2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第9页

Bi-Objective Search Method for Bayesian Network Structure Learning 贝叶斯网络结构学习的双目标搜索方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754657

Ting Wu, H. Qian, Aimin Zhou, Zhenzi Li

引用次数: 1

Road Network Optimization of Intelligent Warehouse Picking Systems Based on Improved Genetic Algorithm 基于改进遗传算法的智能仓储拣选系统路网优化

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754603

Ruiping Yuan, Luke Pan, Juntao Li, Zhixin Chen

{"title":"Road Network Optimization of Intelligent Warehouse Picking Systems Based on Improved Genetic Algorithm","authors":"Ruiping Yuan, Luke Pan, Juntao Li, Zhixin Chen","doi":"10.1109/CCIS53392.2021.9754603","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754603","url":null,"abstract":"Intelligent Warehouse Picking System based on logistics robots is a new type of parts-to-picker order picking system, where robots carry mobile shelves to stationary pickers. The new picking mode puts forward higher requirements for the layout and design of warehousing network. In the existing few research on the path network optimization under the intelligent warehouse picking mode, the turning factors which obviously affects the picking efficiency, are seldom considered. In this paper, a mathematical model minimizing the total travel distance of logistics robots to complete all picking tasks is established, where the turning of robots is transformed into travel distance by cost function. Then an improved genetic algorithm with temperature parameter T and Metropolis acceptance criterion is proposed to solve the road network planning model. Finally, MATLAB is used to simulate and compare different road network layout strategies and algorithms from the total picking distance and total picking time to verify the effectiveness of the proposed method.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125081329","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

Updating Land-Cover Maps by Iterative Difference Learning Network 基于迭代差分学习网络的土地覆盖地图更新

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754673

M. Zhang, Zheng Feng, Jinxin Wei, Maoguo Gong

{"title":"Updating Land-Cover Maps by Iterative Difference Learning Network","authors":"M. Zhang, Zheng Feng, Jinxin Wei, Maoguo Gong","doi":"10.1109/CCIS53392.2021.9754673","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754673","url":null,"abstract":"Multi-temporal remote sensing image classification aims to exploit the available information of image in the source domain to classify target domain. Since the manual labeling is time-consuming and labor-intensive, it is unrealistic to have enough labels for all images of the time series. By analyzing the difference information of multi-temporal images, the labels of unchanged region can be transferred form source domain to the target domain. In order to further utilize the difference information and learn a robust classifier, we propose an iterative difference learning network (IDLnet) to update land-cover maps in this paper. The proposed method aims at optimizing the process of label transfer by analyzing results of classifier and to fine-tuning it with a series of dynamic training sets. In proposed method, we first utilize the source domain data to initialize a training set and train a classifier to classify both the source and target domains. The change detection (CD) is applied on the ground image datasets and the classification result. Then the transfer learning (TL) is employed to transfer the unchanged information to fine-tuning network. We detect changes of the classification result images again and fuse the previous CD results. Finally, the accuracy cannot be improved after several iterations of fine-tuning the classifier.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125845166","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

Research on Data Acquisition and Processing Under the Coordination of Multiple Value Chains in the Manufacturing Industry—Taking the Electric Vehicle Manufacturing Industry as an Example 制造业多价值链协同下的数据采集与处理研究——以电动汽车制造业为例

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754677

Shiping Geng, Yuntian Liu, D. Niu, Xiao-dan Guo

{"title":"Research on Data Acquisition and Processing Under the Coordination of Multiple Value Chains in the Manufacturing Industry—Taking the Electric Vehicle Manufacturing Industry as an Example","authors":"Shiping Geng, Yuntian Liu, D. Niu, Xiao-dan Guo","doi":"10.1109/CCIS53392.2021.9754677","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754677","url":null,"abstract":"With the rapid development of economic globalization and the continuous changes of digital technology and the Internet, the electric vehicle manufacturing industry has become an effective way to improve its own competitive advantage by strengthening the collaborative management of many industrial chains and value chains, while the construction of data space is to increase the company’s An effective tool for chain collaboration. Based on this, this paper first summarizes the characteristics of the data sources of the electric vehicle manufacturing industry from three aspects: “large quantity”, “multiple categories”, and “rapid change”. Secondly, based on the characteristics of the data sources of electric vehicle manufacturers, the theory of data acquisition and processing is explained, and big data methods are proposed to solve the problem of data acquisition in the electric vehicle manufacturing industry. Then, in the context of the rapid growth of the data volume of electric vehicle manufacturers and the increasingly complex data types, the main problems of using big data to obtain data from electric vehicle manufacturers are analyzed. Finally, the countermeasures for data acquisition methods of electric vehicle manufacturers in the future are proposed.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129936710","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

Non-Mydriatic Fundus Images Enhancement Based on Conformal Mapping Extension 基于保角映射扩展的非散瞳眼底图像增强

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754644

Songlin Yan, Xiujiao Chen, Jiehua Sun, Xiaoying Tang, Xuebin Chi

{"title":"Non-Mydriatic Fundus Images Enhancement Based on Conformal Mapping Extension","authors":"Songlin Yan, Xiujiao Chen, Jiehua Sun, Xiaoying Tang, Xuebin Chi","doi":"10.1109/CCIS53392.2021.9754644","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754644","url":null,"abstract":"Image enhancement is an important technique for improving observation, especially for non-mydriatic fundus images. Hence a new non-mydriatic fundus images enhancement pipeline is proposed here. Our fundamental procedure is from automatically generating the mask of the field of view (FOV) to restoring their original color. Briefly speaking, by extending the FOV region with conformal mapping, we can solve the boundary problems of image enhancement. And inspired by high dynamic range imaging (HDRI) theory, a new color restoration tactic is developed to correct the color deformation of enhanced images. To demonstrate the robustness of our algorithms, a hybrid test dataset is introduced. It not only contains some public datasets, e.g. DRIVE, Kaggle and Web (some unannotated images from a web), but also includes many private non-mydriatic datasets that were collected from the third affiliated hospital of our collaborative university. The masks were validated on DRIVE dataset by using 5 famous criteria. And we performed all enhanced results with 10 different objective image quality assessment (IQA) models. The experimental outputs of mask segmentation achieve the similarity coefficients: Cosine 99.594%, Sorensen-Dice 99.593%, Jaccard 99.19% and Pearson 98.714%, and Tanimoto 98.891%, respectively. The enhanced results from the IQA models are: BRISQE 38.9, BLIINDS2 49.87, BIQI 16.49, ILNIQE 43.39, NIQE 6.62, IFC 1.517, MS-SSIM 0.712, PSNR 21.33, SSIM 0.775, and VIF 0.2, respectively. Besides, we will opensource all programs and test codes on GitHub.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086355","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

Mask Image to Real Image Generation Based on Semantic Control Context Encoder 基于语义控制上下文编码器的掩模图像到真实图像的生成

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754642

Yangqianhui Zhang, Pingda Huang, Xinwei Li, Shuda Gao, Liang Zhao

引用次数: 0

An Efficient Neighborhood Structure Generating Method for Large-scale Traveling Salesman Problem 大规模旅行商问题的有效邻域结构生成方法

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754615

Kang Wang, Xinye Cai

引用次数: 1

Research on Online Scheduling Method for Flexible Assembly Workshop of Multi-AGV System Based on Assembly Island Mode 基于装配岛模式的多agv柔性装配车间在线调度方法研究

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754650

Xiangfei Ge, Lingli Li, Hao Chen

{"title":"Research on Online Scheduling Method for Flexible Assembly Workshop of Multi-AGV System Based on Assembly Island Mode","authors":"Xiangfei Ge, Lingli Li, Hao Chen","doi":"10.1109/CCIS53392.2021.9754650","DOIUrl":"https://doi.org/10.1109/CCIS53392.2021.9754650","url":null,"abstract":"In the context of intelligent manufacturing, traditional batch assembly line operations are difficult to meet the individualized and customized needs of customers. Aiming at the scheduling problem of flexible assembly workshop, this paper proposes a new assembly mode—assembly island mode, combined with multiple AGV systems, to meet the individual and customized needs of customers. Aiming at the uncertain characteristics of actual production, this paper proposes an online scheduling method for the assembly workshop of multiple AGV systems based on the static scheduling of the workshop, which is more suitable for the actual production process. In this paper, the online shop scheduling problem is decomposed into the assembly island allocation sub-problem and the AGV scheduling sub-problem to study. In the modeling of the assembly island allocation sub-problem, the AGV transportation system load is taken as part of the objective function to build the model. On the AGV scheduling sub-problem, the AGV system’s anti-deadlock strategy is set, and the AGV scheduling method based on the task benefit value is proposed to solve the AGV task allocation problem. Finally, through simulation experiments, the feasibility and efficiency of the online scheduling method are verified.","PeriodicalId":191226,"journal":{"name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669369","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

ATKey.Net: Keypoint Detection by Handcrafted and Learned CNN with Attention ATKey。Net:关键点检测由手工制作和学习CNN与注意力

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754617

Zhihong Wang, Jinshan Ma, Haiyang He, Zixuan Wu, Changying Wang, Li Cheng

引用次数: 0

Multi-scale Defective Samples Synthesis for Surface Defect Detection 表面缺陷检测的多尺度缺陷样品合成

2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2021-11-07 DOI: 10.1109/CCIS53392.2021.9754643

Zirong Liu, Zhihui Lai, C. Gao

引用次数: 0