2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)最新文献
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00061
Chuanyun Wang, Keyi Si, Tian Wang, Zhaokui Li, Na Lin, Ershen Wang
Addressing the problems of flying small target detection in infrared image sequences, a new approach is proposed based on generalized low-rank background estimation. Firstly, the generalized low rank approximations is introduced to model infrared background image over sequential infrared images. Subsequently, the foreground target image is achieved by background subtraction with the generalized low-rank background estimation. Finally, flying small target detection is achieved over separated target image by threshold segmentation. The experiment results on two infrared image sequences of flying plane demonstrate that the proposed method have effective detection performance and outperform the baseline methods in precision and recall evaluation.
{"title":"Sequential Detection of Flying Small Target in Infrared Images Based on Generalized Low-Rank Background Estimation","authors":"Chuanyun Wang, Keyi Si, Tian Wang, Zhaokui Li, Na Lin, Ershen Wang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00061","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00061","url":null,"abstract":"Addressing the problems of flying small target detection in infrared image sequences, a new approach is proposed based on generalized low-rank background estimation. Firstly, the generalized low rank approximations is introduced to model infrared background image over sequential infrared images. Subsequently, the foreground target image is achieved by background subtraction with the generalized low-rank background estimation. Finally, flying small target detection is achieved over separated target image by threshold segmentation. The experiment results on two infrared image sequences of flying plane demonstrate that the proposed method have effective detection performance and outperform the baseline methods in precision and recall evaluation.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122159801","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}
The internet and medical platform plays an important role increasingly in the process of seeking medical treatment, but the problem, "three long and one short", has not been effectively solved in most of the major hospitals. In this paper, we propose a public platform based on WeChat, which plans the treatment routes of all patients overall and reasonably. Firstly, to improve the traditional medical treatment process, the optimal clinic route system is combined with it, then we propose two different optimal clinic route schemes with the shortest treatment time solved by retrospective method. We carried out simulation experiments on the optimal waiting algorithm. The results show that compared with the worst case, the designed algorithm greatly saves the time and distance of the consultation, which proves that the algorithm can reasonably and effectively plan the treatment route and improve the medical service level.
{"title":"Design of Optimal Clinic Route System Based on WeChat Platform","authors":"Wenhao Chen, Shi-yi Pan, Yang Li, Jiayang Wang, Jiaqi Wang, Yuting Zhang, Wendi Zhu","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00084","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00084","url":null,"abstract":"The internet and medical platform plays an important role increasingly in the process of seeking medical treatment, but the problem, \"three long and one short\", has not been effectively solved in most of the major hospitals. In this paper, we propose a public platform based on WeChat, which plans the treatment routes of all patients overall and reasonably. Firstly, to improve the traditional medical treatment process, the optimal clinic route system is combined with it, then we propose two different optimal clinic route schemes with the shortest treatment time solved by retrospective method. We carried out simulation experiments on the optimal waiting algorithm. The results show that compared with the worst case, the designed algorithm greatly saves the time and distance of the consultation, which proves that the algorithm can reasonably and effectively plan the treatment route and improve the medical service level.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"47 38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124721726","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}
In this paper, we propose a P-spike deep neural network (P-SDNN) for image classification based on an adaptive simplified pulse coupled neural network (SPCNN) temporal coding. The proposed P-SDNN model introduces a SPCNN tem-coding layer into a spiking deep neural network (SDNN) with parameters adjusted by unsupervised STDP learning rule. The advantage of the proposed SPCNN temporal coding is to obtain adaptive time steps in terms of different input images. Each time step corresponds to a spiking-timing map which may contain a semantic segmentation of the input image. This is guaranteed by the working principle of SPCNN that the higher the neuron intensity is, the larger its internal activity will be and the earlier it will fire. And the adjacent neurons with similar intensity will pulse synchronously in a spikingtiming map. We evaluate the proposed P-SDNN model in the tasks of image classification on the Caltech face/motorbike and MNIST datasets. The experiments show that, under the same experimental conditions, the proposed P-SDNN model performs better than the SDNN model without SPCNN tem-coding.
{"title":"P-Spiking Deep Neural Network Based on Adaptive SPCNN Temporal Coding","authors":"Yuli Chen, Huiting Yao, Miao Ma, Zhao Pei, Xingwei Li, Zengguo Sun","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00089","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00089","url":null,"abstract":"In this paper, we propose a P-spike deep neural network (P-SDNN) for image classification based on an adaptive simplified pulse coupled neural network (SPCNN) temporal coding. The proposed P-SDNN model introduces a SPCNN tem-coding layer into a spiking deep neural network (SDNN) with parameters adjusted by unsupervised STDP learning rule. The advantage of the proposed SPCNN temporal coding is to obtain adaptive time steps in terms of different input images. Each time step corresponds to a spiking-timing map which may contain a semantic segmentation of the input image. This is guaranteed by the working principle of SPCNN that the higher the neuron intensity is, the larger its internal activity will be and the earlier it will fire. And the adjacent neurons with similar intensity will pulse synchronously in a spikingtiming map. We evaluate the proposed P-SDNN model in the tasks of image classification on the Caltech face/motorbike and MNIST datasets. The experiments show that, under the same experimental conditions, the proposed P-SDNN model performs better than the SDNN model without SPCNN tem-coding.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125736462","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00085
Weifang Ma, Peiyan Wang, Dongfeng Cai, Dahan Wang
Content-based 3D CAD model retrieval takes a 3D CAD model as input and finds other models with the same or similar structure. This paper proposes a two-stage retrieval method that can take into account the global and local similarity of CAD models. In the first stage, the CAD model formation candidate modles with high global matching degree with the query model is selected, and the TF-IMF (Term Frequency-Inverse Model Frequency) vector method is proposed to describe the global surface line distribution of the 3D CAD model. In the second stage, on the basis of the global similarity, the models which are locally similar with the query models are further filtered, and the attribute adjacency graphs between models are calculated by ACO (ant colony optimization) algorithm. Experimental results show that the proposed method achieves better retrieval performance than the maximum clique method based on the attribute adjacency graph (NDCG), which is 90.68%, and has higher retrieval efficiency.
基于内容的三维CAD模型检索以一个三维CAD模型为输入，查找具有相同或相似结构的其他模型。提出了一种考虑CAD模型全局相似度和局部相似度的两阶段检索方法。第一阶段，选取与查询模型全局匹配度高的CAD模型形成候选模型，提出TF-IMF (Term Frequency- inverse model Frequency)向量法描述三维CAD模型的全局面线分布;第二阶段，在全局相似度的基础上，进一步过滤与查询模型局部相似的模型，并采用蚁群优化算法计算模型间的属性邻接图。实验结果表明，该方法的检索性能优于基于属性邻接图(NDCG)的最大团方法，检索率为90.68%，具有更高的检索效率。
{"title":"Research on 3D CAD Model Retrieval Algorithm Based on Global and Local Similarity","authors":"Weifang Ma, Peiyan Wang, Dongfeng Cai, Dahan Wang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00085","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00085","url":null,"abstract":"Content-based 3D CAD model retrieval takes a 3D CAD model as input and finds other models with the same or similar structure. This paper proposes a two-stage retrieval method that can take into account the global and local similarity of CAD models. In the first stage, the CAD model formation candidate modles with high global matching degree with the query model is selected, and the TF-IMF (Term Frequency-Inverse Model Frequency) vector method is proposed to describe the global surface line distribution of the 3D CAD model. In the second stage, on the basis of the global similarity, the models which are locally similar with the query models are further filtered, and the attribute adjacency graphs between models are calculated by ACO (ant colony optimization) algorithm. Experimental results show that the proposed method achieves better retrieval performance than the maximum clique method based on the attribute adjacency graph (NDCG), which is 90.68%, and has higher retrieval efficiency.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131087960","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}
Poverty has always been one of the most acute social problems in the world. In order to eradicate poverty, the Chinese government has invested a lot of manpower and material resources, and promised to lead all poor areas and poor people into a well-off society by 2020. In this process, the county government as the main battlefield of poverty alleviation, the complex work of poverty alleviation has brought considerable pressure to grass-roots cadres and helping cadres. For the sake of improving the efficiency of support work, we propose a real-time feedback approach based on semantic clustering for poverty alleviation problem, through which we can build a bridge between grass-roots cadres and decision makers. In this method, we first use a fast label extraction method to quickly extract important feature words from the complicated help information. Secondly, we use unsupervised text clustering method to identify important poverty alleviation problems from these feature words, so as to provide a reference for the poverty alleviation workers to carry out their work in an orderly and targeted manner. The experimental results for different regions show that the poverty alleviation problem identified by our proposed method can reflect regional characteristics.
{"title":"A Real-Time Feedback Approach Based on Semantic Clustering for Poverty Alleviation Problem","authors":"Zizhen Peng, Guobei Peng, Zhiyi Mo, Guangyao Pang, Zongyuan Zheng, Xiang Wei","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00081","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00081","url":null,"abstract":"Poverty has always been one of the most acute social problems in the world. In order to eradicate poverty, the Chinese government has invested a lot of manpower and material resources, and promised to lead all poor areas and poor people into a well-off society by 2020. In this process, the county government as the main battlefield of poverty alleviation, the complex work of poverty alleviation has brought considerable pressure to grass-roots cadres and helping cadres. For the sake of improving the efficiency of support work, we propose a real-time feedback approach based on semantic clustering for poverty alleviation problem, through which we can build a bridge between grass-roots cadres and decision makers. In this method, we first use a fast label extraction method to quickly extract important feature words from the complicated help information. Secondly, we use unsupervised text clustering method to identify important poverty alleviation problems from these feature words, so as to provide a reference for the poverty alleviation workers to carry out their work in an orderly and targeted manner. The experimental results for different regions show that the poverty alleviation problem identified by our proposed method can reflect regional characteristics.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848119","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}
Pub Date : 2019-10-01DOI: 10.1109/iucc/dsci/smartcns.2019.00014
Welcome to the 2nd International Workshop on Next Generation Data-driven Networks (NGDN-2019) in-conjunction with the 18th International Conference on Ubiquitous Computing and Communications (IEEE IUCC-2019) held in Shenyang, China. NGDN-2019 is soliciting original and unpublished papers addressing research challenges and advances towards the design, implementation and evaluation of data communication technologies, systems and machine learning.
{"title":"Message from the NGDN 2019 Workshop Chairs","authors":"","doi":"10.1109/iucc/dsci/smartcns.2019.00014","DOIUrl":"https://doi.org/10.1109/iucc/dsci/smartcns.2019.00014","url":null,"abstract":"Welcome to the 2nd International Workshop on Next Generation Data-driven Networks (NGDN-2019) in-conjunction with the 18th International Conference on Ubiquitous Computing and Communications (IEEE IUCC-2019) held in Shenyang, China. NGDN-2019 is soliciting original and unpublished papers addressing research challenges and advances towards the design, implementation and evaluation of data communication technologies, systems and machine learning.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834582","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00041
Yi Zhang, Yao Wang, Cen Wu, Bin Zhao, Jiamei Chen, Jing Dong
Spectrum management is a vital problem in cognitive radio networks. Unfortunately, most current spectrum management algorithms don't consider power control at the same time, which deviates from the original intention for cognitive radio (CR). Thus, a new spectrum management mechanism named Connection degree cost and Reward Based (CRB) algorithm for CR networks is presented in the paper. The new algorithm allocates available resources properly, while guaranteeing quality of service (QoS) for secondary users (SUs). And a new utility function is designed to characterize the system total utility more realistically in the network model. Numerical results validate the new algorithm gains better performance than the existing schemes both in the system total utility performance and in the system fairness performance.
频谱管理是认知无线电网络中的一个重要问题。然而，目前大多数频谱管理算法没有同时考虑功率控制，这偏离了认知无线电(CR)的初衷。为此，本文提出了一种新的基于连接度成本和奖励(Connection degree cost and Reward Based, CRB)的CR网络频谱管理机制。该算法在保证二级用户服务质量的前提下，合理分配可用资源。并设计了一个新的效用函数来更真实地表征网络模型中的系统总效用。数值结果表明，新算法在系统总效用性能和系统公平性性能上都优于现有方案。
{"title":"Connection Degree Cost and Reward Based Algorithm in Cognitive Radio Networks","authors":"Yi Zhang, Yao Wang, Cen Wu, Bin Zhao, Jiamei Chen, Jing Dong","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00041","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00041","url":null,"abstract":"Spectrum management is a vital problem in cognitive radio networks. Unfortunately, most current spectrum management algorithms don't consider power control at the same time, which deviates from the original intention for cognitive radio (CR). Thus, a new spectrum management mechanism named Connection degree cost and Reward Based (CRB) algorithm for CR networks is presented in the paper. The new algorithm allocates available resources properly, while guaranteeing quality of service (QoS) for secondary users (SUs). And a new utility function is designed to characterize the system total utility more realistically in the network model. Numerical results validate the new algorithm gains better performance than the existing schemes both in the system total utility performance and in the system fairness performance.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115160428","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00060
Bin Dong, Linan Jia, Yuntao Wang, Jianqun Li, Guojie Yang
The watershed algorithm is widely used in the field of the image segmentation, which can overcome the difficulty of image analysis caused by cell overlap. However, the result of the image segmentation with the watershed algorithm were often over-segmentated. To solve this problem, the k-medoids clustering algorithm was introduced to simplify the gradient image, which is preprocessed from the original image. The edge information of the original image was obtained by the Canny edge detection operator, and the target region template was calculated by the optimized initial segmentation. Then, the segmentation result was obtained. The improved algorithm was evaluated by the segmentation accuracy compared with the professional segmented pathology image. The results show that the improved watershed algorithm proposed in this paper has a specific advantage in alleviating the phenomenon of over-segmentation, and the target area appears more completely.
{"title":"An Improved Watershed Algorithm Based on k-Medoids in Cervical Cancer Images","authors":"Bin Dong, Linan Jia, Yuntao Wang, Jianqun Li, Guojie Yang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00060","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00060","url":null,"abstract":"The watershed algorithm is widely used in the field of the image segmentation, which can overcome the difficulty of image analysis caused by cell overlap. However, the result of the image segmentation with the watershed algorithm were often over-segmentated. To solve this problem, the k-medoids clustering algorithm was introduced to simplify the gradient image, which is preprocessed from the original image. The edge information of the original image was obtained by the Canny edge detection operator, and the target region template was calculated by the optimized initial segmentation. Then, the segmentation result was obtained. The improved algorithm was evaluated by the segmentation accuracy compared with the professional segmented pathology image. The results show that the improved watershed algorithm proposed in this paper has a specific advantage in alleviating the phenomenon of over-segmentation, and the target area appears more completely.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133539602","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00141
Zhaoyang Sun, Yue Cheng, Xu Mao, Kuitong Xian, He Liu, Xin An
China aims to lift out all the impoverished people living below the current poverty line out of poverty by 2020, this is still a great challenge. Big data technology is used for targeted poverty alleviation. Identification of the poor households is based on the data from various channels. This paper introduced big data and standardization researches and applications in targeted poverty alleviation and presented a standard system framework of targeted poverty alleviation under the background of big data, and the methodology, business analysis of poverty alleviation are also presented.
{"title":"Big Data and Standardization Application in Targeted Poverty Alleviation","authors":"Zhaoyang Sun, Yue Cheng, Xu Mao, Kuitong Xian, He Liu, Xin An","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00141","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00141","url":null,"abstract":"China aims to lift out all the impoverished people living below the current poverty line out of poverty by 2020, this is still a great challenge. Big data technology is used for targeted poverty alleviation. Identification of the poor households is based on the data from various channels. This paper introduced big data and standardization researches and applications in targeted poverty alleviation and presented a standard system framework of targeted poverty alleviation under the background of big data, and the methodology, business analysis of poverty alleviation are also presented.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116538257","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}
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00145
Shuqin Li, Renzhi Wu, Jianbo Bo
Computer game is an important research direction in the field of artificial intelligence. Among them, incomplete information poker games are relatively difficult to implement compared to the intelligent algorithm of chess games. Dou Dizhu game is a kind of incomplete information game poker game. This paper uses convolution neural network, through the analysis large number of game data, to obtain a better landlord identity card model. Experimental results show that the model can provide efficient and reasonable results in Dou Dizhu games, similar to poker played by humans.
{"title":"Study on the Play Strategy of Dou Dizhu Poker Based on Convolution Neural Network","authors":"Shuqin Li, Renzhi Wu, Jianbo Bo","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00145","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00145","url":null,"abstract":"Computer game is an important research direction in the field of artificial intelligence. Among them, incomplete information poker games are relatively difficult to implement compared to the intelligent algorithm of chess games. Dou Dizhu game is a kind of incomplete information game poker game. This paper uses convolution neural network, through the analysis large number of game data, to obtain a better landlord identity card model. Experimental results show that the model can provide efficient and reasonable results in Dou Dizhu games, similar to poker played by humans.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125791954","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}
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