應用資料探勘技術於筆記型電腦GPS檢測實證研究-以A公司為例

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题名:	應用資料探勘技術於筆記型電腦GPS檢測實證研究-以A公司為例
作者:	杜怡葶;Tu, Yi-Ting
贡献者:	工業管理研究所在職專班
关键词:	全球衛星定位GPS系統;羅吉斯迴歸;類神經網路;決策樹;Global Positioning System;logistic;Neural Network;Decision tree
日期:	2022-01-14
上传时间:	2022-07-13 17:49:08 (UTC+8)
出版者:	國立中央大學
摘要:	筆記型電腦面對近年來市場需求快速的演進，其中全球衛星定位系統(Global Positioning System,簡稱:GPS)對定位效率具有關鍵性的影響，利用全球衛星定位GPS系統的應用十分的廣泛，例如個人穿戴裝置的定位系統，或是在交通運用上，例如導航定位以及地理測量等，十分重要。本研究以A公司實際客訴案件作為研究主題，目前個案公司GPS檢測站主要是仰賴人力來驗證良莠，但隨著筆記型電腦的應用越來越多越來越廣，需求大幅成長的環境下，舊有的模式不僅會使人力成本大幅提高也可能帶來檢查人力不足或是誤判的情況發生，因此為解決人力成本問題、降低不良品發生的機率同時提升顧客滿意度，必須要有一套具有效益且數據化的方法解決問題。故使用資料探勘技術於GPS檢測，但由於導致GPS效能不良原因之變數眾多，且均對其功能有相當重要的效能影響，利用資料探勘技術中羅吉斯迴歸、類神經網路以及決策樹建立模型，分析預測實際運用的可能性，作為公司未來發展重要策略之一，進而降低人力成本且提升業界競爭力及客戶滿意度。 ;Require for the orientation market in recent years, the personal notebook computers mainly focus on reducing noise and effectively signals to achieve better performance. Among them, the Global Positioning System (GPS) is a key impact factor on the overall positioning efficiency. The Global Positioning System has wide application, such as the personal wearable devices or transportation applications. , Such as navigation and positioning, automatic control, geographic measurement, etc., are very important. This research takes the actual customer complaint case of Company A as the research. At present, the GPS inspect station relies on human’s diagnosis. The application of notebook becomes more extensive, the demand is growing greatly. The old process will not only increase personnel costs, but may also lead to insufficient inspection or misjudgments. Therefore, the purpose of this research to apply data mining to have to solves this problem. This research will apply data mining technology to GPS detection. Because of the many variables that affect its function, such as antenna performance, system noise interference, and panel noise, all have a very important impact on its function. However, It is very difficult to find out the problem from the huge amount of data. Therefore, we use data mining technology to build models, including logistic regression, neural network and decision tree in study, as the company’s future development One of the important strategies and customer satisfaction.
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