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A dimension-reduction based multilayer perception method for supporting the medical decision making
(2020-03-01)
Lee, Shin-Jye
;
Tseng, Ching-Hsun
;
Lin, G. T-R
;
Yang, Yun
;
Yang, Po
Due to the rapid development of Medical IoT recently, how to effectively apply these huge amounts of IoT data to enhance the reliability of the clinical decision making has become an increasing issue in the medical field. These data usually comprise high-complicated features with tremendous volume, and it implies that the simple inference models may less powerful to be practiced. In deep learning, multilayer perceptron (MLP) is a kind of feed-forward artificial neural network, and it is one of the high-performance methods about stochastic scheme, fitness approximation, and regression analysis. To process these high uncertain data, the proposed work based on MLP structure in particular integrates the boosting scheme and dimension-reduction process. In this proposed work, the advanced ReLU-based activation function is used. Also, the weight initialization is applied to improve the stable prediction and convergence. After the improved dimension-reduction process is introduced, the proposed method can effectively learn the hidden information from the reformative data and the precise labels also can be recognized by stacking a small amount of neural network layers with paying few extra cost. The proposed work shows a possible path of embedding dimension reduction in deep learning structure with minor price. In addition to the prediction issue, the proposed method can also be applied to assess risk and forecast trend among different information systems. (C) 2019 Elsevier B.V. All rights reserved.
A Deep Learning-Based Strategy to the Energy Management-Advice for Time-of-Use Rate of Household Electricity Consumption
(2020-01-01)
Wu, Lu-Xian
;
Lee, Shin-Jye
;
科技管理研究所
;
Institute of Management of Technology
With the high industrialization at a rapid pace, the demand for energy increases exponentially, but it is difficult to meet the balance of demand and supply. Therefore, how to effectively meet the balance of demand and supply intelligently has become a popular issue in this century. In recent years, Taiwan Power Company (Taipower), the biggest electric company in Taiwan, is committed to the construction of Advanced Metering Infrastructure (AMI), which provides communication channels and enables demand-side users to participate in load dispatch. On the other hand, the construction of AMI is expected to generate a tremendous number of valuable data on electricity consumption, but it is not easy to convert these data into effective information by the conventional quantitative methods. In as much as the rapid progression of AI technology in the industrial field, the application of AI technology in the technology management has become an increasing issue as an interdisciplinary study. To address this task, this work applies the recurrent neural network based on deep learning to predict low-voltage usage shortly by the electricity information of low-voltage user and meteorological data. After many vicissitudes, the electricity consumption per hour can be predicted and a sound energy arrangement can be therefore planned. Through introducing the proposed model, Taipower Company will have an effective capability that schedules power, reduces unnecessary backup power, and provides time-consuming electricity prices for industrial enterprises accurately among high usage of Taiwan industries.

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