Specialized Book: Proposal for Anomaly Detection Techniques and Applications Focused on Machine Learning
Based on a wealth of examples, grasp the key points for practical application of anomaly detection. Explain approaches to challenges that arise in many fields at the implementation and operational levels.
- Starting with the basics of machine learning as an introduction, each method of anomaly detection that can address various issues will be explained. - The steps for building an anomaly detection model (constructing the learning model, defining anomaly levels, setting thresholds) and the flow from data measurement to model implementation will be explained using examples of state anomaly detection in manufacturing. - Practical techniques aimed at those seeking to put their ideas into practice will be explained. ⇒ Introducing methods for improving accuracy, such as defining anomalies, approaches when data is scarce, and evaluation methods for the system. - How to establish an internal environment to introduce and utilize AI effectively. ⇒ Approaches to problem-solving during system operation, collaboration with the field, and securing talent, as seen in the initiatives of overseas companies. - Approaches to anomaly detection and case studies for implementation and utilization across various fields and types of data [total of 13 items]. - What should be prepared? ⇒ Consideration of cost-effectiveness, data sensing methods, the amount and type of necessary learning data, etc. - Examples of acquired data and analysis ⇒ Preprocessing, data augmentation, time series data analysis, feature extraction, evaluation methods, etc. - Application to the field and operational improvements ⇒ System configuration and design points, additional learning, challenges during the introduction of anomaly detection, etc.
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Publication Date: November 26, 2019 Price: 52,000 yen + tax Format: B5 size, 230 pages ISBN: 978-4-86502-179-0 ● Toma Sogabe (University of Electro-Communications) ● Kenichi Fukui (Osaka University) ● Kazunari Okada (National Instruments Japan, Inc.) ● Saori Hori (DATUM STUDIO, Inc.) ● Takaya Asakura (DATUM STUDIO, Inc.) ● Kunio Yamamoto (MCS Research Institute) ● Haruko Mitake (MTK Research Institute) ● Ken Okawa (Uniadex, Inc.) ● Takeshi Shiina (Nihon Unisys, Inc.) ● Shunji Maeda (Hiroshima Institute of Technology) ● Chun-Feng Fan (Oki Electric Industry Co., Ltd.) ● Kenichi Shinoda (Toppan Printing Co., Ltd.) ● Masanari Kimura (Ridge-i, Inc.) ● Tatsuo Kamiya (Fukuchiyama Public University) ● Ryosuke Kasahara (Ricoh Company, Ltd.) ● Ikuma Fukui (MOSHIMO Lab) ● Minoru Kondo (Railway Technical Research Institute) ● Yuki Kusutani (Tokyo University of Agriculture and Technology Graduate School) ● Junichi Shirakashi (Tokyo University of Agriculture and Technology Graduate School) ● Kunihiko Saito (Shiga University) ● Ken Matsuda (Nagasaki Prefectural University)
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Applications/Examples of results
Chapter 1: Introduction - Fundamentals of Machine Learning and Statistical Methods in Data Analysis Chapter 2: Anomaly Detection Methods Using Machine Learning Chapter 3: Selection of Data Analysis Methods According to Objectives Chapter 4: Process of Building Anomaly Detection Systems Using Machine Learning - From Data Acquisition to Handling and Implementation Chapter 5: Learning Methods in Anomaly Detection and Techniques for Improving Accuracy and Operational Improvement Proposals Chapter 6: Internal Environment Development for Implementation and Utilization in Business Chapter 7: Proposals and Examples of Implementation and Utilization for Solving Field Issues
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The information organization aims to contribute to the development of the industry through technical seminars on chemistry, pharmaceuticals, electronics, machinery, environment, cosmetics, food, etc., as well as the publication of technical books, correspondence courses, and DVDs that record seminars.
