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

Influence maximization is to find a small seed set of most influence people in a domain. With the increased popularity of social network, influence maximization in the social network has become a potential problem and the result has been begun to be applied in various applications like viral marketing, domain expert search, recommending network etc. The basic influence maximization problem is NP-hard and many researchers has developed different approximation algorithms. In this research we have proposed an algorithm named Graph Reduced Technique (GRT) which estimates influence based on heuristics. The algorithm offers better running time and with feasible amount of spread of influence.

http://networking.khu.ac.kr/layouts/net/publications/data/KIISE%202015/KIISE27.pdf

Running Time Improvement of Influence Maximization in Social Network Using Graph Reducing Technique (GRT)

In this paper, we present an idea of cooperative communication which can enhance the transmission opportunities for cognitive radio ad hoc networks. In this mechanism, primary and secondary users cooperate with each other for their own good. By cooperating with secondary users, primary users can save energy and transmission time without degrading the throughput. Secondary users can achieve more opportunities to access the channel by cooperating with primary users. It is a win-win situation for both primary and secondary users.

http://networking.khu.ac.kr/layouts/net/publications/data/2013DomesticConference27.pdf

Cooperative Spectrum Sharing Among Primary and Secondary Users in Cognitive Radio Networks

Visible light communication is a promising technique in future networks due to its advantages of high data rate and licensed-free spectrum. In addition, non-orthogonal multiple access (NOMA) is considered as a candidate of multiple access schemes in 5G networks and beyond. In this paper, we study the power control problem in NOMA-based visible light communication. An optimization problem is formulated, where the objective is the harmonic-rate utility function. The optimal solution is achieved optimally by equivalently transforming the underlying problem into a convex problem. Simulation result is provided to validate performance of the proposed approach.

Power Control for Harmonic Utility in Non-Orthogonal Multiple Access based Visible Light Communications.

Wearable device communication is a major research field in Internet of Things (IoT) domain. An autonomous network of wearable devices has both medical and non-medical applications. Energy efficiency is among the most crucial aspects of a wireless wearable network. By managing the wakeup schedule of the devices, we can reduce the energy consumption and delay. In this work, we present an out-of-band wakeup scheme using the contention access mechanism. We used a superframe based scheme for the media access control by utilizing time slots for multiple access. We present analysis and simulation study of the out-of-band wakeup with contention access and compared it with the existing IEEE802.15.4 MAC protocol.

An Out-of-band Wakeup using Contention Access for Wearable Device Communication

Wireless sensor networks have taken immense interest in healthcare systems in recent years. One example of it is in an in vivo sensor that is deployed in critical and sensitive healthcare applications like artificial retina, cardiac pacemaker, drug delivery, blood pressure, internal heat calculation, glucosemonitoring etc. In vivo sensor nodes exhibit temperature that may be very dangerous for human tissues. However, existing in vivo thermal aware routing approaches suffer from hotspot creation, delay, and computational complexity. These limitations motivate us toward an in vivo virtual backbone ,a small subset of nodes, connected to all other nodes and involved in routing of all nodes, -based solution. A virtual backbone is lightweight and its fault-tolerant version allows in vivo sensor nodes to disconnect hotspot paths and to use alternative paths. We have formulated the problem as m-connected k-dominating set problem with minimum temperature cost in in vivo sensor network. This is a combinatorial optimization problem and we have been motivated to use evolutionary approach to solve the problem.

Choong Seon Hong

Papers

Running Time Improvement of Influence Maximization in Social Network Using Graph Reducing Technique (GRT)

Cooperative Spectrum Sharing Among Primary and Secondary Users in Cognitive Radio Networks

Power Control for Harmonic Utility in Non-Orthogonal Multiple Access based Visible Light Communications.

An Out-of-band Wakeup using Contention Access for Wearable Device Communication

Towards Evolutionary Approach for Thermal Aware In Vivo Sensor Networks