Expert Systems with Applications最新文献

{"title":"Spatial spiral tube multi-roller bending: Accurate axial prediction utilizing AWPSO-FECAM-LSTM framework","authors":"Zili Wang ,&nbsp;Yonglin Tao ,&nbsp;Shuyou Zhang ,&nbsp;Xiaojian Liu ,&nbsp;Yaochen Lin ,&nbsp;Liangyou Li ,&nbsp;Jianrong Tan ,&nbsp;Zheyi Li","doi":"10.1016/j.eswa.2025.128960","DOIUrl":"10.1016/j.eswa.2025.128960","url":null,"abstract":"<div><div>The multi-roller bending (MRB) process, characterized by its high stability and flexible mold adaptability, is widely employed in the bending forming of spatial metal tubes (such as spatial spiral tubes (SSTs)). However, due to the fewer mold constraints of the already bent-formed section, the bent tube exhibits irregular axial springback, resulting in uncontrollable axial deviations. To improve forming accuracy, this study proposes a novel AWPSO-FECAM-LSTM framework that predicts the axis coordinates of the SSTs formed with MRB. The framework incorporates two prediction modes: the Angle-Regulation-Based (ARB) model, which predicts points based on the same angle, and the Segment-Regulation-Based (SRB) model, which predicts points based on the same segment. The FECAM module extracts frequency-domain features, thereby enhancing the model’s ability to capture both temporal and frequency characteristics. Meanwhile, AWPSO optimizes hyperparameters using time-decay inertia weights and adaptive acceleration coefficients. Validated through bending experiments and finite element (FE) simulations, the model achieves a mean absolute percentage error (MAPE) of 0.98% and a mean squared error (MSE) of 0.000042, outperforming baseline models such as PSO-LSTM and vanilla LSTM. The ARB and SRB models collectively enable precise prediction of tube axis coordinates, with progressive prediction modes effectively reducing error accumulation. This framework demonstrates significant potential for real-time compensation in digital twin applications, advancing high-precision manufacturing of spatial metal tubes.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128960"},"PeriodicalIF":7.5,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-condition milling cutter wear prediction based on split-channel information re-fusion and domain adaptation","authors":"Wujun Yu ,&nbsp;Hongfei Zhan ,&nbsp;Rui Wang ,&nbsp;Junhe Yu ,&nbsp;Dewen Kong ,&nbsp;Guojun Huang","doi":"10.1016/j.eswa.2025.128888","DOIUrl":"10.1016/j.eswa.2025.128888","url":null,"abstract":"<div><div>Accurate tool wear prediction is crucial for ensuring superior quality and operational efficiency in cutting processes, avoiding part defects, and minimizing economic losses. However, due to variations in cutting parameters such as cutting speed, feed rate, and depth of cut under different working conditions, the collected sensor signals (e.g., current, acceleration, acoustic emission) exhibit significant differences in amplitude, spectral density, and temporal feature distribution. This results in a distribution shift of the signal data, making it difficult for traditional models to generalize across multiple working conditions and leading to a notable decline in prediction performance. As a result, traditional models struggle to adapt to these changes and fail to capture wear patterns under diverse conditions, leading to low prediction accuracy across varying working scenarios. Therefore, a multi-condition wear prediction method for the milling cutter is proposed in this paper. This method is based on split-channel information re-fusion and domain adaptation, utilizing cutting signal and machining process data. A split-channel information re-fusion module is proposed to align the spatial wear feature distribution across different working conditions. The module first separates the multidimensional cutting signal and processes data along channel directions. Then, different convolutions for heterogeneous feature extraction are applied, and the multidimensional correlated features along the channels are fused for re-extraction. This process ensures that the diverse working condition features are fully captured and aligned. Moreover, the Mamba is improved due to the inconsistent distribution of temporal wear features under different working conditions. Furthermore, the GLMamba is constructed to fuse global–local attention in parallel configuration with selective state space models (SSMs). The loss of local information during the global compression of the selective SSMs is avoided by updating global information with local information features, ensuring accurate temporal feature extraction and alignment. The Maximum Mean Discrepancy (MMD) algorithm is utilized to quantify the distributional differences between domains in the fully connected layer, aligning feature distributions between the source and target domains. Experimental results on the NASA and Nanjing University of Aeronautics and Astronautics (NUAA) datasets demonstrate that the proposed method achieves significant performance advantages under various working conditions. Specifically, the average RMSE and MAE on the NASA dataset are reduced to 0.0311  mm and 0.0253  mm, respectively, while on the NUAA dataset they are reduced to 0.0058  mm and 0.00425  mm, outperforming several mainstream benchmark models. This study provides a novel solution for predicting milling cutter wear under various working conditions.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128888"},"PeriodicalIF":7.5,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining cell subpopulation identification and cell fate mapping using cell energy","authors":"Chunshen Long ,&nbsp;Hanshuang Li ,&nbsp;Zhifang Li,&nbsp;Jia Zhang,&nbsp;Qilemuge Xi,&nbsp;Yongchun Zuo","doi":"10.1016/j.eswa.2025.128999","DOIUrl":"10.1016/j.eswa.2025.128999","url":null,"abstract":"<div><div>Despite rapid advances in single-cell data analysis algorithms, the accurate and robust mapping of cell fate dynamics across diverse biological systems remains a challenge. Here, we introduce CellEnergy, a novel method capable of assigning a cell energy indicator based on Gene Local Network Energy (GLNE), suitable for quantitatively describing attractor states. Benchmarking against 8 other advanced models, CellEnergy demonstrates superior performance in quantifying cellular plasticity, assessing cell population heterogeneity, automatically detecting terminal cell states, and tracking the dynamics of cell fate transitions. Moreover, CellEnergy shows robustness in rare cells and high dropout ratio datasets, all while significantly reducing computational costs. CellEnergy quantitatively evaluates the heterogeneity of mouse embryonic stem cells and identifies a cell subpopulation that exhibits consistent high differentiation potential, defining 2-cell-like cells. For mouse pancreatic development, CellEnergy accurately identifies four terminal cell states, including the rare Delta cells, and profiles the local network energy change trends of relevant genes across different lineages. Applications of CellEnergy reveal the impact of small molecules on cell plasticity from an energy perspective in human chemical reprogramming data. We further demonstrate the utility of CellEnergy in identifying plasticity-associated genes implicated in the tumorigenesis and prognosis of head and neck squamous cell carcinoma and melanoma. CellEnergy characterizes key branching points in human hematopoietic lineage commitment and decodes the distinct regulatory dynamics of erythropoiesis and megakaryopoiesis.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128999"},"PeriodicalIF":7.5,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An energy-aware scheduling method for parallel tasks based on an adaptive differential evolution algorithm in a multi-cloud environment","authors":"Qin Wang ,&nbsp;Yongsheng Hao ,&nbsp;Yue Xu ,&nbsp;Tinhuai Ma ,&nbsp;Xin Zhang","doi":"10.1016/j.eswa.2025.129008","DOIUrl":"10.1016/j.eswa.2025.129008","url":null,"abstract":"<div><div>With the increasing data and computing scale, the energy consumption of computing is increasing greatly. This study focuses on the scheduling problem of parallel tasks in a cloud environment. Most of the work models tasks according to DAG (Directed Acyclic Graph) and selects a working state based on DVFS (Dynamic Voltage Frequency Scaling) to reduce energy consumption and meet other QoSs (Quality of Services). In contrast to these works, we focus on the task in which parallelism cannot be changed during execution, and the task model supports slot time in a heterogeneous environment. In the paper, we propose a SAEADE (An Self-adaption Differential Evolution Energy-Aware Algorithm) to schedule resources, which considers the parallelism of tasks, the selection of resources, and their working states simultaneously. SAEADE initializes the data by the sine function. During crossover and mutation operations, SAEADE selects the strategy by a roulette algorithm among the three methods: (1) DE-Rand, (2) DE-current-to-best, and DE-rand-to-best. Simulations show that SAEADE performs well in terms of makespan, energy consumption, the number of completed tasks, and the number of completed instructions. Compared to the performance of PSO (Particle Swarm Optimization), SAEADE also has good performance in efficiency.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 129008"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and hybrid intelligence for wind energy optimization: A comprehensive state-of-the-art review","authors":"Ashutosh Kumar Dubey ,&nbsp;Abhishek Kumar ,&nbsp;Isaac Segovia Ramírez ,&nbsp;Fausto Pedro García Márquez","doi":"10.1016/j.eswa.2025.128926","DOIUrl":"10.1016/j.eswa.2025.128926","url":null,"abstract":"<div><div>Wind energy plays a pivotal role in the global transition toward sustainable energy. However, its intermittent and stochastic nature presents challenges in achieving optimal performance, reliability, and seamless grid integration. Recent advances in machine intelligence—including machine learning (ML), deep learning (DL), and reinforcement learning (RL)—offer powerful tools to address these challenges across forecasting, control, maintenance, and diagnostics. This systematic review provides a comprehensive evaluation of how machine intelligence has contributed to the optimization of wind energy systems. These techniques have been applied to enhance turbine-level performance, reduce power losses, predict faults, and maximize energy yield under uncertain and dynamic conditions. Particular emphasis is placed on hybrid models that combine data-driven algorithms with physical dynamics and domain heuristics, enabling real-time, predictive, and autonomous wind farm operations. Furthermore, the study critically examines integration barriers such as noisy SCADA data, regulatory compliance, computational costs, and sustainability trade-offs. The findings highlight that multi-objective optimization—balancing energy production, system resilience, and cost efficiency—is central to the most successful implementations. Hybrid frameworks, explainable artificial intelligence (AI), edge computing, and transfer learning are identified as key enablers for scalable deployment. This review offers a comprehensive roadmap for the application of machine intelligence in advancing wind energy optimization and provides actionable insights for researchers, engineers, and policymakers committed to developing intelligent, adaptive, and sustainable wind power infrastructures.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128926"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperation dynamics on hypergraphs with punishment and Q-learning","authors":"Kuan Zou ,&nbsp;Changwei Huang","doi":"10.1016/j.eswa.2025.128989","DOIUrl":"10.1016/j.eswa.2025.128989","url":null,"abstract":"<div><div>Punishment has been proved to be a useful mechanism on pairwise interaction networks for promoting cooperation. However, these networks cannot effectively describe higher-order interactions in the multi-agent system, while hypergraph as a higher-order network has aroused extensive interests of researchers. Here, we study the evolutionary dynamics of spatial public goods game on uniform random hypergraphs with peer punishment mechanism. In that model, each agent chooses to become a cooperator, defector or punisher, and each punisher pay a cost to make each defector bear a fine. Different from the imitation rules in previous studies, we adopt self-regarding Q-learning algorithm to update agent’s strategy where agents take an action based on their historical experience to maximize their reward. Simulation results show that there is a moderate synergy factor can obtain the best result of the evolution of cooperation. For a certain relatively large synergy factor, there exists a combination of cost and fine to optimally promote cooperation. Furthermore, the theoretical analysis results are consistent with the simulation results.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128989"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel EPI-guided network with progressive fusion for light field reconstruction","authors":"Baoshuai Wang,&nbsp;Yilei Chen,&nbsp;Xinpeng Huang,&nbsp;Ping An","doi":"10.1016/j.eswa.2025.128964","DOIUrl":"10.1016/j.eswa.2025.128964","url":null,"abstract":"<div><div>Dense light fields (LFs) hold significant potential in computer vision. However, since the existing LF acquisition devices struggle to capture high-quality dense LFs, researchers have proposed computational methods to reconstruct dense LFs from sparse LFs. Nevertheless, existing methods usually learn multiple features of the LF equally to achieve dense LF reconstruction while overlooking the discrepancies and correlations between different features. The specificity of epipolar plane images (EPIs) features as an important bridge between the LF spatial and angular information is worth exploring in depth. In this paper, we propose a novel EPI-guided network to emphasize differentiated learning of EPI features. The network extracts new guiding information from EPI global features and guides spatial and angular features to establish spatial-angular correlations of LF data effectively. We also introduce a progressive feature fusion strategy, which sequentially fuses the LF spatial, angular, and EPI features. This strategy fully explores the correlations between each pair of features and achieves differentiated learning among different features. The quantitative and qualitative experimental results demonstrate that our network achieves state-of-the-art reconstruction performance on large and small disparities datasets. Codes will be released at <span><span>https://github.com/Baoshuai129/EGPFNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128964"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Audio-visual occlusion-robust gender recognition and age estimation approach based on multi-task cross-modal attention","authors":"Maxim Markitantov ,&nbsp;Elena Ryumina ,&nbsp;Alexey Karpov","doi":"10.1016/j.eswa.2025.127473","DOIUrl":"10.1016/j.eswa.2025.127473","url":null,"abstract":"<div><div>Gender recognition and age estimation are essential tasks within soft biometric systems, where identifying these characteristics supports a wide range of applications. In real-world scenarios, challenges such as partial facial occlusion complicate these tasks by obscuring crucial voice and facial characteristics. These challenges highlight the importance of development of robust and efficient approaches for gender recognition and age estimation. In this study, we develop a novel audio-visual Occlusion-Robust GENder recognition and AGE estimation (ORAGEN) approach. The proposed approach is based on intermediate features of unimodal transformer-based models and two Multi-Task Cross-Modal Attention (MTCMA) blocks, which predict gender, age, and protective mask type using voice and facial characteristics. We conduct detailed cross-corpus experiments on the TIMIT, aGender, CommonVoice, LAGENDA, IMDB-Clean, AFEW, VoxCeleb2, and BRAVE-MASKS corpora. The proposed unimodal models outperform State-of-the-Art approaches for gender recognition and age estimation. We investigate the impact of various protective mask types on the performance of audio-visual gender recognition and age estimation. The results show that the current large-scale data are still insufficient for a robust gender recognition and age estimation in partial facial occlusion conditions. On the Test subset of the VoxCeleb2 corpus, the proposed approach showed Unweighted Average Recall (UAR) of 99.51%, Mean Absolute Error (MAE) of 5.42, and UAR of 100% for gender recognition, age estimation, and protective mask type recognition, respectively, while on the Test subset of the BRAVE-MASKS corpus, it showed UAR=96.63%, MAE=7.52, and UAR=95.87%, for the same tasks. These results indicate that using data of people wearing protective masks, as well as including the protective mask type recognition task, yields performance gains on all tasks considered. ORAGEN can be integrated into the OCEAN-AI framework for optimizing Human Resources processes, as well as into expert systems with practical applications in various domains including forensics, healthcare, and industrial safety. We make the source code publicly available at <span><span>https://smil-spcras.github.io/ORAGEN/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 127473"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-scene hyperspectral image classification based on cross-domain feature extraction and category decision collaborative optimization","authors":"Chi Wang ,&nbsp;Ronghua Shang ,&nbsp;Yangyang Li ,&nbsp;Jie Feng ,&nbsp;Songhua Xu","doi":"10.1016/j.eswa.2025.128842","DOIUrl":"10.1016/j.eswa.2025.128842","url":null,"abstract":"<div><div>Cross-scene hyperspectral image classification aims to enable the model to complete the classification of unlabeled target domain data by learning from labeled source domain data. Aiming at the problem that most current cross-scene hyperspectral image classification algorithms do not fully consider the cross-domain feature representation and category decision boundary optimization, a cross-domain Feature Extraction and Category Decision collaborative optimization (FECD) network is proposed. First, an adaptive feature discovery based on dynamic masks is designed. In this mechanism, the dynamically scaled masks are applied to the 3D representation of source and target domain data to generate an informative feature space and enhance the cross-scene discrimination potential of the model. Second, a dual-stream convolutional cross-domain feature extraction based on Mamba stream and ViT stream is constructed. Long sequence modeling and convolutional attention mechanisms are used to capture cross-domain spectral features between pixel, and self-attention mechanisms and multi-scale convolution are used to excavate cross-domain space patterns of pixel. Finally, a category decision based on the co-optimization of dual-stream classifiers is implemented. The spectral and spatial boundaries learned by the dual streams are fused to optimize the category decision. Therefore, the risk of false labeling is avoided while obtaining more accurate category boundaries. Compared with seven state-of-the-art algorithms on three widely used datasets, FECD obtains better categorization results on three categorization metrics: OA, AA, and Kappa.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128842"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Noise: A BERT-Enhanced framework for Intelligent product optimization via online review Analytics","authors":"Liangxing Shi ,&nbsp;Xiaoyuan Wang ,&nbsp;Yingdong He ,&nbsp;Zhen He","doi":"10.1016/j.eswa.2025.128812","DOIUrl":"10.1016/j.eswa.2025.128812","url":null,"abstract":"<div><div>Online reviews provide valuable insights into customer preference and product attributes (PAs), enabling companies to formulate effective product improvement strategies. In addition to PAs, however, reviews often contain noise, such as information about logistics and marketing strategies. While managing this noise is crucial to improving PA extraction and categorization accuracy and efficiency, existing studies have largely overlooked or handled noise inadequately. To address this gap, this study proposes a hybrid AI-driven framework for identifying and categorizing PAs while filtering out noise from online reviews. First, we use bidirectional encoder representations from transformers (BERT) to identify informative reviews (i.e., those containing at least one PA). Then, we integrate latent Dirichlet allocation and Word2Vec to extract irrelevant information, aiming to isolate noise and extract PAs from the reviews. Furthermore, we categorize PAs using importance-performance analysis (IPA) and IPA-GAP1. In this process, the importance of attributes is calculated by fitting the relationship between customer sentiment toward attributes and customer satisfaction using random forest, and customer sentiments are determined using BERT-based sentiment analysis. Additionally, we use importance–performance competitor analysis to assess attribute performance and importance across different products. Finally, we propose an Improvement Priority Score (IPS), which integrates attribute importance, performance, and competitive performance gap to provide companies with actionable insights for product optimization under limited resources. The proposed framework is validated through a case study using phone reviews from <span><span>JD.com</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"296 ","pages":"Article 128812"},"PeriodicalIF":7.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}