theory. Simulation results obtained through the ROS/Gazebo framework demonstrate graphically and quantitatively that the proposed observer-based controller reduces the influence of perturbations and requires less torque effort than existing methods in the presence of sensor noise.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 248-257"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI 具有可解释性人工智能的六自由度拟人机器人优化运动学逆建模和关节角预测。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.008

Rakesh Chandra Joshi , Jaynendra Kumar Rai , Radim Burget , Malay Kishore Dutta

{"title":"Optimized inverse kinematics modeling and joint angle prediction for six-degree-of-freedom anthropomorphic robots with Explainable AI","authors":"Rakesh Chandra Joshi , Jaynendra Kumar Rai , Radim Burget , Malay Kishore Dutta","doi":"10.1016/j.isatra.2024.12.008","DOIUrl":"10.1016/j.isatra.2024.12.008","url":null,"abstract":"<div><div>Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models. This study involves rigorous evaluation and Bayesian optimization for hyperparameter tuning to identify the optimal regressor, balancing both accuracy and computational efficiency. Utilizing five-fold cross-validation on a publicly available dataset, the selected model demonstrates exceptional performance in predicting six joint angles for end effector configuration, yielding an average mean square error of 1.934 × 10<sup>−3</sup> to 3.522 × 10<sup>−3</sup>. Its computational efficiency, with a prediction time of approximately 1.25 ms per sample, makes it a practical choice. Additionally, the study employs Explainable AI, using SHAP (SHapley Additive exPlanations) analysis to gain an understanding of feature importance. This analysis not only enhances model interpretability but also reaffirms the efficacy in this challenging multi-input multi-output predictive task. This research advances state-of-the-art models and neural networks by prioritizing computational efficiency alongside accuracy—a critical yet often overlooked factor. Pioneering a significant advancement in anthropomorphic robot kinematics, it balances accuracy and efficiency, offering practical robotic automation solutions.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 340-356"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive neural network approach for resilient leader-following consensus control of multi-agent systems under cyber-attacks 网络攻击下多智能体系统弹性领导-跟随共识控制的自适应神经网络方法。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.11.046

Muhammad Mamoon , Ghulam Mustafa , Naeem Iqbal , Muhammad Rehan , Ijaz Ahmed , Muhammad Khalid

{"title":"An adaptive neural network approach for resilient leader-following consensus control of multi-agent systems under cyber-attacks","authors":"Muhammad Mamoon , Ghulam Mustafa , Naeem Iqbal , Muhammad Rehan , Ijaz Ahmed , Muhammad Khalid","doi":"10.1016/j.isatra.2024.11.046","DOIUrl":"10.1016/j.isatra.2024.11.046","url":null,"abstract":"<div><div>This paper addresses the dynamic neural networks (DNNs) based resilient leader-following consensus control of multi-agent systems (MASs) under unidentified false data injection (FDI) attacks. We have examined generic linear leader-following agents in the context of stochastic FDI attacks on the network topology. When information is sent from one agent to another, it is altered as a result of the attacks. In this study, we have introduced a new method to identify FDI attacks using DNNs. The DNNs adapt by adjusting their weights based on system errors, allowing them to approximate the nonlinear dynamics of these attacks using a state translation method for the receiving agent, as we do not have any estimate or the information of the states of the sending agent. The attacks considered in this study are network attacks, which are easier to initiate but harder to counter compared to the traditional input–output attacks. The unknown FDI attacks are estimated with the help of DNNs, which allow the evaluation and isolation of large amplitude attack signals. Unlike previous methods, this approach handles probabilistic stochastic FDI attacks and negates attack estimations from the system dynamics, enhancing the controller resilience. Additionally, the paper extends resilient consensus control to the output feedback methodology, providing a feasible consensus method for MASs under stochastic FDI attacks. Simple design constraints for the consensus control are introduced, and the approach is validated through simulations with six unmanned ground vehicles (UGVs).</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 20-34"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust ALOHA and sequential clustering-based mode estimator for low frequency oscillations in power system using synchrophasors 利用同步传感器对电力系统低频振荡进行基于 ALOHA 和顺序聚类的鲁棒模式估计。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.005

Manoranjan Sahoo, Shekha Rai

{"title":"A robust ALOHA and sequential clustering-based mode estimator for low frequency oscillations in power system using synchrophasors","authors":"Manoranjan Sahoo, Shekha Rai","doi":"10.1016/j.isatra.2024.12.005","DOIUrl":"10.1016/j.isatra.2024.12.005","url":null,"abstract":"<div><div>Accurate estimation of low frequency modes in power system are very much important for improving small signal stability. The parametric model parameters estimator known as Total least square estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) works effectively even in noisy conditions. However, this model parameter estimator requires prior information about numbers of modes of the signal. There are different Model order (MO) estimation techniques discussed in the recent past, which consider the significant eigenvalues of auto-correlation matrix (ACM) for its estimation. As the eigenvalues of ACM highly affected by bad measurements and Outliers, making these techniques inefficient and harder to automate in real time. So, to overcome aforementioned limitations, this paper proposes a robust mode estimation technique that can precisely detect the signal low frequency modes even in the presence of high variance noise and outliers. So, in this proposed work, an annihilating filter-based low-rank Hankel matrix (ALOHA) technique is implemented to obtain the rank deficient Hankel matrix to nullify the existence of noise and outliers in Phasor measurement unit (PMU) signal, thereafter approximated low rank Hankel matrix is considered for estimation of signals dominant modes. Wherein a sequential clustering technique (Sequential K-Mean++) is implemented for detection of numbers of prominent low frequency modes by segregating eigenvalues of ACM into two opponents i.e the signal and noise subspace. Thereafter the estimated MO is considered for estimation of modes through TLS-ESPRIT. The robustness of the proposed technique is validated by scheduling comparative study with recently developed techniques for synthetic signal, two area data, real PMU data of Western Electricity Coordinating Council (WECC) and oscillatory power data obtained from Western System Coordinating Council (WSCC) 9 bus system and IEEE68 bus system simulated through Real Time Digital Simulator (RTDS).</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 631-648"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142823001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing unlabeled data: Enhanced rare earth component content prediction based on BiLSTM-Deep autoencoder 利用未标记数据：基于BiLSTM-Deep自编码器的增强稀土成分含量预测。

IF 6.3 2区计算机科学

ISA transactions Pub Date : 2025-02-01 DOI: 10.1016/j.isatra.2024.12.027

Wenhao Dai , Rongxiu Lu , Jianyong Zhu , Pengzhan Chen , Hui Yang

{"title":"Harnessing unlabeled data: Enhanced rare earth component content prediction based on BiLSTM-Deep autoencoder","authors":"Wenhao Dai , Rongxiu Lu , Jianyong Zhu , Pengzhan Chen , Hui Yang","doi":"10.1016/j.isatra.2024.12.027","DOIUrl":"10.1016/j.isatra.2024.12.027","url":null,"abstract":"<div><div>Traditional data-driven models for predicting rare earth component content are primarily developed by relying on supervised learning methods, which suffer from limitations such as a lack of labeled data, lagging, and poor usage of a major amount of unlabeled data. This paper proposes a novel prediction approach based on the BiLSTM-Deep autoencoder enhanced traditional LSSVM algorithm, termed BiLSTM-DeepAE-LSSVM. This approach thoroughly exploits the implicit information contained in copious amounts of unlabeled data in the rare earth production process, thereby improving the traditional supervised prediction method and increasing the accuracy of component content predictions. Initially, a BiLSTM autoencoder is established for unsupervised training on the rare earth production process data, enabling the extraction of inherent time series characteristics. Subsequently, boolean vectors are introduced in the Deep autoencoder training process to perform masking operations on the input data, simulating scenarios with noise and missing data. This is facilitated by their adherence to Bernoulli distributions, which allow for the random setting of certain input vector dimensions to zero. Additionally, the Deep autoencoder is capable of extracting high-dimensional implicit features from the data. After that, the conventional supervised prediction technique, least squares support vector machine (LSSVM), is fused with the implicit characteristics derived from the well-constructed BiLSTM-Deep autoencoder, culminating in the creation of a prediction model for rare earth component content. Ultimately, the simulation verification using LaCe/PrNd extraction field data demonstrates the effectiveness of the proposed approach in harnessing substantial quantities of unlabeled data from the rare earth extraction production process, thereby bolstering the accuracy of model predictions.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"157 ","pages":"Pages 357-367"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0