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

Dynamic spectrum access and cognitive radio is a viable solution to solve congestion in ISM band. The dynamic environment of multi-channel wireless LAN is modeled by using continuous time Markov process. Bayes theorem is applied to infer channel access decisions dynamically to ensure current data transmission is switched to only likely candidate channels.

http://networking.khu.ac.kr/layouts/net/publications/data/2012DomesticConference17.pdf

Likelihood Estimation Using Continuous-Time Markov Channels for Cognitive Radio Networks in Wireless LAN

The present invention relates to a sensor network, and more particularly, to an implanted sensor network in a human body sensor network in which a plurality of sensor nodes are implanted in the human body to detect biosignals of the human body, wherein the implanted sensor network can prevent the temperature of a sensor node implanted in a human body from rising and thus protect the human body from injury caused thereby.

Implanted human body sensor network

In this paper, we have proposed Whizan M2M Big Data learning engine that enables service providers to monetize by estimating choices-of-interest(usage-contexts, for example: emotion, weather, location, etc.) from observed incentive filters(real-time feedback, for example)

Whiz- M2M Big Data Learning Engine for Autonomic Happy Internet Management

Security requirements are very pressing in distributed sensor networks due to exploitation purposes of these networks in human life, especially in military tasks. To obtain security in these sorts of networks, it is crucial to enable message encryption and authentication features among sensor nodes. This thing could be performed using keys agreed upon by communicating nodes. Nonetheless, acquiring such key agreement in distributed sensor networks becomes extremely intricate due to resource constraints. Up to now, there are many key agreement schemes proposed wired and wireless networks of which key predistribution schemes are considered to be the fittest solutions. Based on this observation, in this paper, we propose a key predistribution scheme relying on sensor nodes' unique identifiers. Our scheme exhibits several noteworthy properties: direct pairwise key establishment permission with explicit key authentication, high resiliency against information-theoretic security attack (node capture attack). We also present a detailed security and performance analysis of our scheme in terms of node capture attack, memory usage, communication overhead, and computational overhead.

An efficient ID-based bilinear key predistribution scheme for distributed sensor networks

Nowadays, unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of subtasks, and each subtask has dependencies upon other subtasks. In the real world, subtasks with dependencies must wait for their preceding subtasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two subproblems: 1) offloading decision problem and 2) communication resource allocation problem. Then, a metaheuristic method is proposed to find the suboptimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.

Choong Seon Hong

Papers

Likelihood Estimation Using Continuous-Time Markov Channels for Cognitive Radio Networks in Wireless LAN

Implanted human body sensor network

Whiz- M2M Big Data Learning Engine for Autonomic Happy Internet Management

An efficient ID-based bilinear key predistribution scheme for distributed sensor networks

Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAV Networks: A Metaheuristic Approach