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by 吳俊逸 2021-02-10 12:59:42, Reply(0), Views(29)
Ref: https://www.appliedmaterials.com/ja/nanochip/nanochip-fab-solutions/july-2018/leveraging-the-digital-twin-in-smart-microelectronics-manufacturingBy James MoyneAmong the many tenets of smart manufacturing,[1] “digital twin” solutions represent a significant opportunity for microelectronics manufacturers to leverage existing and emerging technologies to improve quality and throughput, and reduce variability and cost.
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by 吳俊逸 2021-02-10 12:47:44, Reply(0), Views(42)
Ref:http://franktsao.blogspot.com/2009/10/apc.html曹永誠 [刊登於電機月刊第174期 (2005年6月)]1、前言以一座八吋晶圓廠來說,投資額約在200億~300億,而12吋晶圓
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by 吳俊逸 2020-11-19 19:09:27, Reply(0), Views(152)
https://www.lexalytics.com/lexablog/text-analytics-nlp-rpa-use-casesThis article explains everything you need to know about text analytics and natural language processing in robotic process automation. First, we define RPA and natural language processing, and explain how they fit together. Then we outline a number of trending text analytics use cases in RPA. Finally, we cite Forrester and Gartner to put these use cases in perspective and explain how the RPA market is changing, and where it’s going. As we demonstrate, the future of RPA is in better analytics and customization with larger, transformational use cases. To stay ahead, RPA vendors must improve their NLP capabilities.
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by 吳俊逸 2020-10-21 01:47:15, Reply(0), Views(275)
原文網址:https://kknews.cc/tech/38qm5l8.html
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by 吳俊逸 2020-06-08 00:00:35, Reply(0), Views(380)
REF: https://machinelearningmastery.com/how-to-get-started-with-deep-learning-for-time-series-forecasting-7-day-mini-course/Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
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by 吳俊逸 2020-05-12 10:36:05, Reply(0), Views(290)
Ref: https://www.kdnuggets.com/2019/12/mathworks-predictive-maintenance-theory-practice.html?s_v1=30212&elqem=2962332_EM_WW_20-05_NEWSLETTER_CG-DIGEST-DEFAULTThese days you keep hearing about predictive maintenance and how valuable it is. It’s the utopia where equipment operators can anticipate impending malfunctions, preemptively schedule repairs, minimize disruption to factory operation, and, most importantly, protect equipment from catastrophic failures. With bright eyes, we can all agree on the value of predictive maintenance, or should I say the expected value of predictive maintenance?
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by 吳俊逸 2020-05-10 22:28:24, Reply(0), Views(257)
以下,將展示如何使用Orange3來顯示一組與所選參考圖像最相似的圖像。 Orange3將使用以下工作流程:
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by 吳俊逸 2020-05-07 12:19:07, Reply(0), Views(284)
Ref: https://thenewstack.io/when-holt-winters-is-better-than-machine-learning/Machine Learning (ML) gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time-series space. Error, Trend, Seasonality Forecast (ETS), Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters are three Classical methods that are not only incredibly popular but are also excellent time-series predictors.
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by 吳俊逸 2020-04-28 22:23:46, Reply(0), Views(216)
Q1: When we extract the data and found that there is an error in the data and we disposing the data, do we still count this data as one of the null sample or considering this as an outlier? or just ditch it completely?
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by 吳俊逸 2020-04-28 22:21:16, Reply(0), Views(307)
Q1: Could you please give me an example to consider that which model should be applied when we want to predict downtime of the machine, when we have only the data collected from sensors which installed on that machine?
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