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

A Novel SDN-based Cross Handoff Scheme in Industrial Mobile Networks

Internet is getting more mobile and the service environment for users is getting more ubiquitous due to rapid development in the wireless network technology. Besides, as for MNN (Mobile Network Node: like vehicle, train, airplane, ship, etc.), telecommunication devices which intend to receive various and high quality Internet services by accessing to Internet not only from fixed places but also under high speed movement are in a trend of getting increased. This paper reinforced security under the network evaluation of wire/wireless integration of PMIPv6-based NEMO. Moreover, fast handoff technique was also adopted to reduce handoff delay time in PMIPv6 and X-FPMIPv6 (eXtension of Fast Handoff for PMIPv6) was used to support global mobility. In addition, AX-FPMIPv6 (Authentication eXtension of Fast Handoff for PMIPv6) is proposed which integrated SK-L2AM and X-FPMIPv6 by applying Piggybacks method to reduce the overhead of authentication and signaling. Our proposed scheme shows better performance than the existing schemes in authentication and handoff delay.

Security-effective fast authentication scheme for PMIPv6-based NEMO with global mobility support

Mobile users (MUs) want to be provided with undisrupted network services when they navigate on the Next-Generation wireless networks (NGWNs). For that, interlocking with a heterogeneous network is important, but there have been few studies on the method for guaranteeing global mobility. Thus, this paper proposes the Proxy-LMA technique, the mobile IP-based global inter-networking system, to enhance global mobility and interoperability within the NGWN. The purpose of the proposed Proxy-LMA system is to expand the boundary of the mobility with regards to the existing mobility management protocol (PMIPv6 and MIPv6) in order to guarantee global mobility and interoperability within the heterogeneous network environment. The results of the performance evaluation showed that the proposed Proxy-LMA system was more efficient than other methods from the standpoint of signaling cost and delay in the heterogeneous network environment.

Cost-Effective Proxy-LMA Mobility Management Scheme in Mobile Networks with Global Mobility Support

Project Management for the Productivity Improvement of Small and Medium-sized Enterprises (SMEs): Industrial Machinery and Equipment Manufacturing Enterprises

Jongpil Jeong

Papers

A Novel SDN-based Cross Handoff Scheme in Industrial Mobile Networks

Security-effective fast authentication scheme for PMIPv6-based NEMO with global mobility support

Cost-Effective Proxy-LMA Mobility Management Scheme in Mobile Networks with Global Mobility Support

Project Management for the Productivity Improvement of Small and Medium-sized Enterprises (SMEs): Industrial Machinery and Equipment Manufacturing Enterprises

On PMIPv6-based Cost-Effective Function-distributed Mobility Management Scheme for Industrial Future Internet.