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

The significant growth in data consumption among mobile users necessitates the development of new architecture to meet the increasing demand. On the other hand, reconfigurable intelligent surface (RIS) has grown in popularity in 6G due to its improved spectral efficiency, simplicity of deployment, and low cost. However, with the constrained limitation of the coverage by conventional RIS, the research direction has turned towards simultaneously transmitting and reflecting RIS (STAR-RIS) to provide 360° coverage alongside the benefits of RIS. In this paper, a STAR-RIS-assisted downlink communication system for both indoor and outdoor users is investigated. Then, the optimization problem to maximize the spectral efficiency while jointly controlling the beamforming power for each user and phase shift values of the STAR-RIS is formulated. Since the formulated problem is NP-hard and challenging to solve in polynomial time, a policy gradient method for reinforcement learning named proximal policy optimization (PPO) is implemented to solve the problem. To demonstrate the effectiveness of our proposed algorithm, extensive simulation results are executed. Numerical results prove that our proposed algorithm outperforms several benchmark schemes in the literature.

Deep Reinforcement Learning based Spectral Efficiency Maximization in STAR-RIS-Assisted Indoor Outdoor Communication

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users. 

https://ieeexplore.ieee.org/ielx7/4275028/5699970/10172231.pdf

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

In these modern days, video streaming is the major portion of Internet traffic. To cope with the increasing demand Information Centric Networking (ICN) architecture has been proposed. Layered video, encoded with Scalable Video Coding (SVC), are envisioned to be very beneficial in CCN. In this paper we propose a mechanism for video quality selection that opportunistically request additional enhancement layers of SVC to fully utilize advantages of CCN routers’ content caching. We enable the routers to take decision of discard the forwarding of outdated optional enhancement layers of the video in order to reduce unnecessary traffic inside the Network.

http://networking.khu.ac.kr/layouts/net/publications/data/KCC2015-11.pdf

Opportunistic Quality Adaptation for Scalable Video Streaming in Information Centric Networks

GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network

Cloud based healthcare services are the forthcoming patient-care amenities of autonomic and persuasive healthcare, where cloud broker acts as an intermediary between cloud service providers and healthcare service providers. Among the three type of cloud services i.e. infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS); the compute-as-a-service (CaaS) of IaaS services are more costly than other cloud services necessary for patient-care. This paper proposes an optimized solution of selection of CaaS providers based upon the online offered prices for their per hour CPU usage. The produced optimized solution is compared with the naive certainty equivalent approach. The optimization formulation is horizontally scalable to select costeffective IaaS and SaaS cloud services for healthcare provisioning.

http://networking.khu.ac.kr/layouts/net/publications/data/2013DomesticConference40.pdf

Choong Seon Hong

Papers

Deep Reinforcement Learning based Spectral Efficiency Maximization in STAR-RIS-Assisted Indoor Outdoor Communication

Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach

Opportunistic Quality Adaptation for Scalable Video Streaming in Information Centric Networks

GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network

Cost-optimized Selection of CaaS Provider in Cloud Brokering Architecture for Cloud Based Healthcare Services