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

The work proposes novel multiband and of low profile antenna arrays with hybrid type elements, for the realization of a MIMO (Multiple Input–Multiple Output) wireless communication system with great capacity Each element is composed of a linear dipole integrated with a non-uniform Electromagnetic Band Gap (EBG) lattice This EBG type, influences the system by two ways, the strong reduction of the Mutual Coupling (MC) among the closely positioned array's elements and the high radiation gain values of each element, inside wide space areas Both of them make the antenna element capable of enhancing the MIMO channel capacity It was ascertained by the results which were received taking into account globally, the MC and the radiation patterns of the antenna elements The Spatial Channel Model (SCM) scenario for Urban Micro and Urban Macro environments was applied and the capacity calculation was made in one step which incorporates both the channel and the antenna characteristics Results for the capacity at all three bands of operation are presented, for 10 dB SNR level, along with respective ones received from dipole arrays in front of Perfect Electric Conductor (PEC) The comparison shows clearly the superiority of the proposed hybrid type array and its robust performance in wireless propagation scenarios

Contribution of non-uniform EBG antenna arrays to the enhancement of MIMO channel capacity

In this paper, new planar spiral antennas with meander lines and loads for passive Radiofrequency identification tag application at ultra-high-frequency band are designed and optimized using the global best (gbest)-guided Artificial Bee Colony (GABC) algorithm. The GABC is an improved Artificial Bee Colony algorithm, which includes gbest solution information into the search equation to improve the exploitation. The optimization goals are antenna size minimization, gain maximization, and conjugate matching. The antenna dimensions were optimized and evaluated in conjunction with commercial software FEKO. GABC is compared with other popular algorithms. The optimization results produced show that GABC is a powerful optimization algorithm that can be efficiently applied to tag antenna design problems.

Application of Gbest-guided artificial bee colony algorithm to passive UHF RFID tag design

Design process and respective results for the synthesis of specific Radiofrequency Identification (RFID) tag antennas, suitable for dielectric and metallic objects, are presented. The antennas were designed for the UHF (865 MHz-869 MHz) band and their basic configuration is that of the printed spiral type. Six modification steps to the classical spiral layout are proposed and it was proved that they can lead to tags with high readability and reading distances up to 10 m when designed for dielectric object and up to 7 m in the case of metallic objects. The results of the measurements of the fabricated tags are explained via theoretical evaluations which take into account reflection phenomena, which are present in a real environment at which the tags are used.

/pdf/passive-uhf-rfid-tags-with-specific-printed-antennas-for-1awtxzo4yg.pdf

Passive UHF RFID Tags with Specific Printed Antennas for Dielectric and Metallic Objects Applications

In the article, a dual and wide band antenna array suitable for RF rectenna applications was synthesized and global rectenna systems are presented. The array consists of two bowtie-shaped patches, printed on the one side of a dielectric slab (FR4). On the other side of the slab, an aperture-textured metallic ground layer, is printed. Examples of full-wave rectifiers connected, through matching networks, to the antenna elements and forming integrated rectenna systems for radio-frequency (RF) power harvesting at 868 MHz, from 920 to 960 MHz and at 1.8 GHz, are presented. Statistical results over frequency and the directions of arrival (DoAs) of incoming waves were received showing, at the rectifier, mean direct current (DC) voltage of 580 mV, and mean power of 58 μW, for circularly polarized waves of field intensity of 1.8 V/m. The DC voltage can reach 800 mV, the power 120 μW and the efficiency 68% when the waves come from DoAs of maximum antenna’s gain. Due to the wideband performance of the antenna, it could be used at various frequency slots as long as the matching network’s operation frequency is changed. Thus the proposed rectennas could be suitable for energizing low-power sensors or at least to charge their batteries.

https://www.mdpi.com/2079-9292/8/10/1108/pdf

Synthesis of Rectenna for Powering Micro-Watt Sensors by Harvesting Ambient RF Signals’ Power

The power gain of arrays of wire antennas near an elliptic cylinder is maximized. The hybrid MM-GTD technique is used where the method of moments (MM) and the geometrical theory of diffraction (GTD) are formally combined. Results are presented for some special cases.

Katherine Siakavara

Papers

Contribution of non-uniform EBG antenna arrays to the enhancement of MIMO channel capacity

Application of Gbest-guided artificial bee colony algorithm to passive UHF RFID tag design

Passive UHF RFID Tags with Specific Printed Antennas for Dielectric and Metallic Objects Applications

Synthesis of Rectenna for Powering Micro-Watt Sensors by Harvesting Ambient RF Signals’ Power

A hybrid MM-GTD technique for the optimization of the power gain of arrays of wire antennas near an elliptic cylinder