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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Lin, Yi-BingSubject: search.filters.subject.Machine learning

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    Machine learning with parallel neural networks for analyzing and forecasting electricity demand
    (2020-08-01)
    Chen, Yi-Ting
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    Sun, Edward W.
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    Lin, Yi-Bing
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    資訊工程學系
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    Department of Computer Science
    Traditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods.
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    Implementing AI as Cyber IoT Devices: The House Valuation Example
    (2020-04-01)
    Lin, Yun-Wei
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    Lin, Yi-Bing
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    Liu, Chun-You
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    Lin, Jiun-Yi
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    Shih, Yu-Lin
    Internet of Things (IoT) has been widely utilized with artificial intelligence (AI). However, it requires substantial effort to integrate AI and big data with IoT. To mitigate this problem, AItalk was proposed. By treating AI as a cyber IoT device, we do not need to write code of the AI mechanism in the network applications, as traditional AI-based IoT applications did. This article describes how the AI tools such as scikit-learn and TensorLayer are accommodated as cyber IoT devices in AItalk, and extends AItalk for non-IoT applications. We use house valuation as an example to show how AItalk can flexibly include the factors that have significant impact on the house price. We show that by adding extra features other than housing profile features, the accuracy for the prediction (valuation) can be improved by 38 & x0025;. We also investigated the communication overhead of the distributed AItalk structure, which is 3.7 & x0025; for the computation of one house price valuation.
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    Network security management with traffic pattern clustering
    (2014-09-01)
    Chiou, Tao-Wei
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    Tsai, Shi-Chun
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    Lin, Yi-Bing
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    資訊工程學系
    ;
    Department of Computer Science
    Profiling network traffic pattern is an important approach for tackling network security problem. Based on campus network infrastructure, we propose a new method to identify randomly generated domain names and pinpoint the potential victim groups. We characterize normal domain names with the so called popular 2gram (2 consecutive characters in a word) to distinguish between active and nonexistent domain names. We also track the destination IPs of sources IPs and analyze their similarity of connection pattern to uncover potential anomalous group network behaviors. We apply the Hadoop technique to deal with the big data of network traffic and classify the clients as victims or not with the spectral clustering method.