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2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)最新文献

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A 12-lead ECG integrated circuit design with adjustable gain and bandwidth schemes 具有可调增益和带宽方案的12导联ECG集成电路设计
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.1109/IFUZZY.2017.8311814
Chang-Hsi Wu, Chen Chen, Jianchang Zhou
{"title":"A 12-lead ECG integrated circuit design with adjustable gain and bandwidth schemes","authors":"Chang-Hsi Wu, Chen Chen, Jianchang Zhou","doi":"10.1109/IFUZZY.2017.8311814","DOIUrl":"https://doi.org/10.1109/IFUZZY.2017.8311814","url":null,"abstract":"In this paper an integrated circuit design for 12-lead Electrocardiogram (ECG) measurement is proposed. This ECG capture circuit is designed having adjustable gain and bandwidth via externally digital control. In addition to a third-order high-pass filter (HPF) with 1 Hz lower 3-dB frequency and a third-order low-pass filter (LPF) with 150 Hz upper 3-dB frequency, a second-order notch filter (NF) for 60Hz power line noise rejection is also designed on the chip. Besides, a right-leg drive circuit (RLDC) is added to attenuate the common-mode noise. Simulated results of the proposed ECG capture circuit reveal gain of about 40∼70dB spanned from 0.1 to 150 Hz range excepting a 60 Hz notch point with attenuation lower than −57.6dB. The power consumption is 30mW from ±0.6V supply voltage. The core area is 1.2∗2.4mm2 in TSMC CMOS 0.18-μm process.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127032274","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
Appliances recognizable core extension with energy saving system using genetic algorithm 家电可识别核心扩展与节能系统采用遗传算法
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.1109/IFUZZY.2017.8311813
Pohsiang Tsai
{"title":"Appliances recognizable core extension with energy saving system using genetic algorithm","authors":"Pohsiang Tsai","doi":"10.1109/IFUZZY.2017.8311813","DOIUrl":"https://doi.org/10.1109/IFUZZY.2017.8311813","url":null,"abstract":"For the purpose of monitoring and understanding the circumstances of home appliances electricity usage at any time to take duly measures in energy saving; how to grasp the electricity usage status of home appliances in real-time scenario, a home energy saving system must be able to recognize the identities of the plugging appliances for core extensions or any wall outlets. Therefore, in this paper, we aim at designing an energy saving system that using a genetic algorithm for scheduling optimization, along with an intelligent appliances recognizable core extension that can automatically detect and recognize the plugging home appliances' identities in real-time scenarios.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127402963","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
Multi-attribute based fuzzy C-means in approximated feature space 近似特征空间中基于多属性的模糊c均值
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.1109/IFUZZY.2017.8311818
Zhulin Liu, C. L. P. Chen, Long Chen, Jin Zhou
{"title":"Multi-attribute based fuzzy C-means in approximated feature space","authors":"Zhulin Liu, C. L. P. Chen, Long Chen, Jin Zhou","doi":"10.1109/IFUZZY.2017.8311818","DOIUrl":"https://doi.org/10.1109/IFUZZY.2017.8311818","url":null,"abstract":"Recently, clustering illustrates its importance in knowledge discovery. However, most of the considered algorithms are efficient only on those linear separable datasets. Although the kernel based methods perform better with non-linear separable ones, at the same time, they are suffered from the missing of priority knowledge of a suitable kernel. Two latest methods which approximate date to infinity dimensional kernel feature spaces are considered in this paper. Furthermore, new attribute multi-kernel weighted fuzzy c-means are proposed by combining the randomly projected features. From the experiments shown in the simulation section, we conclude that we develop an improved version of attribute multi-kernel weighted fuzzy c-means, which achieves better accuracy.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"9 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113979937","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
Generalized fuzzy data mining for incomplete information 不完全信息的广义模糊数据挖掘
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.4236/JSEA.2018.116018
P. V. Reddy
{"title":"Generalized fuzzy data mining for incomplete information","authors":"P. V. Reddy","doi":"10.4236/JSEA.2018.116018","DOIUrl":"https://doi.org/10.4236/JSEA.2018.116018","url":null,"abstract":"Defining data inherently with fuzziness reduce the complexity of data mining during knowledge discovery process. The mining with fuzzy database will provide security because the original data need not be disclosed The fuzzy logic with two membership functions will give more evidence than the Zadeh single membership function. In this paper, the fuzzy data mining methods are discussed. The two fold fuzzy set with two membership functions is studied with \"Belief\" and \"\"Disbelief\". The fuzzy certainty factor (FCF) is difference of the two membership functions to eliminate conflict, The FCF and gives single fuzzy membership function. The fuzzy risk set is defined with fuzzy certainty factor for decision making. The fuzzy MapReducing with functional dependency is studied for association rules. The Generalized fuzzy reasoning is studied for data mining. The data mining with fuzzy risk set is studied to take the decisions. The business intelligence is given as an example.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132409108","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
Synthetic minority over-sampling technique based on fuzzy c-means clustering for imbalanced data 基于模糊c均值聚类的不平衡数据合成少数派过采样技术
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.1109/IFUZZY.2017.8311793
Hansoo Lee, Seung-Hyun Jung, Minseok Kim, Sungshin Kim
{"title":"Synthetic minority over-sampling technique based on fuzzy c-means clustering for imbalanced data","authors":"Hansoo Lee, Seung-Hyun Jung, Minseok Kim, Sungshin Kim","doi":"10.1109/IFUZZY.2017.8311793","DOIUrl":"https://doi.org/10.1109/IFUZZY.2017.8311793","url":null,"abstract":"Some solutions to solve the class imbalance problem that is one of the representative difficulties in machine learning have been proposed. Among them, SMOTE algorithm is proposed recently to reduce the influence of the problem and shows its remarkable performance to solve real world problems. This paper proposes a novel kind of the SMOTE algorithm by combining the previous SMOTE algorithm and fuzzy logic to deal with uncertainties underlying learning samples. Also, fuzzy c-means clustering is used to assign membership degree to given samples efficiently. Moreover, the extended gap statistics is applied to select the optimal number of cluster. The proposed algorithm is evaluated on using several benchmark datasets and shows its good performance by combining support vector machine classifier.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133714507","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: 12
Multi-layer fuzzy system modeling a new approach: Theory and application 多层模糊系统建模新方法:理论与应用
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2017-11-01 DOI: 10.1109/IFUZZY.2017.8311811
M. Zeinali
{"title":"Multi-layer fuzzy system modeling a new approach: Theory and application","authors":"M. Zeinali","doi":"10.1109/IFUZZY.2017.8311811","DOIUrl":"https://doi.org/10.1109/IFUZZY.2017.8311811","url":null,"abstract":"In this paper, the concept of multi-layer fuzzy system modeling methodology is presented to deal with rule uncertainties, to cover the different aspect of the modeled system, to reduce data acquisition process and to improve the accuracy and robustness of the systematic fuzzy modeling method proposed in references [1–3]. This method is based on partitioning of the output space using fuzzy c-means clustering and the projection of output space clusters onto input space to construct generic Zadeh fuzzy rules from input-output data. First, several fuzzy models of the system which are called \"primary fuzzy model\" in this paper, are constructed corresponding to different values of membership grade, the fuzziness parameter, and number of clusters. Then the final output of fuzzy model is calculated based on the weighted sum of the primary fuzzy models output. An approximation of two benchmark nonlinear functions are used to illustrate and describe the new modeling concept. Finally, the developed method is being employed to construct the multi-layer fuzzy dynamic model of a two degrees of freedom robot for control applications.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701116","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
Type-2 fuzzy neural network for lip activity detection 唇动检测的2型模糊神经网络
2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 1900-01-01 DOI: 10.1109/ifuzzy.2017.8311784
Gin-Der Wu, Li-Hui Lee
{"title":"Type-2 fuzzy neural network for lip activity detection","authors":"Gin-Der Wu, Li-Hui Lee","doi":"10.1109/ifuzzy.2017.8311784","DOIUrl":"https://doi.org/10.1109/ifuzzy.2017.8311784","url":null,"abstract":"A type-2 fuzzy neural network (T2FNN) is proposed for lip activity detection (LAD). In image and speech signal processing, LAD is an important classification problem. The gradient descent method is applied to reduce the cost function of T2FNN. In handing noisy data with uncertainties, type-2 fuzzy-systems usually outperform their type-1 counterparts. By structure and parameter learning, the classification of T2FNN has high discriminability. Compared with other existing fuzzy neural networks, the advantage of the T2FNN is its consideration of uncertainty. The effectiveness of the T2FNN is tested by lip activity detection. Experimental result shows that T2FNN works better than the other type-1 fuzzy neural networks.","PeriodicalId":148453,"journal":{"name":"2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988324","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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