TP03 Signals and Intelligent Systems
Time : 17:00~18:30
Room : 201~204
Chair : (, )
       TP03-71
Bluetooth-based Tree Topology Network for Wireless Industrial Applications

Joonkyo Kim, Shin-kwang Kang, Jaehyun Park(Inha University, Korea, Republic of )

The Bluetooth Low Energy(BLE) which has been widely adopted in the most of smartphone plays a major communication channel for the range of industrial applications as well as the personal applications. However, since BLE supports only the star topology, it is impossible to configure more complex network topologies, and, in turn, the coverage of the network is limited. In this paper, we propose a method to implement the tree topology network using the BLE. By switching the mode of the BLE module in each network node, it can communicate between nodes at the different layers of the tree topology.
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       TP03-72
Development of methods for receiving analytical data from different SQL/ NoSQL databases and leading to uniform format

Nurzhan Kakenovich Mukazhanov, Raissa Kabilevna Uskenbayeva, Abu Abdykadyrovich Kuandykov(International Information Technology University, Kazakhstan)

In this paper unified structure formation method (ETL) development for operative analysis of data stored in different SQL and NoSQL databases are considered. Unified structure formation method (ETL) is based on work with Map Reduce parallel model. In order to perform the method following steps should be considered: development of searching forms (pattern) for data searching in different database, transforming results of searching through Map Reduce parallel model, taking pre-aggreagtional data during transforming process and preparing data for downloading to data warehouse.
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       TP03-73
Nolinear Multi-component Spectroscopy Analysis Based on Evolutionary Construction Optimazation

Boyan Cai, Hui Cao, Yanbin Zhang, Lixin Jia, Gangquan Si(Xian Jiao Tong University, China), Zhongjian Li(Northwestern Polytechnical University, China)

Spectroscopy has been widely used to evaluate product quality or to predict components. To deal with the nonlinearity of spectral data, artificial neural networks (ANN) are widely used. One weakness of ANN is we have no accurate analytical method to design a optimal network structure. A multivariate component prediction method based on optimized neural network combined with evolutionary algorithm (EA) for spectral analysis is proposed in the paper. For the proposed method, ANNs are combined with nonlinear adaptive evolutionary programming algorithm (NAEP) to evolve ANN’s architecture including
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       TP03-74
Weak False Label Learning Model for Sensor Data Recognition

SUNG JUNE CHANG, HUN JOO LEE(ETRI, Korea, Republic of )

Real world behavior recognitions tend to suffer from incomplete data because sensors are not perfect. Although machine learning algorithms are successfully applied to recognitions, they do not work well in multi-valued output functions because true and false label in same input collide in learning process. In this paper, we propose a noble algorithm which lessens multi-valued function’s problem by weakening false labels. It also includes virtual samples and output normalization to compensate for the balance between true and false labels
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       TP03-75
Artificial Neural Network-Based Model for Quality Estimation of Refined Palm Oil

Nurul Sulaiha Sulaiman, Khairiyah Mohd Yusof(Universiti Teknologi Malaysia, Malaysia)

In this paper, we developed an accurate artificial neural network (ANN)-based model to predict significant quality of refined palm oil which is Free Fatty Acid (FFA) content. The variables; FFA content, Iodine Value (IV), moisture content, bleaching earth and citric acid dosage, pressure and temperature of the deodorizer were used to build the ANN prediction model. A feed forward neural network was designed using a back-propagation training algorithm and comparison of ANN predicted result with industrial data was made. It proved that ANN can be used to estimate the quality of refined oil.
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       TP03-76
Entropy Estimation of the Position of the Barrier Dimension: Applicability Nearsighted and Farsighted Iterative Algorithms for Processing High-Dimensional Data

Berik Akhmetov(Hoja Ahmet Yasaui International Kazakh-Turkish University, Kazakhstan), Alexander Ivanov(Penza Research Electrotechnical Institute, Russian Federation), Anis Gilmutdinov(A.N.Tupolev Kazan National Research Technical University, Russian Federation), Ivan Ognev(Moscow Power Engineering Institute (National Research University), Russian Federation), Kaiyrkhan Mukapil(Kazakh National Research Technical University named after K.I.Satpayev, Kazakhstan)

It is shown that large neural networks allow solving tasks that cannot classical quadratic forms in linear algebra. Thus the assessment of output entropy of neural network converters biometrics code is possible. The assessment of high-dimensional entropy is based on the symmetrization of the problem of the correlation of biometric data. Entropy of low dimension and high-dimensional entropy are differently connected with equally correlated data. For low-dimensional transformations only short-sighted algorithms, which not capable to bypass local extrema of quality are effective. The algorithms c
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