Engineering Applications of Artificial Intelligence最新文献

{"title":"Thermoeconomic optimization of climate-adaptive solar and wind multi-generation systems using artificial intelligence and thermal energy recovery","authors":"Ehsanolah Assareh ,&nbsp;Nima Izadyar ,&nbsp;Emad Tandis ,&nbsp;Mehdi Khiadani ,&nbsp;Amir shahavand ,&nbsp;Neha Agarwal ,&nbsp;Arian Gerami ,&nbsp;Ahmed Rezk ,&nbsp;Minkyu Kim ,&nbsp;Reza Kord ,&nbsp;Tahereh Pirhoushyaran ,&nbsp;Mehdi Hosseinzadeh ,&nbsp;Saleh Mobayen","doi":"10.1016/j.engappai.2025.112481","DOIUrl":"10.1016/j.engappai.2025.112481","url":null,"abstract":"<div><div>This study presents a hybrid multi-generation energy system designed to overcome solar intermittency while meeting the global demand for integrated delivery of electricity, water, cooling, and sustainable fuels in the transition to decarbonization. The engineering application integrates solar thermal and wind energy with a modified Brayton cycle, a Steam Rankine Cycle (SRC), and a Thermoelectric Generator (TEG) to simultaneously produce electricity, fresh water via Reverse Osmosis (RO), hydrogen and oxygen via Proton Exchange Membrane Electrolyzer (PEME), and cooling (via absorption chiller) within a unified optimization framework. The system was modeled using Engineering Equation Solver (EES) and optimized via Response Surface Methodology (RSM) based on 11 decision variables. To address the complexity of optimization, a second phase applied Artificial Intelligence (AI) techniques: Adaptive Boosting (AdaBoost) for predictive modelling and Particle Swarm Optimization (PSO) for global optimization. Under optimal conditions, the Response Surface Methodology yielded an exergy efficiency of 45.8 % with a cost rate of 576.76 United States Dollars per hour (USD/h), while AI reduced costs to 211.2 USD/h with a moderate efficiency trade-off. Simulation of the optimized configuration across eight diverse climates identified Quebec as most viable, generating 22,629.6 Megawatt-hours per year (MWh/year) of electricity and avoiding 4616.4 tons of Carbon Dioxide (CO₂) emissions annually. Integration of wind energy stabilizes solar variability, enhancing performance. AI contributes to optimizing complex interactions, nonlinear constraints, and multiple conflicting objectives. The methodology offers a scalable, generalizable framework for designing intelligent, climate-resilient infrastructures. Future research includes AI-enabled real-time control, experimental validation, and broader deployment strategies.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112481"},"PeriodicalIF":8.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222687","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}

{"title":"Physics-informed graph neural network for 3D spatiotemporal structural response modeling of flexible pavements","authors":"Fangyu Liu,&nbsp;Imad L. Al-Qadi","doi":"10.1016/j.engappai.2025.112391","DOIUrl":"10.1016/j.engappai.2025.112391","url":null,"abstract":"<div><div>Quantifying pavement damage is crucial for roadway agencies' maintenance planning. This study proposed a Physics-informed Graph Neural Network-based Pavement Simulator (PhyGPS) to predict three-dimensional (3D) asphalt concrete pavement responses, building upon an established data-driven Graph Neural Network-based Pavement Simulator (GPS) model. The key innovation lies in integrating knowledge graphs and mechanics equations to create a physics loss function, distinguishing it from its data-driven counterpart. The physics loss function comprises strain-displacement and stress loss components derived from 3D strain-displacement relations and stress equilibrium principles. A thorough 3D finite element (FE) pavement database supported the model development. The 3D FE pavement data was transformed into graph format where nodes and edges represent 3D FE pavement models’ nodes and node connections, respectively. Performance evaluation employed two case studies: “OneStep” for assessing short-term predictive capabilities and “Rollout” for examining long-term prediction accuracy under practical conditions. Results demonstrated that the physics-informed GPS model showed superior long-term predictive capability and robustness while maintaining excellent short-term accuracy compared to the data-driven model. Both models achieve rollout time under 8 s per FE simulation case, a dramatic improvement over the 12-h runtime of traditional 3D FE pavement models. The PhyGPS model successfully integrates physics principles, spatial relationships between structural components, temporal correlations in structural data, and complex material properties, offering an accurate, robust, and computationally efficient solution for predicting 3D pavement responses.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112391"},"PeriodicalIF":8.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223167","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}

{"title":"A novel data-efficient double deep Q-network framework for intelligent financial portfolio management","authors":"Mahshad Alidousti,&nbsp;Morteza Khakzar Bafruei,&nbsp;Amir Hosein Afshar Sedigh","doi":"10.1016/j.engappai.2025.112436","DOIUrl":"10.1016/j.engappai.2025.112436","url":null,"abstract":"<div><div>Navigating the complexities of dynamic and uncertain financial markets demands intelligent systems capable of learning profitable strategies amidst risk and volatility. While Deep Q-Networks (DQN) offer a foundation for such systems, they often suffer from overestimation bias, training instability, and poor generalization in noisy financial environments. To address these challenges, this work introduces Portfolio Double Deep Q-Network (PDQN), a novel architecture inspired by recent advancements in reinforcement learning. PDQN enhances portfolio management by integrating Double Q-Learning to reduce overestimation, alongside Leaky ReLU activation, Xavier initialization, Huber loss, and dropout regularization to improve learning stability and generalization. Unlike prior methods that rely on large datasets and heavy computational infrastructure, PDQN achieves competitive—and often superior—performance using substantially less training data and lightweight infrastructure, making it well-suited for real-world, resource-constrained financial applications. Distinct from conventional approaches, PDQN uses separate networks to adapt portfolio decisions across varying market conditions. Empirical results across multiple market years show that PDQN often outperforms baseline strategies, including classic DQN and Buy-and-Hold, across key metrics such as Sharpe ratio, Sterling ratio, and cumulative return. PDQN—like all data-driven models—exhibits room for improvement under highly irregular or extreme financial scenarios. These observations suggest promising directions for future refinement and increased robustness, without detracting from the model's practical effectiveness and competitive edge.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112436"},"PeriodicalIF":8.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223169","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}

{"title":"Model-free safe deep reinforcement learning for grid-to-vehicle management considering grid constraints and transformer thermal stress","authors":"Zhewei Zhang ,&nbsp;Rémy Rigo-Mariani ,&nbsp;Nouredine Hadjsaid ,&nbsp;Yan Xu","doi":"10.1016/j.engappai.2025.112529","DOIUrl":"10.1016/j.engappai.2025.112529","url":null,"abstract":"<div><div>The increasing penetration of Electric Vehicles (EVs) presents challenges to the distribution grid, due to more volatile power profiles and higher peak demand. One key research question is how to accommodate EVs with limited-capacity grid equipment, such as transformers and lines. However, uncertainties from the EV side and the complexity of grid equipment models challenge the performance of the control strategies implemented. Moreover, the thermal loading of the transformer is often neglected. In this work, we propose a fully model-free, safe Deep Reinforcement Learning (DRL)- based grid-to-vehicle management strategy to avoid electric and thermal overloading of the transformer and power grid constraint violation. The management strategy is based on Projection-based Constraint Policy Optimization (PCPO) and takes only the observable information from the grid and vehicles. The target is to maximize energy delivery to the EV fleet while considering safe constraints, such as transformer thermal loading, voltage magnitude limits, and line loading limits. We compared the proposed strategy with conventional DRL and other safe DRL methods and investigated its robustness against higher ambient temperatures. The results show that the proposed strategy can deliver 92 % energy and reduce violations of the grid and transformers, while the other benchmarks deliver less than 80 %. The robustness test demonstrates that the proposed strategy is effective in various temperature. Moreover, the proposed strategy can effectively reduce at most 90 % of the transformer aging incurred by the thermal stress, compared with the uncontrolled charging.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112529"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223172","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}

{"title":"Physics-informed dynamic Bayesian networks for time-dependent reliability prediction of subsea wellhead sealing system with multi-states","authors":"Shengnan Wu ,&nbsp;Han Gong ,&nbsp;Long Yu ,&nbsp;Aibo Zhang ,&nbsp;Laibin Zhang ,&nbsp;Yiliu Liu","doi":"10.1016/j.engappai.2025.112492","DOIUrl":"10.1016/j.engappai.2025.112492","url":null,"abstract":"<div><div>The Subsea Wellhead Sealing System (SWSS) is crucial for the safety of deepwater operating, yet its reliability assessment faces challenges from harsh environments and multi-factor interactions. This study developed a data-driven, physics-informed reliability assessment method combining Finite Element Analysis (FEA) and Dynamic Bayesian Networks (DBN). An FEA model is established based on metal sealing theory, and a data-driven reliability model is subsequently constructed through sampling analysis, with a numerical-to-state conversion method bridging FEA and DBN. The FEA-DBN approach offers two key advantages: eliminating expert scoring subjectivity through physics-based modeling and effectively capturing multi-factor interactions and time-dependent behaviors. Results show this method can precisely quantify the evolution of SWSS reliability throughout its service lifecycle, with the probability of failure increasing from 0.64 % to 3.38 % over a 30-year service life. Case studies demonstrate its effectiveness for deep-sea equipment assessment, particularly in operating environments where real-time monitoring proves challenging, thereby demonstrating significant engineering application value.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112492"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223174","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}

{"title":"Inverse design of particle shapes with target sphericity and packing fraction using variational autoencoders","authors":"Yutong Qian ,&nbsp;Shuixiang Li","doi":"10.1016/j.engappai.2025.112509","DOIUrl":"10.1016/j.engappai.2025.112509","url":null,"abstract":"<div><div>Sphericity and packing fraction are fundamental properties governing the behavior of granular materials in many engineering applications. Conventional methods for designing particles with these target properties usually suffer from limited accuracy, diversity, and interpretability due to complex relationships between particle shape and properties. To address this, we propose an inverse design framework based on deep learning. First, a rotation- and reflection-invariant variational autoencoder (VAE) parameterizes two-dimensional convex particle shapes into a low-dimensional latent space, enabling accurate reconstruction and capturing geometric interpretations such as sphericity and symmetry. Second, a conditional variational autoencoder (CVAE) facilitates inverse design by generating particle shapes corresponding to target sphericity or packing fraction, and also enables the coupling control of both properties. Trained on a dataset of over 1600 convex shapes, the framework demonstrates robustness and universality. The rotation- and reflection-invariant architecture consistently maps different orientations of the same shape to a unified representation, which enhances interpretability. The main contribution in artificial intelligence lies in developing invariant generative models that learn shape representations and enable property-driven shape generation. The engineering contribution is providing a precise and efficient tool for the inverse design of particle shapes with target properties, supporting the optimization of granular materials in engineering applications.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112509"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222443","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}

{"title":"Fault diagnosis via multi-sensor fusion with auxiliary contrastive learning and phased fine-tuning","authors":"Yulin Jin ,&nbsp;Xiaochuan Luo ,&nbsp;Xiangwei Kong ,&nbsp;Yulin Zhang","doi":"10.1016/j.engappai.2025.112427","DOIUrl":"10.1016/j.engappai.2025.112427","url":null,"abstract":"<div><div>Typically, deep learning-based fault diagnosis models fail to fully utilize the potential information in large amounts of normal state data and encounter difficulties when learning from limited fault samples. To address these challenges, this study proposes an auxiliary contrastive learning framework designed for multi-sensor data. The framework incorporates auxiliary classifiers after each sensor-specific branch to enhance feature representation, and enables model pretraining using only normal condition data. In addition, a phased fine-tuning strategy is developed, which combines full-model fine-tuning with lightweight adapter tuning to improve the adaptability of the fine-tuning process. A novel multi-sensor data augmentation technique is also introduced to enrich the contrastive learning tasks by generating structurally diverse negative samples. By enabling the effective utilization of normal condition data in model training, the proposed framework offers a new perspective for fault diagnosis applications. Experimental results on three benchmark datasets demonstrate that the proposed method significantly improves the generalization capability of the pre-trained model. Furthermore, the phased fine-tuning strategy exhibits high adaptability to the target tasks. Compared to other data fusion methods, the proposed auxiliary contrastive learning framework achieves notable performance advantages.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112427"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222379","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}

{"title":"Edge computing and server-based high-precision flood level classification system","authors":"Ankang Lu ,&nbsp;Runlong Cao ,&nbsp;Yuanbin Wang ,&nbsp;Wenjun Hu ,&nbsp;Yuncan Gao ,&nbsp;Zhifeng Hu ,&nbsp;Ying Zang","doi":"10.1016/j.engappai.2025.112442","DOIUrl":"10.1016/j.engappai.2025.112442","url":null,"abstract":"<div><div>Urban flooding and the resulting road water accumulation have become a significant threat to public transportation safety and the stability of municipal infrastructure. Traditional monitoring networks based on physical water level sensors suffer from low deployment density, high maintenance costs, and lagging response times. To address these shortcomings of traditional water accumulation monitoring systems, this study proposes an edge-computing intelligent monitoring system based on collaborative inference between the edge end (You Only Look Once version 5, YOLOv5) and the server end (Transform Vision Detection, TrVDet). A dual-modal perception architecture of “edge-end triggering and server-end precise analysis” has been constructed. At the edge end, the YOLOv5 model is deployed on embedded devices to achieve efficient preliminary screening of water accumulation, reducing dependence on the central server, lowering latency, and enhancing real-time response capabilities. On the server end, multi-object segmentation is performed on the detected water accumulation images, including roads, cars, motorcycles, and bicycles. Finally, a series of logical judgments is applied to determine the water accumulation level based on reference objects within the water. Since there is no publicly available dataset for target object recognition in flooded areas, we employed professional annotators to perform pixel-level labeling on the collected and organized flood data and constructed a multi-class target flood dataset (City Flood Segmentation, CityFloodSeg). Given the scarcity of moderate and severe water accumulation samples, we optimized the instance segmentation model TrVDet under the (A Visual Representation for Neon Genesis, EVA-02) framework and applied five data augmentation methods, including Mosaic and Flip, to expand the diversity of the dataset. Moreover, based on domain expert standards, we designed a logical judgment rule algorithm for model inference of water accumulation levels to classify the levels of water accumulation. Experimental results show that the server-end processing delay is stable within 0.4 s, capable of accurately judging different water accumulation risk levels. This provides centimeter-level real-time situational awareness for urban flood control decision-making and promotes the development of intelligent municipal infrastructure towards higher reliability and universality.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112442"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222381","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}

{"title":"Uncovering the geometry-dependent optical asymmetry of gold nanorods helical assemblies using artificial neural networks","authors":"Yang Liu ,&nbsp;Yongguang Chen ,&nbsp;Xiyang Wei ,&nbsp;Jianhua Shang ,&nbsp;Lina Zhao","doi":"10.1016/j.engappai.2025.112513","DOIUrl":"10.1016/j.engappai.2025.112513","url":null,"abstract":"<div><div>The optical asymmetry of gold nanorods (Au-NRs) helical assemblies is well-documented with a wide range of applications. Nevertheless, the geometry-dependent optical asymmetry within these assemblies has not been adequately explored and quantified. The present study proposes a novel approach to predict the optical asymmetry of Au-NRs helical assemblies based on geometric characteristics using artificial neural networks (ANN). The performance of the ANN termed <span><math><mrow><mn>3</mn><msub><mi>N</mi><mrow><mi>H</mi><mi>L</mi></mrow></msub><mn>50</mn><msub><mi>N</mi><mi>N</mi></msub></mrow></math></span> was significantly enhanced through the optimization of the hidden layer and node, resulting in an R² of the outcomes exceeding 0.998 and a reduction in computational time exceeding 99.99 %. In instances where the specific geometric characteristics are needed to attain a desired optical asymmetry, a retrieval of geometric characteristics of Au-NRs helical assemblies was additionally investigated using a traversing mechanism featured particle swarm optimization (PSO) algorithm. The results of the retrieval were obtained within 6 s and demonstrate a high degree of accuracy and reliability. The combination of the <span><math><mrow><mn>3</mn><msub><mi>N</mi><mrow><mi>H</mi><mi>L</mi></mrow></msub><mn>50</mn><msub><mi>N</mi><mi>N</mi></msub></mrow></math></span> and the PSO algorithm is capable of accurately predicting the optical asymmetry of Au-NRs helical assemblies and the retrieval of the geometry characteristics, thereby enabling the quantitative understanding of their overall geometry-dependent optical asymmetry.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112513"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222384","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}

{"title":"Selecting after sales provider of complex product based on game and matching framework","authors":"Xin Huang ,&nbsp;Xiaoyan Qi ,&nbsp;Xiaojuan Xu","doi":"10.1016/j.engappai.2025.112524","DOIUrl":"10.1016/j.engappai.2025.112524","url":null,"abstract":"<div><div>As a strategic enabler of high-end manufacturing, the high-quality evolution of complex equipment is indispensable for any nation aspiring to industrial leadership. After sales service (AS) long relegated to a support function, which has emerged as a decisive determinant of product life-cycle value and, consequently, of this transformative journey. This study therefore investigates the technological innovation of AS for complex products through a Stackelberg game that captures the collaborative dynamics between an original equipment manufacturer (OEM) and an after-sales service provider (ASP). We derive the necessary and sufficient conditions under which an ASP finds participation economically viable, then embed these conditions into a multi-criteria matching framework that links ASP capabilities with spare-part requirements. Leveraging an entropy weighted DEMATEL (Decision-making Trial and Evaluation Laboratory) hybrid and we first quantify the causal salience of matching attributes and build a parsimonious evaluation index system. Next, by explicitly encoding bilateral attribute preferences, we formulate a two-sided matching model that identifies the Pareto-optimal ASP portfolio for any given product architecture. Finally, backward induction over the integrated game-matching structure yields a prescriptive tool that not only screens ASPs but also prescribes contractual levers to sustain long-term co-innovation. The proposed framework thus unifies strategic participation incentives with operational compatibility, offering OEMs a rigorous, implementable roadmap for selecting and governing after-sales partners in the era of servitized, high-stakes manufacturing.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"162 ","pages":"Article 112524"},"PeriodicalIF":8.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222380","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}