Journal of Machine and Computing最新文献

Video Face Tracking for IoT Big Data using Improved Swin Transformer based CSA Model 使用基于 CSA 模型的改进型斯温变换器进行物联网大数据视频人脸跟踪

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404029

Anbumani K, Cuddapah Anitha, Achuta Rao S V, Praveen Kumar K, Meganathan Ramasamy, M. R.

{"title":"Video Face Tracking for IoT Big Data using Improved Swin Transformer based CSA Model","authors":"Anbumani K, Cuddapah Anitha, Achuta Rao S V, Praveen Kumar K, Meganathan Ramasamy, M. R.","doi":"10.53759/7669/jmc202404029","DOIUrl":"https://doi.org/10.53759/7669/jmc202404029","url":null,"abstract":"Even though Convolutional Neural Networks (CNNs) have greatly improved face-related algorithms, it is still difficult to keep both accuracy and efficiency in real-world applications. The most cutting-edge approaches use deeper networks to improve performance, but the increased computing complexity and number of parameters make them impractical for usage in mobile applications. To tackle these issues, this article presents a model for object detection that combines Deeplabv3+ with Swin transformer, which incorporates GLTB and Swin-Conv-Dspp (SCD). To start with, in order to lessen the impact of the hole phenomena and the loss of fine-grained data, we employ the SCD component, which is capable of efficiently extracting feature information from objects at various sizes. Secondly, in order to properly address the issue of challenging object recognition due to occlusion, the study builds a GLTB with a spatial pyramid pooling shuffle module. This module allows for the extraction of important detail information from the few noticeable pixels of the blocked objects. Crocodile search algorithm (CSA) enhances classification accuracy by properly selecting the model's fine-tuning. On a benchmark dataset known as WFLW, the study experimentally validates the suggested model. Compared to other light models, the experimental findings show that it delivers higher performance with significantly fewer parameters and reduced computing complexity.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convolutional Deep Belief Network Based Expert System for Automated Fault Diagnosis in Hydro Electrical Power Systems 基于卷积深度信念网络的水电系统故障自动诊断专家系统

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404031

Husam Alowaidi, Prashant G C, Gopalakrishnan T, Sundar Raja M, Padmaja S M, Anjali Devi S

{"title":"Convolutional Deep Belief Network Based Expert System for Automated Fault Diagnosis in Hydro Electrical Power Systems","authors":"Husam Alowaidi, Prashant G C, Gopalakrishnan T, Sundar Raja M, Padmaja S M, Anjali Devi S","doi":"10.53759/7669/jmc202404031","DOIUrl":"https://doi.org/10.53759/7669/jmc202404031","url":null,"abstract":"The paper developed an approach for fault diagnosis in Hydro-Electrical Power Systems (HEPS). Using a Renewable Energy System (RES), HEPS has performed a significant part in contributing to addressing the evolving energy demands of the present. Several electro-mechanical elements that collectively comprise the Hydro-Electric (HE) system are susceptible to corrosion from routine usage and unplanned occurrences. Administration and servicing systems that are successful in implementing and achieving these goals are those that regularly track and predict failures. Detect models applied in the past included those that were primarily reactive or reliant on human involvement to identify and analyse abnormalities. The significant multiple variables intricacies that impact successful fault detection are disregarded by these frameworks. The research presented here proposes a Convolutional Deep Belief Network (CDBN) driven Deep Learning (DL) model for successful fault and failure detection in such power systems that address these problems. Applying sample data collected from two Chinese power plants, the proposed framework has been assessed compared to other practical DL algorithms. Different metrics have been employed to determine the effectiveness of the simulations, namely Accuracy, Precision, Recall, and F1-score. These outcomes indicated that the CDBN is capable of predicting unexpected failures. Graphic representations demonstrating control used to measure turbine blade load, vibration level, and generator heat for assessing the replicas.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"45 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System 基于集合机器学习辅助作物和肥料推荐系统的可持续粮食开发

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404030

Komala Devi K, Josephine Prem Kumar

{"title":"Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System","authors":"Komala Devi K, Josephine Prem Kumar","doi":"10.53759/7669/jmc202404030","DOIUrl":"https://doi.org/10.53759/7669/jmc202404030","url":null,"abstract":"Agriculture is the most vital sector for the global food supply, and it also provides raw materials for other types of industries. A crop recommendation system is essential for farmers who want to get the most out of their crop-choosing decisions. Over the last several decades, the world's ability to produce food has grown substantially owing to the extensive usage of fertilizers. Therefore, there has to be a more eco-friendly and effective way to utilize fertilizers that include nitrogen (N), phosphorous (P), and potassium (K) to ensure food security. For the reason, this study proposes an ensemble machine learning–assisted crop and fertilizer recommendation system (EML–CFRS) to maximize agricultural output while ensuring the correct use of mineral resources. The research used a dataset obtained from the Kaggle repository like that people can assess several distinct ML algorithms. The databases include data on three climate variables—temperature, rainfall, and humidity—and information on NPK and soil pH. The yields agricultural crops were used to train these models, including Decision Tree, KNN, XGBoost, Support Vector Machine, and Random Forest. Depending on the current weather and soil conditions, the trained model may then recommend the optimal fertiliser for a certain crop. Predicting the ideal kind and quantity of fertilizer for different crops was accomplished with a 96.5% accuracy rate by our suggested strategy.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots 基于 DNN 的相对定位技术，用于移动式无人蜂群机器人的实时定位

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404048

In-Young Hyun, Seung-Mi Yun, Eui-Rim Jeong

{"title":"DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots","authors":"In-Young Hyun, Seung-Mi Yun, Eui-Rim Jeong","doi":"10.53759/7669/jmc202404048","DOIUrl":"https://doi.org/10.53759/7669/jmc202404048","url":null,"abstract":"The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"22 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration 水下软机器人技术的进步与挑战：材料、控制和集成

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404049

Lekha T R, Saravanakumar K, Akshaya V S, Aravindhan K

{"title":"Advancements and Challenges in Underwater Soft Robotics: Materials, Control and Integration","authors":"Lekha T R, Saravanakumar K, Akshaya V S, Aravindhan K","doi":"10.53759/7669/jmc202404049","DOIUrl":"https://doi.org/10.53759/7669/jmc202404049","url":null,"abstract":"This article focuses on the progress of underwater robots and the importance of software architectures in building robust and autonomous systems. The researchers underscore the challenges linked to implementation and stress the need for comprehensive validation of both reliability and efficacy. Their argument is on the extensive implementation of a globally applicable architectural framework that complies with established standards and guarantees interoperability within the field of robotics. The research also covers advancements in underwater soft robotics, which include the development of models, materials, sensors, control systems, power storage, and actuation techniques. This article explores the challenges and potential applications of underwater soft robotics, highlighting the need of collaboration across many fields and advancements in mechanical design and control methods. In the last section of the paper, the control approach and algorithms used to underwater exploration robots are reviewed. Particular attention is given to the application of Proportional Integral Derivative (PID) control and the incorporation of Backpropagation Neural Network (BPNN) for PID parameter determination.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"27 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation 基于乌鸦搜索和粒子群算法优化的混合 CH 选择方法，用于扩展无线传感器网络的运行范围

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404028

V. P, Venkatesh K

{"title":"Hybrid Crow Search and Particle Swarm Algorithmic optimization based CH Selection method to extend Wireless Sensor Network operation","authors":"V. P, Venkatesh K","doi":"10.53759/7669/jmc202404028","DOIUrl":"https://doi.org/10.53759/7669/jmc202404028","url":null,"abstract":"In ad hoc wireless sensor networks, the mobile nodes are deployed to gather data from source and transferring them to base station for reactive decision making. This process of data forwarding attributed by the sensor nodes incurs huge loss of energy which has the possibility of minimizing the network lifetime. In this context, cluster-based topology is determined to be optimal for reducing energy loss of nodes in WSNs. The selection of CH using hybrid metaheuristic algorithms is identified to be significant to mitigate the quick exhaustion of energy in entire network. This paper explores the concept of hybrid Crow Search and Particle Swarm Optimization Algorithm-based CH Selection (HCSPSO-CHS) mechanism is proposed with the merits of Flower Pollination Algorithm (FPA) and integrated Crow Search Algorithm (CSA) for efficient CH selection. It further adopted an improved PSO for achieving sink node mobility to improve delivery of packets to sink nodes. This HCSPSO-CHS approach assessed the influential factors like residual energy, inter and intra-cluster distances, network proximity and network grade during efficient CH selection. It facilitated better search process and converged towards the best global solution, such that frequent CH selection is avoided to maximum level. The outcomes of the suggested simulation HCSPSO-CHS confirm better performance depending on the maximum number of active nodes by 23.18%, prevent death of sensor nodes by 23.41% with augmented network lifetime of 33.58% independent of the number of nodes and rounds of data transmission.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT Innovations as a Strategy for Minimizing Construction Expenses 将物联网创新作为最大限度降低建筑成本的策略

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404033

Deepak Tulsiram Patil, Amiya Bhaumik

{"title":"IoT Innovations as a Strategy for Minimizing Construction Expenses","authors":"Deepak Tulsiram Patil, Amiya Bhaumik","doi":"10.53759/7669/jmc202404033","DOIUrl":"https://doi.org/10.53759/7669/jmc202404033","url":null,"abstract":"The revolutionary impact of Internet of Things (IoT) improvements on the construction enterprise is carefully tested on this extensive research, with a focus on cost-cutting strategies. Examining a wide range of IoT programs from the predictive repair of equipment to the actual-time monitoring of building materials the study highlights how those packages can appreciably lessen operating charges. This inquiry identifies key areas wherein IoT technology are expected to sell cost-saving measures by utilizing a thorough evaluation of relevant literature along with a robust method that includes case research and empirical records evaluation. Using 12 records points and a aggregate of documentation evaluation and interviews, this examine assesses the impact of IoT technology on constructing charges. It offers insights into how IoT adoption in creation might be financially viable with the aid of highlighting the way it influences fee dynamics and undertaking control. The observe concludes with the aid of dropping mild at the broader implications of IoT adoption inside the construction enterprise and emphasizing how important it is to promoting a sustainable environment and strengthening the competitive fringe of companies on this zone. The present investigation not only emphasizes the economic blessings of implementing IoT, but additionally indicates its capability to convert conventional building methods by way of facilitating the improvement of greater reasonably priced, efficient, and environmentally friendly venture execution strategies.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"10 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection 通过基于稀疏自动编码器的异常检测加强水处理厂的预测性维护

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404027

Hussein Z, Balaji V, Ramesh R, Arokia Jesu Prabhu L, Venubabu Rachapudi, E. V

{"title":"Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection","authors":"Hussein Z, Balaji V, Ramesh R, Arokia Jesu Prabhu L, Venubabu Rachapudi, E. V","doi":"10.53759/7669/jmc202404027","DOIUrl":"https://doi.org/10.53759/7669/jmc202404027","url":null,"abstract":"The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"37 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis 针对遥感分析中滑坡易感性绘图缺失数据预测的权重优化

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404043

Kanchana S, Jayakarthik R, Dineshbabu V, Saranya M, Srikanth Mylapalli, Rajesh Kumar T

{"title":"Weight Optimization for missing data prediction of Landslide Susceptibility Mapping in Remote sensing Analysis","authors":"Kanchana S, Jayakarthik R, Dineshbabu V, Saranya M, Srikanth Mylapalli, Rajesh Kumar T","doi":"10.53759/7669/jmc202404043","DOIUrl":"https://doi.org/10.53759/7669/jmc202404043","url":null,"abstract":"To keep track of changes to the Earth's surface, extensive time series of data from remote sensing using image processing is required. This research is motivated by the effectiveness of computational modelling techniques; however, the problem of missing data is multifaceted. When data at numerous a-periodic timestamps are absent during multi-temporal analysis, the issue becomes increasingly problematic. To make remote sensing time series analysis easier, weight optimised machine learning is used in this study to rebuild lost data. Keeping the causality restriction in mind, this method makes use of data from previous and subsequent timestamps. The architecture is based on an ensemble of numerous forecasting modules, built on the observed data in the time-series order. Dummy data is used to connect the forecasting modules, which were previously linked by the earlier half of the sequence. After that, iterative improvements are made to the dummy data to make it better fit the next segment of the sequence. On the basis of Landsat-7 TM-5 satellite imagery, the work has been proven to be accurate in forecasting missing images in normalised difference vegetation index time series. In a performance evaluation, the proposed forecasting model was shown to be effective.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure 利用深度强化学习优化建筑能源管理，打造智能和可持续的基础设施

Journal of Machine and Computing Pub Date : 2024-04-05 DOI: 10.53759/7669/jmc202404036

Nabeel S. Alsharafa, Suguna R, Raguru Jaya Krishna, Vijaya Krishna Sonthi, Padmaja S M, Mariaraja P

{"title":"Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure","authors":"Nabeel S. Alsharafa, Suguna R, Raguru Jaya Krishna, Vijaya Krishna Sonthi, Padmaja S M, Mariaraja P","doi":"10.53759/7669/jmc202404036","DOIUrl":"https://doi.org/10.53759/7669/jmc202404036","url":null,"abstract":"This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.","PeriodicalId":516221,"journal":{"name":"Journal of Machine and Computing","volume":"4 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0