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

A serious problem to fight attacks through network is that attackers use incorrect or spoofed IP addresses in attack packets. Due to the stateless nature of the internet structure, it is a difficult problem to determine the source of these spoofed IP packets. While many IP traceback techniques have been proposed, they all have shortcomings that limit their usability in practice. In this paper we propose new IP marking techniques to solve the IP traceback problem. We have measured the performance of this mechanism and at the same time meeting the efficient marking for traceback and low system overhead.

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Lightweight IP Traceback Mechanism

Vehicle Ad Hoc Network (VANET) created between vehicles or between vehicles and centralized infrastructures is a dynamic network. Specifically, VANET can support many services such as safety, traffic management, and user-oriented services to improve transportation efficiency. Medium Access Control (MAC) protocol in VANETs not only responds the different requirements of these services but also improves the efficient broadcasting safety messages. One such protocol is to employ both TDMA and CSMA schemes in the Control CHannel (CCH), called a hybrid MAC protocol. A vehicle will send safety message without collision on the TDMA-based period during its reserved time slot and utilize the SCH resources on the CSMA-based period for the non-safety message. In addition, the TDMA-based period can be adjusted according to vehicle density. Hence, these systems can adapt themselves to various traffic conditions and improve the throughput for safety messages. In this survey, we compare different hybrid MAC protocols using dynamic TDMA-based periods as well as their benefits and limitations. Finally, we discuss some issues and promising future research which can be satisfied different QoS requirements and efficiently support both safety and non-safety services.

Adaptive TDMA and CSMA-Based MAC Protocols for Vehicular Ad Hoc Networks: A Survey

Internet of Planets (IoP) is a concept that enables solar planets to communicate with each other using the Internet. While there is a plethora of research on IoP, the delay-tolerant network (DTN) has emerged as the most advanced technology in recent years. DTN is an asynchronous networking technology that has been deployed for the networking environment in which steady communication paths are not available, and therefore, it stores receiving data in a data storage and forward them only when the communication links are established. DTN can be applied to sensor networks and the mobile ad-hoc network, as well as space communication that supports data transmissions among satellites. In DTN networking environments, it is crucial to secure a scheme that has relatively low routing overhead and high reliability to achieve efficiency. Thus, this article proposes a time (delay) information based DTN routing scheme, which is able to predict routing paths for achieving efficient data transmissions among the nodes that have comparatively periodic moving patterns. The results of the proposed DTN routing algorithm using NS-3 simulation tools indicate satisfied levels of routing performance in comparison with the existing DTN algorithm.

Internet of Planets (IoP): A New Era of the Internet

In this paper, a novel framework is proposed for joint subchannel assignment and power allocation in the uplink of cognitive femtocell network (CFN). In the studied model, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels in a macrocell network. The problem of optimal allocation of subchannels and transmit power is formulated as an optimization problem in which the goal is to maximize the overall uplink throughput while guaranteeing minimum rate requirement of the served FUEs and macrocell base station (MBS) protection. To solve this problem, a novel framework based on matching theory is proposed to model and analyze the competitive behaviors among the FUEs and FBSs. Using this framework, distributed algorithms are implemented to enable the CFN to make decisions on subchannel allocation and power control. The developed algorithms are then shown to converge to stable matchings. Simulation results show that the proposed approach yields a notable performance improvement, in terms of the overall network throughput and outage probability while requiring only a small number of iterations for convergence.

Matching-based distributed resource allocation in cognitive femtocell networks

As more and more data-driven applications are emerging, the demand for data rate are also increasing exponentially specially in the cellular network. Sometimes the Quality-of-service (QoS) are not guaranteed for all user equipments (UEs). To meet this demand, massive MIMO, LTE-A, cooperative communications are considered as the suitable candidate for the next generation cellular network. As most of the smart phones have multiple connectivity, we consider cooperative communication among UEs as a possible solution of this problem in our paper. We use matching theory to find suitable candidates to cooperate in the network. Simulation results are shown to represent the performance of the process.

Choong Seon Hong

Papers

Lightweight IP Traceback Mechanism

Adaptive TDMA and CSMA-Based MAC Protocols for Vehicular Ad Hoc Networks: A Survey

Internet of Planets (IoP): A New Era of the Internet

Matching-based distributed resource allocation in cognitive femtocell networks

Cooperative Communication for Content Distribution in Downlink Cellular Network