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2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)最新文献

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Storage and retrieval of massive heterogeneous IoT data based on hybrid storage 基于混合存储的海量异构物联网数据存储与检索
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393258
Shanshan Wu, Liang Bao, Zisheng Zhu, Fan Yi, Weizhao Chen
{"title":"Storage and retrieval of massive heterogeneous IoT data based on hybrid storage","authors":"Shanshan Wu, Liang Bao, Zisheng Zhu, Fan Yi, Weizhao Chen","doi":"10.1109/FSKD.2017.8393258","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393258","url":null,"abstract":"With the rapid development of the Internet of Things (IoT), the IoT is characterized by a wide variety of data sources, large scale and heterogeneous structure. But those characteristics bring great difficulties to the storage and rapid retrieval of IoT data. By considering the common attributes of IoT data, based on plug-in ideas, combined with Redis and HBase, the paper proposes a framework named HSFRH-IoT, which solves the problem of efficient storage and retrieval of massive heterogeneous IOT. Finally, the insertion and query performance of the proposed HSFRH-IoT framework is tested in detail, and the results shows that it has better performance than other RDBMS based solutions.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125986459","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: 7
Parameter optimization based two-layer SVM classification model for evaluation of maize breeding 基于参数优化的双层支持向量机分类模型玉米育种评价
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393163
Xin Mao, Gang Zhao, R. Sun
{"title":"Parameter optimization based two-layer SVM classification model for evaluation of maize breeding","authors":"Xin Mao, Gang Zhao, R. Sun","doi":"10.1109/FSKD.2017.8393163","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393163","url":null,"abstract":"In the crop breeding evaluation process, breeders have to observe and record hundreds of thousands of material dates. The traditional breeding evaluation method is effective for selecting the optimal breeding materials by consideration of a lot of characters such as yield, resistance and growth period etc. But the traditional method is difficult to meet the needs of large-scale breeding. Combined with breeders on crop performance evaluation of comprehensive evaluation, the paper proposes parameter optimization based two-layer SVM classification model. The model uses the radial basis function as the kernel function, and uses the method of cross validation to train the sample data several times to obtain the optimal penalty coefficient C and the kernel function parameter g. In the first layer classification model, the breeding trait data is divided into three parts: high yield, stable yield and disease resistance, and the corresponding classification results are obtained. In the second layer model, the first layer classification results are used as the characteristic attribute; being input to the model to get the final category. In order to verify the effect of the model, the paper uses k-neighborhood, decision tree, and random forest and Naive Bayesian classifier as control. The experimental results show that the classification accuracy of the proposed two-layer classification optimization model is 91.523%, is more than other classifiers. So, the parameter optimization based two-layer SVM classification model is suitable for breeding evaluation technology.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121955160","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: 1
A kNN classifier optimized by P systems 一个由P系统优化的kNN分类器
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393307
Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo
{"title":"A kNN classifier optimized by P systems","authors":"Juan Hu, Guangchun Chen, Hong Peng, Jun Wang, Xiangnian Huang, Xiaohui Luo","doi":"10.1109/FSKD.2017.8393307","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393307","url":null,"abstract":"We propose a k-nearest neighbors (kNN) classification algorithm optimized by P systems in this article, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of several cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k-nearest neighbors for a sample. The proposed kNN-P is evaluated on 18 benchmark datasets and compared with classical kNN algorithm and 8 recently developed improved algorithms. Comparison results demonstrate the availability and effectiveness of the proposed algorithm.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"48 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113936248","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: 5
An accurate and efficient face recognition method based on hash coding 一种基于哈希编码的准确高效的人脸识别方法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393076
Yan Zeng, Xiaodong Cai, Yuelin Chen, M. Wang
{"title":"An accurate and efficient face recognition method based on hash coding","authors":"Yan Zeng, Xiaodong Cai, Yuelin Chen, M. Wang","doi":"10.1109/FSKD.2017.8393076","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393076","url":null,"abstract":"To improve the efficiency in face recognition with highdimension features extracted from deep model, a fast recognition method based on hash coding is proposed. Different from others, the hash coding and the cascade network are designed for a two-stage face recognition. Firstly, the low-dimensional and high-dimensional features are extracted according to different models. Secondly, the low-dimensional features are quantized into hash codes by a piecewise function. And then, the first-identify is completed by calculating hamming distance between the hash codes. Finally, the second-identify is completed by calculating cosine distance between the high-dimensional features of face images after the first-identify. The experimental results show that the method proposed can improve the Rank-1 recognition efficiency up to 64% while the accuracy is the same as VGG.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124529108","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: 2
Resource scheduling strategy in consistency-based information-centric networking 基于一致性的信息中心组网中的资源调度策略
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393182
Lan Shi, Ying Zhao, Jianhui Lv, Peng Yin
{"title":"Resource scheduling strategy in consistency-based information-centric networking","authors":"Lan Shi, Ying Zhao, Jianhui Lv, Peng Yin","doi":"10.1109/FSKD.2017.8393182","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393182","url":null,"abstract":"In this paper, the information of Information-Centric Networking (ICN) based on the information consistency is classified and stored according to the classification standard. Then the resources in ICN are divided by the classification standard to calculate the category popularity. Afterward, combining the 0–1 knapsack model with the dynamic programming algorithm, we design the 0–1 knapsack resource scheduling strategy based on the category popularity. At last, the simulation experiments illustrate the rationality and superiority of resource scheduling strategy.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116420885","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
Semi-automated data classification with feature weighted self organizing map 基于特征加权自组织映射的半自动数据分类
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392964
A. Starkey, Aliyu Usman Ahmad
{"title":"Semi-automated data classification with feature weighted self organizing map","authors":"A. Starkey, Aliyu Usman Ahmad","doi":"10.1109/FSKD.2017.8392964","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392964","url":null,"abstract":"This paper presents a Feature Weighted Self-Organizing Map (FWSOM) that analyses the topology information of a converged standard Self organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with irrelevant inputs. We demonstrate an improved classification accuracy with the proposed method by comparison with the standard SOM and other relevant existing classifiers on synthetic and real-world datasets. In addition, the FWSOM method was able to successfully identify the relevant features which in turn were able to improve the classification performance of the other classification methods.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125043275","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
Multiscale method based on spline regression for comparing multiple nonparametric curves 基于样条回归的多尺度非参数曲线比较方法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8393375
Na Li, Xuhua Liu
{"title":"Multiscale method based on spline regression for comparing multiple nonparametric curves","authors":"Na Li, Xuhua Liu","doi":"10.1109/FSKD.2017.8393375","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8393375","url":null,"abstract":"SiZer (SIgnificant ZERo crossing of the derivatives) is a powerful scale-space visualization technique for exploratory data analysis. In this paper a new version of SiZer based on regression spline is proposed for comparing multiple regression curves. The new SiZer is constructed on the basis of p-values for testing the equality of multiple regression functions at different locations and scales. Fiducial inference and regression spline are applied to gain the p-values. In addition, multiple testing adjustments are carried out to control the row-wise false discovery rate and family-wise error rate of the proposed SiZer, respectively. The new SiZer is more powerful even in the case of small sample size case due to the good properties of p-value and FDR control. Simulation results show that the new SiZer performs well compared with the existing SiZers. Finally, a real data example is carried out to illustrate its usage in applications.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132316118","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
The computing approach for attribute reducts of information systems based on fuzzy matrix 基于模糊矩阵的信息系统属性约简计算方法
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392937
Yanling Wang, Yanyong Guan, Fasheng Xu, Hongkai Wang
{"title":"The computing approach for attribute reducts of information systems based on fuzzy matrix","authors":"Yanling Wang, Yanyong Guan, Fasheng Xu, Hongkai Wang","doi":"10.1109/FSKD.2017.8392937","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392937","url":null,"abstract":"For the set-valued information systems, the similarity degree of two fuzzy matrices is defined, and utilized to design one heuristic algorithm for computing the attribute reducts. This heuristic algorithm only employs the minimizing operation and comparison operation of the matrices. For the set-valued decision information systems, the relative reducts is defined according to the decision values of the objects, so, the fuzzy matrix determined by the condition attributes is modified, and utilized to design the heuristic algorithm for computing the relative attribute reducts. This approach is also applicable to other information systems if the fuzzy relation is employed to describe the similarity degree of objects.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126835338","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: 3
Balance control by diffuse logic for a phoenix hexapod robot 凤凰六足机器人的漫射逻辑平衡控制
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392947
D. Alulema, Cristian Andino, Diego Rodriguez, V. Proano, Alexander Ibarra, D. Morocho, Freddy Tapia, Veronica Alulema
{"title":"Balance control by diffuse logic for a phoenix hexapod robot","authors":"D. Alulema, Cristian Andino, Diego Rodriguez, V. Proano, Alexander Ibarra, D. Morocho, Freddy Tapia, Veronica Alulema","doi":"10.1109/FSKD.2017.8392947","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392947","url":null,"abstract":"Using diffuse control algorithms, a position control for the six extremities of the Hexapod Robot was built. As input to the controller, a LIS3DSH accelerometer was used, which gives the Roll values for the angle in which the accelerometer tilts, typical of the Discovery STM32F4 card. It was necessary to build a PI controller in order to reduce the error in stationary state and stabilize the robot in a zero degree tilt position, allowing the Hexapod robot to stand and balance automatically over different surfaces.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126349464","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: 2
Some operations on strong intuitionistic fuzzy k-uniform hypergraphs 强直觉模糊k-一致超图上的一些运算
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2017-07-29 DOI: 10.1109/FSKD.2017.8392989
Qian Wang, Z. Gong
{"title":"Some operations on strong intuitionistic fuzzy k-uniform hypergraphs","authors":"Qian Wang, Z. Gong","doi":"10.1109/FSKD.2017.8392989","DOIUrl":"https://doi.org/10.1109/FSKD.2017.8392989","url":null,"abstract":"Hypergraph is a useful tool to model complex systems and it could be considered as a natural generalizations of graphs. In this paper, the notion of strong intuitionistic fuzzy k-uniform hypergraph was applied and some operation of strong intuitionistic fuzzy k-uniform hypergraphs was defined, such as Cartesian product, normal product, strong product, lexicographic product, join, union. Further more, we proved if hypergraph H is formed by one of these operations, then this hypergraph is strong intuitionistic fuzzy k-uniform hypergraph.","PeriodicalId":236093,"journal":{"name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121581635","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
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