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

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

Linear Regression Analysis

This paper proposes a design methodology for dual-band Doherty power amplifier (DPA) with performance enhancement using dual-band phase offset lines. In the proposed architecture, 50-dual-band offset lines with arbitrary electric lengths at two frequencies are key components, and a novel analytical design solution has been proposed for their design and implementation. The methodology is validated with the design and fabrication of a 10-W GaN-based DPA for code division multiple access and Worldwide Interoperability for Microwave Access applications at 1960 and 3500 MHz, respectively. The dual-band Doherty amplifier using the proposed design methodology has better performance than the current state of the art. The peak drain efficiency of the amplifier is 59.5% at the first frequency and 49.6% at the second frequency of operation. Compared to balanced mode operation, there is an improvement of more than 10% in drain efficiencies, around 6.5-dB back-off, at both frequencies.

http://clarke06.businesscatalyst.com/pdf/Cree_Tech_Paper_GaN_Doherty_Dual_Band.pdf

Design Methodology for Dual-Band Doherty Power Amplifier With Performance Enhancement Using Dual-Band Offset Lines

This study presents a modified shunt-stub dual-band impedance transformer. The load impedances can be complex and unequal at two uncorrelated frequencies. The proposed transformer has two parts; each part is composed of a two-section transmission line. Similar to the single-stub matching network at a single frequency, a two-section transmission line is connected to the load and transforms the load impedances to normalized unit conductance at the two designated frequencies simultaneously. The resulting susceptances are then canceled out simultaneously by a two-section shunt stub at the two frequencies. The required nonlinear simultaneous equations are derived and solved to obtain the physical parameters of transmission lines. Rearranging the simultaneous equations to construct an objective function with only one unknown variable makes it easier to solve the equations. This approach avoids high-impedance transmission lines by choosing the characteristic impedance of each transmission line first. Return losses for various load impedances are simulated to validate the proposed structures. Good agreements between numerical and measured results validate the proposed structure.

Dual-Band Impedance Transformer Using Two-Section Shunt Stubs

In this letter, we propose a practical three-section dual-band transformer, which can terminate frequency-dependent complex load impedance at two arbitrary bands simultaneously. Analytical equations are derived to achieve the exact closed-form solutions. Numerical examples are examined to verify the validity. This three-section transformer can be utilized to match the complex load impedance with unequal values at two different frequencies, such as microwave amplifiers based on transistors, mixers, various kinds of antennas, and so forth.

A Three-Section Dual-Band Transformer for Frequency-Dependent Complex Load Impedance

Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging Therefore, building an effective charging planning system has become a crucial task to reduce the total charging time for EVs In this paper, we first introduce EVs charging scheduling problem and prove the NP-hardness of the problem Then, we formalize the scheduling problem of EV charging as a Markov Decision Process and propose deep reinforcement learning algorithms to address it The objective of the proposed algorithms is to minimize the total charging time of EVs and maximal reduction in the origin-destination distance Finally, we experiment on real-world data and compare with two baseline algorithms to demonstrate the effectiveness of our approach It shows that the proposed algorithms can significantly reduce the charging time of EVs compared to EST and NNCR algorithms

Effective Charging Planning Based on Deep Reinforcement Learning for Electric Vehicles

Provision of multimedia contents over the Internet of things (IoT) presents significant challenges to wireless networks owing to nodes diversity. Therefore, efficient utilization of resources is required to meet the growing diversity in user's behavior and wireless services that mark the future of wireless communication. Automatic classification of wireless signals based on their modulation schemes is a crucial technology that enables wireless transceivers to utilize the resources efficiently. Traditional feature-based approaches lack generalization, and versatility by relying on single classifier predictions. In this paper, two adaptive boosting (Adaboost) based wireless signal classifiers called SigmaBoost and KNNAdaboost are proposed. Adaboost generates an optimal prediction rule by combining the prediction of many weak component classifiers (CCs). However, sometimes it overfits on real-world scenarios with noisy data. That makes the choice of CC vital for its success in classifying wireless signals. Therefore, two well-known classifiers support vector machine (SVM) and k-nearest neighbor (KNN) are used as CCs in proposed schemes respectively. In SigmaBoost an adaptive decrementing mechanism is introduced, which decreases the value of Gaussian radial function (RBF) of SVM kernel as boosting progresses. It not only ensures the generation of weak RBFSVM component classifiers but also improves diversity across the ensemble. Signal spectral features and higher-order cumulants are used as input features. The experimental results demonstrate significant gain in the performance of SigmaBoost, as compared to KNNAdaboost and other single classifier-based approaches. Hence, SigmaBoost can be used in multimedia-enabled IoT for quick discrimination of wireless signals to ensure better radio spectrum management.

/pdf/automatic-wireless-signal-classification-in-multimedia-41h0655178.pdf

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Latency-energy optimization for joint WiFi and cellular offloading in mobile edge computing networks

In the Internet of Vehicles (IoV) scenarios, vehicles are equipped with computing resources to support vehicle-oriented IoV applications. Meanwhile, these computing resources can be also leveraged to enhance the task processing performance for the devices in Multi-access Edge Computing (MEC) scenarios, and alleviate the load of edge servers. Existing research works rarely consider the resource utilization of moving vehicles, which can be an important complement to the MEC schemes with the assistance of parked vehicles. In this paper, a joint task offloading and resource allocation scheme is proposed for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices, parked vehicles, and moving vehicles covered by a Base Station (BS) equipped with an edge server. The tasks of the devices can be either offloaded to the BS or further offloaded from the BS to the vehicles. The service time that a moving vehicle can provide its task offloading service before it moves out of the coverage of the BS, are taken into account of our system model. The aim of our scheme it to minimize the total priority-weighted task processing delay for all the devices through offloading the tasks to the edge server or to the vehicles, allocating the wireless channels of the BS, and allocating the computing resource of the edge server and the vehicles. A generalized benders decomposition and reformulation linearization-based iterative algorithm is designed to obtain the optimal solution to the optimization problem, and a two-stage heuristic algorithm is also given to provide near-optimal solutions with low computational complexity. The simulation results demonstrate the superiority of our scheme in seven different scenarios by comparing it with three other schemes.

Fan Wu

Papers

A Three-Section Dual-Band Transformer for Frequency-Dependent Complex Load Impedance

Effective Charging Planning Based on Deep Reinforcement Learning for Electric Vehicles

Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Latency-energy optimization for joint WiFi and cellular offloading in mobile edge computing networks

Joint Task Offloading and Resource Allocation for Multi-Access Edge Computing Assisted by Parked and Moving Vehicles