2018 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第9页

Research For Hakka Input Method With Hio-Liu Accent On Mobile Platforms 移动平台客家语重音输入法研究

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526977

Feng-Long Huang, M. Liu

{"title":"Research For Hakka Input Method With Hio-Liu Accent On Mobile Platforms","authors":"Feng-Long Huang, M. Liu","doi":"10.1109/ICMLC.2018.8526977","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526977","url":null,"abstract":"According to a survey study during year 2016 about the Hakka dialect made by Hakka Affairs Council, Hakka people in Taiwan has a population ratio of 19.3%, with 4.54 million people, making it the second largest cultural group in Taiwan. Coincidentally, the researcher of this thesis is a Taiwanese from Hsinchu city with a Hakka origin. Also, the Hakka accent in Hsinchu is called Hio-liu accent. Thus, this paper is based just on Hio-liu accent.The Hakka input method has not only the basic functions such as Preference, Quick-type, words-prediction, displays but also a pinyin selection on the smart phone monitor for the user to key-in the desired word and instantly provides the special Hakka characters. This input method is designed for people who do not understand Hakka language well, and allows the users to use phonetic notation (without accents) as input. There are 5,366 records in Hakka monoword Pinyin database, 19,351 records in Hakka Word Pinyin database, 8,505 records in Hakka Previous and Successive word database and 1,679 records in Chinese To Hakka database. Speech synthesis system requires many voice files: 2,949 words and 1,566 phrases.In our survey, the Hio-liu input method got 4.0 points on average. Chinese-Hakka input method got 4.5 points on average and Hakka Language speech got 4.3 points on average. Most users in Taiwan use pinyin input method, which has the large influence on the points. In the questionnaire about speech input, users believe that the system is conducive to those people who are learning Hakka and effective for the development of e-learning environment.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123124835","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

Fuzzy Logic Infused Intelligent Scent Dispenser For Creating Memorable Customer Experienceof Long-Tail Connected Venues 融入模糊逻辑的智能香味机，为长尾连接场所创造难忘的客户体验

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527046

Chandrasekar Vuppalapati, Rajasekar Vuppalapati, Sharat Kedari, Anitha Ilapakurti, J. Vuppalapati, S. Kedari

{"title":"Fuzzy Logic Infused Intelligent Scent Dispenser For Creating Memorable Customer Experienceof Long-Tail Connected Venues","authors":"Chandrasekar Vuppalapati, Rajasekar Vuppalapati, Sharat Kedari, Anitha Ilapakurti, J. Vuppalapati, S. Kedari","doi":"10.1109/ICMLC.2018.8527046","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527046","url":null,"abstract":"In today’s competitive business environment, creating memorable experiences and emotionalconnections with consumers is critical to win customer spending and long term brand loyalty. Brands want their customers to be in pleasing, subliminal scented environments. Even a few micro particles of scent can do a lot of marketing’s heavy lifting, from improving consumer perceptions of quality to increasing the number of visits. Hence, high roller venues such as Trump Towers and Caesars Palace of the World use digital connected intelligent scent disperser systems that deliver seamless olfactory experiences to connect with consumers on a deeper, emotionaland personal level.The challenges, however, for venues with limited digital connected infrastructure and deficient intelligent systems are lack of engagement with patrons at a personal and emotional level and thus miss recurring business opportunities and sustained long-term brand loyalty.This research paper addresses the challenge by developing fuzzy control infused autonomous intelligent scent dispensers that bring connected intelligence to non-connected venues. The paperpresents prototyping solution design, as well as its application and certain experimental results.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115092058","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

Solar Image Matching Based on Improved Freak Algorithm 基于改进Freak算法的太阳图像匹配

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527012

Meiping Song, Yue-Jing Cao, Chunyan Yu, Jubai An, Chein-I. Chang

{"title":"Solar Image Matching Based on Improved Freak Algorithm","authors":"Meiping Song, Yue-Jing Cao, Chunyan Yu, Jubai An, Chein-I. Chang","doi":"10.1109/ICMLC.2018.8527012","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527012","url":null,"abstract":"FREAK algorithm has the defects of not having scale invariance and single feature point matching strategy, being prone to unsatisfactory results. Based on AKAZE and RANSAC algorithm, an improved FREAK algorithm is proposed, named AKAZE-FREAK. The image pyramid was constructed by using the nonlinear diffusion filter and the numerical solution was obtained by fast display diffusion (FED) to obtain the image point coordinates with sub-pixel precision. Then the feature points were described by the FREAK descriptors to distribute directions for the feature points. Finally, based on the initial matching of the hamming distance to the eigen vector, RANSAC is used to eliminate the error matching point. In this paper, this algorithm is applied to the recognition of repeated shooting solar images. After the improved algorithm is compared with the SIFT-FREAK, SURF-FREAK and FREAK algorithms, it shows an improvement in the image feature point matching accuracy with good robustness on the scale difference, illumination difference and rotation difference of the image. It also improves the accuracy of the judgement of the late repeat.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116048725","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

Preserving Community Feature Extraction And Mrmr Feature Selection For Link Classification In Complex Networks 基于社区特征提取和Mrmr特征选择的复杂网络链路分类

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526974

Jiehua Wu, Bei Zhou, Jing Shen

引用次数: 0

Applying Geometric Dilution Of Precision Approximation To Adaptive Neural Network Learning For Precise Mobile Station Positioning 基于几何稀释精度逼近的自适应神经网络学习在移动站精确定位中的应用

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526984

Chien-Sheng Chen, Jen-Fa Huang, Siou-Cyuan Lin, Ching-Chuan Tseng, Chia-Ming Wu

{"title":"Applying Geometric Dilution Of Precision Approximation To Adaptive Neural Network Learning For Precise Mobile Station Positioning","authors":"Chien-Sheng Chen, Jen-Fa Huang, Siou-Cyuan Lin, Ching-Chuan Tseng, Chia-Ming Wu","doi":"10.1109/ICMLC.2018.8526984","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526984","url":null,"abstract":"The geometric dilution of precision is widely used as criterion for selecting the best set of the measurement devices. Some methods had been proposed to rmd the value of the Geometric Dilution of Precision (GDOP), such as using inverse matrix to solve the linear equation. But it takes a large amount of computing time to find the optimal solution. In this paper, we propose to adopt a neural network with gradient descent adaptive learning rate training algorithm to approximate the value of GDOP. The proposed two types of input/output mapping used the element in the matrix as the input data of the neural network. After finishing the training process, it can get a better approximation results with the proposed two types. The simulation result shows that proposed algorithm has lower GDOP approximation error using less Epochs than the other types. For cellular wireless communication system, it will significantly reduce the computational complexity and the training time by selecting the service base station at first with three other base stations to estimate the position of mobile station.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116533268","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

An Improved Particle Swarm Algorithm for Constrained Optimization Problem 约束优化问题的改进粒子群算法

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526956

Kang Hu, Guo-Li Zhang, Bo Xiong

引用次数: 3

ICMLC 2018 List Reviewer Page ICMLC 2018列表审核页面

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/icmlc.2018.8526967

引用次数: 0

ICMLC 2018 Greetings from Program Chairs ICMLC 2018项目主席的问候

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/icmlc.2018.8526920

引用次数: 0

Discrete-Time Lu Chaotic Systems Synchronization with One ZND Controller Input and Zead Formulas 具有一个ZND控制器输入和前置公式的离散时间Lu混沌系统同步

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526983

Huanchang Huang, Jian-Xin Zhang, Jian Li, Min Yang, Yunong Zhang

引用次数: 2

Recent Advance On Generative Adversarial Networks 生成对抗网络研究进展

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526990

Sufang Zhang, Jun-Hai Zhai, Ding-Sheng Luo, Yan Zhan, Jun-Fen Chen

引用次数: 5