Engineering Applications of Artificial Intelligence最新文献

{"title":"A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem","authors":"Jiawei Wu,&nbsp;Yong Liu","doi":"10.1016/j.engappai.2024.109688","DOIUrl":"10.1016/j.engappai.2024.109688","url":null,"abstract":"<div><div>This paper extends a novel model for modern flexible manufacturing systems: the multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem (MDPR-HFSP). The model considers partial-re-entrant processing, dynamic disturbance events, green manufacturing demand, and machine workload. Despite advancements in applying deep reinforcement learning to dynamic workshop scheduling, current methods face challenges in training scheduling policies for partial-re-entrant processing constraints and multiple manufacturing objectives. To solve the MDPR-HFSP, we propose a modified multi-agent proximal policy optimization (MMAPPO) algorithm, which employs a routing agent (RA) for machine assignment and a sequencing agent (SA) for job selection. Four machine assignment rules and four job selection rules are integrated to choose optimum actions for RA and SA at rescheduling points. In addition, reward signals are created by combining objective weight vectors with reward vectors, and training parameters under each weight vector are saved to flexibly optimize three objectives. Furthermore, we design an adaptive trust region clipping method to improve the constraint of the proximal policy optimization algorithm on the differences between new and old policies by introducing the Wasserstein distance. Moreover, we conduct comprehensive numerical experiments to compare the proposed MMAPPO algorithm with nine composite scheduling rules and the basic multi-agent proximal policy optimization algorithm. The results demonstrate that the proposed MMAPPO algorithm is more effective in solving the MDPR-HFSP and achieves superior convergence and diversity in solutions. Finally, a semiconductor wafer manufacturing case is resolved by the MMAPPO, and the scheduling solution meets the responsive requirement.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109688"},"PeriodicalIF":7.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723295","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":"LCRTR-Net: A low-cost real-time recognition network for rail corrugation in railway transportation","authors":"Xueyang Tang ,&nbsp;Xiaopei Cai ,&nbsp;Yuqi Wang ,&nbsp;Yue Hou","doi":"10.1016/j.engappai.2024.109708","DOIUrl":"10.1016/j.engappai.2024.109708","url":null,"abstract":"<div><div>Rail corrugation has a significant impact on the safety of high-speed railway operations, making its identification particularly important. Traditional manual inspection methods are infeasible for large-scale identification within limited time frames, while existing methods based on machine vision or axle box acceleration face challenges such as high costs, complex equipment installation and maintenance, as well as difficulties in achieving real-time performance. To address these challenges, this study proposes an innovative low-cost real-time recognition network (LCRTR-Net), which utilizes accelerometers installed on the underside of the train body and combines wavelet packet decomposition with dilated causal convolution in a residual neural network. Specifically, the approach first extracts the latent features of train body acceleration caused by rail corrugation through wavelet packet decomposition and reconstruction. Next, dilated causal convolution is employed to capture the temporal causal relationships and long-term dependencies of these latent features. Finally, the integration of residual connections further enhances the feature extraction performance and computational efficiency of LCRTR-Net. Experimental results demonstrate that LCRTR-Net exhibits significant generalization ability and real-time performance, achieving an average recognition accuracy exceeding 97.0%, with a recognition time of only 0.17 ms per rail corrugation sample, significantly outperforming existing rail corrugation recognition methods. This indicates that LCRTR-Net has broad application potential in practical railway operations. Future research directions will focus on unsupervised or few-shot learning algorithms and multi-sensor integration to further improve recognition accuracy and real-time performance, promoting the practical application of this technology.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109708"},"PeriodicalIF":7.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700728","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":"Dual-branch feature Reinforcement Transformer for preoperative parathyroid gland segmentation","authors":"Lei Lyu ,&nbsp;Chen Pang ,&nbsp;Qinghan Yang ,&nbsp;Kailin Liu ,&nbsp;Chong Geng","doi":"10.1016/j.engappai.2024.109672","DOIUrl":"10.1016/j.engappai.2024.109672","url":null,"abstract":"<div><div>The parathyroid glands are easily injured or accidentally removed during thyroid surgery, causing temporary or even permanent hypocalcemia. Thus, accurate preoperative identification and localization of the parathyroid glands by ultrasound is crucial in protecting the parathyroid glands and preventing parathyroid injury during thyroid surgery. However, there are only a few methods used for highlighting the parathyroid gland in ultrasound images before thyroid surgery. In this study, we propose a Dual-branch feature Reinforcement Transformer Network (DRT-Net) for preoperative parathyroid gland segmentation. DRT-Net incorporates a dual-branch structure, consisting of a devised convolution network (CNN) backbone called Feature Reinforcement subnet (FR-subnet) and a Transformer branch capturing detailed features and context information from the confused ultrasound image. Furthermore, we design a Margin Tracking Attention (MTA) that optimizes the ability of FR-subnet to process margin information by tracking margin pixels of feature map. Finally, we employ a Cross-channel Feature Reinforcement Module (CFRM) to fuse the extracted detailed features from the CNN branch with the global context information from the Transformer branch. We trained and evaluated the DRT-Net on the self-built parathyroid gland segmentation dataset and an open-access Kvasir-SEG dataset. Extensive experiments have been carried out to validate the efficiency of our method.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109672"},"PeriodicalIF":7.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700717","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":"Deep reinforcement learning optimizer based novel Caputo fractional order sliding mode data driven controller","authors":"Amir Veisi ,&nbsp;Hadi Delavari","doi":"10.1016/j.engappai.2024.109725","DOIUrl":"10.1016/j.engappai.2024.109725","url":null,"abstract":"<div><div>The design of controllers in engineering applications typically requires a model that accurately captures the dynamics of the real system. However, finding a precise model for controller design can be challenging in real engineering applications. Consequently, data-driven methods have gained widespread use in engineering systems. This paper presents a novel robust data-driven fractional-order controller optimized through deep reinforcement learning. Additionally, a new robust fractional-order observer has been introduced to improve both the robustness and speed of the system. To establish the stability of the proposed control system, a new Lyapunov stability theorem based on the Caputo fractional-order definition is provided. The proposed controller offers significant advantages, including enhanced robustness against external disturbances, increased resilience to parameter uncertainties and unmodeled nonlinear dynamics, improved accuracy, greater speed, and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning including enhanced robustness against external disturbances, uncertainties of parameters, and unmodeled nonlinear dynamics; improved accuracy; greater speed; and guaranteed optimal control coefficients. Furthermore, assured adaptability is demonstrated due to the optimization provided by deep reinforcement learning. The performance of the proposed method has been compared with that of conventional integer-order sliding mode control, highlighting the superiority of this approach. The proposed method has been evaluated under normal conditions, external disturbances, and system uncertainties. Notably, performance improvements of 15%, 30%, and 68% have been achieved under normal conditions, external disturbances, and internal uncertainties, respectively, compared to the conventional integer-order sliding mode controller.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109725"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700719","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":"Assessing the factors of blockchain technology-enabled hospitals using an integrated interval-valued q-rung orthopair fuzzy decision-making model","authors":"Rashmi Pathak ,&nbsp;Badal Soni ,&nbsp;Naresh Babu Muppalaneni ,&nbsp;Muhammet Deveci","doi":"10.1016/j.engappai.2024.109641","DOIUrl":"10.1016/j.engappai.2024.109641","url":null,"abstract":"<div><div>In the current era, blockchain technology (BT) has emerged as a novel technique to maintain the operations of healthcare management systems. Assessment of blockchain technology (BT)-enabled hospitals can be considered as a multi-criteria decision making (MCDM) problem because of the existence of several criteria. The aim of this study is to develop a hybrid MCDM method for evaluating the factors of multi-criteria BT-enabled hospital selection problem under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). For this purpose, a weighted aggregated sum product assessment (WASPAS) model is presented with the combination of IVq-ROF interaction aggregation operators, the standard deviation (SD)-based model and pivot pairwise relative criteria importance assessment (PIPRECIA) tool called IV-q-ROF-SD-PIPRECIA-WASPAS model within the context of IVq-ROFSs. For this purpose, some new IVq-ROF interaction aggregation operators are developed with their desirable characteristics. Next, the standard deviation-based model and PIPRECIA model on IVq-ROFSs are proposed to obtain the final weight of criteria, whereas the rank-based formula is presented to determine the decision experts’ weights with IVq-ROF information. The presented IV-q-ROF-SD-PIPRECIA-WASPAS model is applied on a case study of BT-enabled hospitals assessment, which confirms its applicability and usefulness. Sensitivity analysis and comparative discussion have been performed to reveal the consistency, robustness and efficiency of the presented model. The BT-enabled hospital-II with highest UD (0.4453) has emerged as the best choice among a set of BT-enabled hospitals. The factor \"flexibilty\" with highest weight (0.0898) value followed that the scalability (0.0809), transaction speed and accountability with same weight (0.0779) value, and network availability with weight (0.0771) for BT-enabled hospitals assessment. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple indicators and input uncertainties.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109641"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720895","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":"WA-Net: Wavelet Integrated Attention Network for Silk and Bamboo character recognition","authors":"Shengnan Li,&nbsp;Chi Zhou,&nbsp;Kaili Wang","doi":"10.1016/j.engappai.2024.109674","DOIUrl":"10.1016/j.engappai.2024.109674","url":null,"abstract":"<div><div>Chu Bamboo and Silk ancient Chinese character (CBSC) was originated in the Chu state over 2000 years ago, representing an intermediate script between oracle bone script and seal script. Existing text images have degraded and suffered damage due to their ancient historical origins and insufficient preservation. Due to distinct structural and stroke texture characteristics, significant differences exist between CBSC and contemporary characters, posing challenges for intelligent recognition. Targeting these aforementioned characteristics, we propose a method called Wavelet Integrated Attention Network (WA-Net). This method integrates discrete wavelet transform and attention mechanisms to extract more discriminative features from severe noise interference and degraded text images. Additionally, a dataset named Chu Bamboo and Silk 730 (Chu730) for CBSC recognition has been created due to the lack of publicly available datasets. WA-Net introduces the discrete wavelet attention among layer (L-DWT) to broaden the feature learning space of convolutional neural networks into the wavelet domain, capturing latent information across various frequencies. Subsequently, a wavelet convolution (C-DWT) module is proposed to mitigate the partial information loss of conventional convolution operations. In the W-bneck module, the SE (Squeeze-and-Excitation) attention module and average pooling downsampling are introduced to enhance the extraction of valuable feature maps. Extensive experiments were conducted, including a baseline method that achieved top-1 recognition accuracy of 87.42%. The proposed method achieved an accuracy of 89.27%, and other top-n results also significantly surpassed the baseline accuracy. Other experiment results demonstrate the superiority of the proposed modules and theirvaluable applications in ancient text intelligent recognition and cultural heritage digital preservation. Furthermore, this approach holds significant promise in facilitating the study of other handwritten or ancient characters recognition. Dataset and code are available at: <span><span>https://github.com/Nancy45-ui/WA-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109674"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700727","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":"Exploring structural components in autoencoder-based data clustering","authors":"Sujoy Chatterjee ,&nbsp;Suvra Jyoti Choudhury","doi":"10.1016/j.engappai.2024.109562","DOIUrl":"10.1016/j.engappai.2024.109562","url":null,"abstract":"<div><div>Clustering is a fundamental machine-learning task that has received extensive popularity in the literature. The foundational tenet of traditional clustering approaches is that data are learned to be vectorized features through various representational learning techniques. The conventional clustering methods can no longer manage the high-dimensional data as the data gets more intricate. Numerous representational learning strategies using deep architectures have been presented over the years, particularly deep unsupervised learning due to its superiority over conventional approaches. In most existing research, especially in the autoencoder-based approaches, only the distance information of pair-of-points in the original data space is retained in the latent space. However, combining this with additional preserving factors like the variance and independent component in the original data and latent space, respectively, is important. In addition, the model’s stability under noisy conditions is crucial. This paper provides a unique method for clustering data that combines autoencoder (AE), principal component analysis (PCA), and independent component analysis (ICA) to capture a relevant latent space representation. A further aid in lowering the dimensionality to improve clustering effectiveness is employing two-dimensional reduction algorithms, i.e., PCA and <span><math><mrow><mi>t</mi><mo>−</mo></mrow></math></span>distributed stochastic neighbor embedding (<span><math><mrow><mi>t</mi><mo>−</mo></mrow></math></span>SNE). The proposed technique produces more precise and reliable clustering by utilizing the advantages of both approaches. To compare the efficiency of the proposed methods to conventional clustering methods and stand-alone autoencoders, we conduct comprehensive experiments on 13 real-life datasets. The outcomes demonstrate the approach’s intriguing potential for addressing complicated clustering problems, and importantly, effectiveness is demonstrated even under noisy conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109562"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700796","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":"Motion planning for 7-degree-of-freedom bionic arm: Deep deterministic policy gradient algorithm based on imitation of human action","authors":"Baojiang Li ,&nbsp;Shengjie Qiu ,&nbsp;Haiyan Ye ,&nbsp;Yuting Guo ,&nbsp;Haiyan Wang ,&nbsp;Jibo Bai","doi":"10.1016/j.engappai.2024.109673","DOIUrl":"10.1016/j.engappai.2024.109673","url":null,"abstract":"<div><div>Smart bionic arms have played a great role in returning amputees to society. However, most of the current bionic arms are radial configuration type with few degrees of freedom and humeral form configuration type, which are only applicable to patients with proximal amputation. Patients with shoulder amputation urgently need a 7-degree-of-freedom bionic arm that can fully mimic human upper limb movements. Meanwhile, bionic arms often require specific programming to be implemented for the subject to initially meet the control requirements, which makes it difficult to match the motion of the bionic arm with the wearer's movement habits and reduces the adaptability and reliability of human-computer interaction. To address this problem, this paper proposes a motion imitation based on human upper limb joint point guidance and a motion planning algorithm based on reinforcement learning method to achieve the purpose of making the shoulder disconnected bionic arm accomplish humanoid motion by learning the dynamic motion imitation of the human upper limb. The algorithm analyzes and learns 3D poses of human arm movement features from unlabeled videos, then designs a reward function based on human motion patterns, and uses a reinforcement learning algorithm based on deep deterministic policy gradient (DDPG) to train the humanoid motion of the bionic arm. We evaluated the effectiveness of shoulder detached bionic arms through several tasks in a simulation environment, and the results showed that this method has good performance in planning the humanoid motion of bionic arms and can be widely applied in bionic machine control.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109673"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700720","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 return-to-home unmanned aerial vehicle navigation solution in global positioning system denied environments via bidirectional long short-term memory reverse flightpath prediction","authors":"Mustafa Alkhatib ,&nbsp;Mohammad Nayfeh ,&nbsp;Khair Al Shamaileh ,&nbsp;Naima Kaabouch ,&nbsp;Vijay Devabhaktuni","doi":"10.1016/j.engappai.2024.109729","DOIUrl":"10.1016/j.engappai.2024.109729","url":null,"abstract":"<div><div>In this paper, bidirectional long short-term memory (B-LSTM) deep learning modeling is proposed as an approach to facilitate autonomous return-to-home (RTH) aerial navigation in environments with compromised global positioning system (GPS) reception. Logged samples of ten radiometric features are extracted from onboard sensors (i.e., accelerometer, barometer, GPS, gyroscope, magnetometer) in two outdoor experimental scenarios of different altitudes and velocities. These samples are used for training and validating B-LSTM models with single and parallel architectures. The former architecture consists of a single B-LSTM model that processes all input features across the <em>x</em>-, <em>y</em>-, and <em>z</em>-axes to predict a three-dimensional local position, whereas the latter comprises three parallel B-LSTM models, each for processing only the features of a specific dimension (i.e., <em>x</em>, <em>y</em>, or <em>z</em>) and predicting local position in the respective axis. Evaluations demonstrate the validity of the proposed approach, with a 4-m average mean square error (MSE). This is achieved without imposing resource-consuming computational overhead, modifications to existing hardware, or changes to physical infrastructure and communication protocols. Due to using existing onboard sensors and accommodating varied scenarios, the proposed approach finds applications in autonomous navigation, including unmanned aerial vehicles (UAVs) and ground vehicles.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109729"},"PeriodicalIF":7.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700772","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 depthwise convolutional neural network model based on active contour for multi-defect wafer map pattern classification","authors":"Jeonghoon Choi,&nbsp;Dongjun Suh","doi":"10.1016/j.engappai.2024.109707","DOIUrl":"10.1016/j.engappai.2024.109707","url":null,"abstract":"<div><div>As semiconductor manufacturing processes continue to witness increased integration density and design complexity, semiconductor wafers are experiencing a growing diversity and complexity of defects. While previous research in wafer map classification using deep learning has made significant advancements in dealing with single defect patterns, the classification of mixed-type defects has received less attention due to their considerably higher difficulty level compared to single defects. This research addresses this critical gap, emphasizing the need for improved methods to classify mixed-type defects, which are more complex and challenging. To tackle this challenge, this paper introduces the active contour-based lightweight depthwise network (AC-LDN) model for the classification of multi-defect wafer map patterns. Initially, multi-defect features are extracted using an active contour-based segmentation model. Subsequently, the learning model employs a depthwise convolutional neural network (CNN) architecture that combines separable CNN and dilated CNN techniques. This unique approach optimizes the model in the separable segment while effectively addressing defect complexity in the depthwise segments. Consequently, AC-LDN outperforms other state-of-the-art models, offering a balance between lightweight characteristics and high accuracy. The proposed method demonstrates its superiority over previous models when evaluated on the extsdsensive multi-wafer map dataset, achieving an average classification accuracy exceeding 98% and a confusion matrix coefficient surpassing 0.97.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109707"},"PeriodicalIF":7.5,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}