Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Economic Control of Quality of Manufactured Product.

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

In this paper, we propose a new user authentication (UA) scheme based on one-time password (OTP) protocol using smart cards for home networks. The proposed scheme is to authenticate home users who uses home devices. Several techniques using technology based on biometrics, passwords, certificates, and smart cards can be used for user authentication in the similar environments. However, such user authentication techniques must be examined before being employed in an environment where home devices have low efficiency and performance. Here, we present the important security functions of home networks. The proposed authentication protocol is designed to accept the existing home networks based on the one-time password protocol. Also, it is a well suited solution and is quite satisfactory in terms of the security requirements of home networks, because of requiring low computation by performing simple operations using one-way hash functions. Our proposed scheme can protect against illegal access for home services and devices and does not allow unnecessary service access by legitimate users. Therefore, it allows the user to provide real-time privilege control and good implementation in secure home networks.

Integrated OTP-Based User Authentication Scheme Using Smart Cards in Home Networks

Time-Series Data Augmentation based on Interpolation

Components for Smart Autonomous Ship Architecture Based on Intelligent Information Technology

A Novel Architecture of Air Pollution Measurement Platform Using 5G and Blockchain for Industrial IoT Applications

This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development.

Jongpil Jeong

Papers

Integrated OTP-Based User Authentication Scheme Using Smart Cards in Home Networks

Time-Series Data Augmentation based on Interpolation

Components for Smart Autonomous Ship Architecture Based on Intelligent Information Technology

A Novel Architecture of Air Pollution Measurement Platform Using 5G and Blockchain for Industrial IoT Applications

Mobile Robot Path Optimization Technique Based on Reinforcement Learning Algorithm in Warehouse Environment