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

Differential Evolution: A review of more than two decades of research

A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an $N$ -dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.

The Wind Driven Optimization Technique and its Application in Electromagnetics

Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is often very low (eg, several weeks' optimization time), which limits the application of these methods for many industrial applications To address this problem, a new method, called surrogate model assisted differential evolution for antenna synthesis (SADEA), is presented in this paper The key ideas are: (1) A Gaussian Process (GP) surrogate model is constructed on-line to predict the performances of the candidate designs, saving a lot of computationally expensive EM simulations (2) A novel surrogate model-aware evolutionary search mechanism is proposed, directing effective global search even when a traditional high-quality surrogate model is not available Three complex antennas and two mathematical benchmark problems are selected as examples Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization

/pdf/an-efficient-method-for-antenna-design-optimization-based-on-3adinriwkc.pdf

An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques

A simple numerical technique for the solution of the discontinuity problem of a symmetric loaded rectangular dielectric post centered in a rectangular waveguide is presented. The waveguide is divided into three regions where the field is expressed in suitable waveguide modes. By applying the continuity condition at the common surfaces of the regions, a system of linear equations determining the reflection and transmission coefficients is formed. Several examples are compared with experimental results and show the validity of the method. >

The discontinuity problem of a rectangular dielectric post in a rectangular waveguide

A new Direction of Arrival (DoA) estimation method based on Neural Networks (NNs) is presented. The proposed NN-DoA procedure is especially designed for a Switched-Beam System (SBS), whose basic component is an 8 £ 8 Butler Matrix (BM). The technique is simple and appropriate for real time applications. Simulations of DoA estimation tests show accurate results even for a big set of simultaneously incident signals.

https://piers.org/piersonline/pdf/Vol3No8Page1160to1164.pdf

Multiple Signal Direction of Arrival (DoA) Estimation for a Switched-Beam System Using Neural Networks

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.

Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

A novel microstrip antenna is synthesized by the square-curve fractal algorithm. The generator element is a rectangular microstrip ring and the antenna realizes multiband operation. This performance can also be achieved by using other fractal algorithms found in the literature. However, in all cases the frequency bandwidth is very narrow. In the present work, bandwidths up to 12% are achieved by the use of double feed and one pin. The antenna's additional figure of merit is its ability to be driven only by probes and without the need of any matching circuit. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 180–185, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20086

Katherine Siakavara

Papers

The discontinuity problem of a rectangular dielectric post in a rectangular waveguide

Multiple Signal Direction of Arrival (DoA) Estimation for a Switched-Beam System Using Neural Networks

Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making

A multi-wideband microstrip antenna designed by the square-curve fractal technique

Neural Network --- Based Design of EBG Surfaces for Effective Polarization Diversity of Wireless Communications Antenna Systems