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    Item type:Publication,
    A Result Verification of Decision Tree Model for Industrial Wireless Sensors Selection using Analytic Hierarchy Process
    (2019-07-01)
    Meesawad, Saksiri
    ;
    Thanasopon, Bundit
    ;
    Wongwirat, Olarn
    Nowadays, an industrial wireless sensor (IWS) is interested and widely used in a Gas industry in Thailand. There are many vendors trying to improve quality of IWS products for gaining advantage in competitive market. Therefore, choosing the IWS becomes a challenge for users not only the brand name and price but also several factors needed to be considered, e.g., data rate, output power, operating voltage, transmitting current, receiving current, and operating temperature. Selecting the proper IWS is considered as a multi-objective decision problem that is complicated for an engineer and a project manager. The classification method using a Decision Tree)DT(model can be applied to solve such the problem, but the accuracy is depended on the number of historical data. For IWS in the Gas industry in Thailand, not only the number of training and testing data is limited but also there are only a few brands that are chosen regularly. In this paper, the method of applying the DT model for IWS classification and selection is presented. Then, the classification result of the DT model is verified by using an Analytic Hierarchy Process)AHP(for confirming whether it is accurate based on the limit number of historical data. The verification result can be preliminarily ensured that the DT model can be applied as a decision tool for choosing the appropriate IWS accurately.
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    Item type:Publication,
    Industrial wireless sensor selection method by using decision tree
    (2017-07-01)
    Meesawad, Saksiri
    ;
    Wongwirat, Olam
    Presently, an industrial wireless sensor (IWS) has been interested and widely used by various industries. There are many industries trying to adopt qualified IWS products in competitive market for gaining advantage. The challenge in selection IWS includes not only the price, but also several factors, e.g., data rate, output power, operating voltage, current transmitting, current receiving, operate temperature, brand and so on. These factors cause difficulty for making decision by engineers or project managers, since there are a number of factors to select simultaneously in order to find the optimal IWS to use. In this paper, we propose the method for IWS selection by using decision tree in data mining. The proposed method is suitable for the problems involving a multiple factors selection simultaneously, as in the IWS selection problem. The IWS factors used for attribute decision are defined in this work, as well as the model of training set preferences. The classification method and decision tree technique are also derived in the paper to acquire the result, including the cross validation check for the result obtained. The result expressed that the decision tree provides the correct decision to optimal classification. It is rationality and feasibility for selection the qualified IWS products.