There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

IEEE 80211 provides multiple channels for wireless communications at the physical layer, however the Medium Access Control (MAC) protocol is designed only for single channel Moreover, the Carrier Sense Multiple Access (CSMA)-based MAC protocol in IEEE 80211 suffers from the starvation problem By exploiting the multiple channels and employing the power control algorithm to increase the spatial reuse, we can improve the network performance Motivated by this, herein we present multichannel MAC protocols and power control algorithms proposed for wireless ad hoc networks


Keywords:

ad hoc network;
multichannel MAC;
power control;
SINR

Multi‐channel MAC Protocols and Power Control for Wireless Ad hoc Networks

Recently, Internet Default Free Zone (DFZ) is facing a scalability problem due to the double use of the current Internet Protocol (IP) namespace. IP namespace is used for both the location finding and host identification. However, scalability is not the only problem, mobility is difficult to achieve due to the dual use of IP. Therefore, Identifier and Locator separation is proposed and discussed in the research community as a way to solve the above problems. But, this solution further creates a big challenging issue in designing a mapping system to support efficient mobility of users while providing scalability. In this paper, we propose a hierarchical mapping system based on today's IP allocation/assignment. We evaluate our mapping system and show that scalability, and short lookup time can be achieved. However, to maintain scalability in the case of mobility is a challenge. Therefore, we provide a smooth location management scheme for mobility with a low signaling cost compared to the approach based on distributed hash table. Signals due to node movement are kept locally in each edge network.

http://networking.khu.ac.kr/layouts/net/publications/data/2011InternationalConference07.pdf

HMS: towards hierarchical mapping system for ID/locator separation

Wireless Sensor Network typically assimilates various real time applications that must meet some QoS requirements (e.g. delay, jitter, throughput, packet loss) under severe resource limitations. Hence, quality-of-service (QoS) management in Wireless Sensor Network is an important issue yet to be investigated. Due to the high data rate and burst traffic for real time applications, occurrence of congestion is very common. Ensuring the QoS requirements under congested scenario is quite challenging. As an attempt to this direction, in this paper we propose a hop-by-hop dynamic rate control algorithm which controls the congestion as well as ensures that the real time traffic will meet the soft QoS requirements. We have introduced per-hop deadline miss ratio as the congestion indication metric and performed the rate control when this ratio goes above to a certain level. Thus by per-hop rate adjustment, soft QoS is being met locally which in turns meets the end-to-end soft QoS. Finally, simulation has demonstrated the effectiveness of our approach.

A Hop by Hop Rate Control Based QoS Management for Real Time Traffic in Wireless Sensor Networks

This paper presents a novel MAC protocol that allows the secondary links to optimally adjust their transmission power and persistence probability on an ingress rate basis regulated by cognitive users (CUs) for multi-hop cognitive radio ad hoc networks (CRAHNs). By introducing a unique collision probability constraint required by spectrum holder, we show that the CUs can opportunistically access the unused spectrum vacated by licensed users (LUs) to globally maximize the net revenue in a de-centralized manner. Finally, our illustrative results verify the ef� ciency of the proposed protocol. Index Terms—Power control MAC, cross-layer optimization, multi-hop CRAHNs, persistence probability.

http://networking.khu.ac.kr/layouts/net/publications/data/2013DomesticConference04.pdf

Optimal Power Control MAC for Multi-hop Cognitive Radio Ad-hoc Networks

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.

/pdf/re-anonymization-technique-for-dynamic-data-using-decision-3w0apr3bvy.pdf

Choong Seon Hong

Papers

Multi‐channel MAC Protocols and Power Control for Wireless Ad hoc Networks

HMS: towards hierarchical mapping system for ID/locator separation

A Hop by Hop Rate Control Based QoS Management for Real Time Traffic in Wireless Sensor Networks

Optimal Power Control MAC for Multi-hop Cognitive Radio Ad-hoc Networks

Re-anonymization Technique for Dynamic Data Using Decision Tree Based Machine Learning