Application and Challenges of Machine Learning-Assisted Antenna Design

Machine learning-assisted antenna design has attracted a lot of attention due to the advancement of machine learning techniques. Despite its success, there are many challenges that need to be solved. Firstly, the quality of training data has an important effect on the accuracy and efficiency of the machine learning model. Research on high-quality training data generation is very necessary. Secondly, most existing machine learning methods rely on human experts to collect data, which is a time-consuming and tedious process. Studying how to automate antenna design is another challenge. Finally, while reinforcement learning (RL)-based antenna design can achieve fully automated design, their learning performance can be poor if they do not have sufficient prior knowledge, leading to slow convergence or even divergence. Determining how to improve learning efficiency is meaningful. This paper investigates the above three problems, illustrated by using three examples. For the first problem, we use domain knowledge stemming from the understanding of EM problems to guide the generation of training dataset so that the generated dataset are more relevant to the design goals, which reduces the solution space and improves model training efficiency. For the second problem, we use an RL algorithm to fully automate the antenna design. Unlike supervised learning training, which requires the use of human-supplied data, RL allows agents to learn from their own experience gained from interaction with the environment. The entire process does not require human involvement, relieving designers' burden. For the third problem, we take advantage of both imitation learning (IL) and RL. IL allows the agent to imitate the behavior of a human expert, and then RL autonomously explores the environment to boost generalization performance. This combination of IL and RL exhibits better performance compared to pure RL or IL. In addition, we also propose some future directions for machine learning-based antenna design. The machine learning-aided design approach is showing great potential in solving complex antenna design problems, which may become an indispensable tool for humans to design antennas in the future

A Machine Learning Framework for the Design of STCDME Structures in RIS Applications

This article introduces a machine learning (ML) framework for the design of space-time-coding digital metasurface elements (STCDMEs), commonly used in reconfigurable intelligent surface (RIS)-based communication. It includes inverse design, forward design, and automodeling, which quickly achieve multistate electromagnetic (EM) structure designs, e.g., STCDME. The decision tree (DT) model is chosen for use with its lightweight, fast response, and highly accurate in EM structure small-scale data modeling. In addition, we present a new sensing STCDME design method, using gap technology based on ground structure. Using the proposed framework, we successfully design a sensing STCDME with a reflection phase within 180° ± 30° ranging from 6.66 to 7.3 GHz and a reflection coefficient larger than -2 dB, meeting RIS communication requirements. In the 8.27-9.5-GHz band, the structure's transmission coefficient exceeds -3 dB, achieving EM wave transmission and sensing capabilities. The proposed framework offers a novel method for STCDME design, and the resulting sensing STCDME structure can be used for RIS sensing, contributing significantly to wireless communication and sensing applications.</p

A Novel Flexible Multiport Interlinking Converter for DC Microgrid Clusters

This article presents a multiport interlinking converter (MIC) for synergising the power flow in direct-current microgrid (DCMG) clusters. The proposed MIC integrates three-level neutral point clamped (NPC) modules with pluggable full-bridge modules. It enables the interconnection of DC subgrids at wide voltage levels, ranging from low to medium voltage. The proposed MIC adopts the multiple-transformer design which facilitates the decoupling of power flow among subgrids and plug-and-play operations in the DCMG cluster. Different operating modes of the MIC are thoroughly analysed. A global power sharing (GPS) strategy is devised for the MIC to proactively manage the power imbalance between power generation and consumption. It involves the intrinsic droop characteristics of each subgrid, enabling an optimised power profile of the DCMG cluster. The proposed MIC and its power control strategy are validated through hardware-in-the-loop (HIL) and experimental tests based on a 4-kW MIC prototype. The proposed MIC is designed to provide flexible tie interfaces for DCMG clusters to push the boundaries of their compatibility, flexibility, and interoperability.</p

Risk-Aware Stochastic Scheduling of Hybrid Integrated Energy Systems with 100% Renewables

Recently, ambitious endeavors have been carried out to facilitate the transition from traditional grids to hybrid interconnected energy networks in the form of grid modernization. Align to such efforts, this article aims at developing a novel framework for satisfying techno-economic-environmental goals in the grid modernization process. To this end, a detailed examination is conducted for the optimal exploitation of energy hubs (EHs) equipped with 100% renewables to pursue the environmental goal alongside intending technical and economic constraints. The energy conversion technology is adopted to enable the power-to-gas system for establishing multi-energy interactions among electricity and gas networks. Fully benefiting from renewable units has exposed the system to uncertain fluctuations that necessitate the modeling of uncertainties to achieve near-reality results. Hence, risk-averse and seeker strategies are developed based on robustness and opportunistic modes of the information gap decision theory (IGDT) method to deal with stochastic fluctuations of uncertain parameters. The integrated electricity and gas test system is considered to analyze the applicability of the proposed framework in modeling efficient multi-energy interactions. Given the obtained results, 43.68% more energy cost is reached for EHs when they adopted a robust strategy against uncertainties under the risk-averse strategy. Moreover, the proposed framework procured a rational decision-making model for balancing multi-energy in the hybrid energy grid with 100% renewables

A binary inflectional voice contrast in Mabaan (Western Nilotic)

In Mabaan, a Western Nilotic language, there is a binary inflectional voice contrast in the morphology of verbs. In addition to a morphologically unmarked basic voice, there is a fully productive applicative voice, which is morphologically marked. This applicative voice may be called circumstantial in order to distinguish it from another applicative voice, which is derivational, namely benefactive. The circumstantial voice turns an adjunct into an object, making an intransitive verb transitive and a transitive verb ditransitive. In a main clause, however, a transitive verb needs to be detransitivized via antipassive derivation in order for an adjunct to become object through the circumstantial voice. In some types of subordinate clauses, by contrast, any verb can get the circumstantial voice, whatever its transitivity, derivational status and meaning. This voice is obligatory in relative clauses when the relativized constituent is an adjunct and in some types of adverbial clauses.In Mabaan, a Western Nilotic language, there is a binary inflectional voice contrast in the morphology of verbs. In addition to a morphologically unmarked basic voice, there is a fully productive applicative voice, which is morphologically marked. This applicative voice may be called circumstantial in order to distinguish it from another applicative voice, which is derivational, namely benefactive. The circumstantial voice turns an adjunct into an object, making an intransitive verb transitive and a transitive verb ditransitive. In a main clause, however, a transitive verb needs to be detransitivized via antipassive derivation in order for an adjunct to become object through the circumstantial voice. In some types of subordinate clauses, by contrast, any verb can get the circumstantial voice, whatever its transitivity, derivational status and meaning. This voice is obligatory in relative clauses when the relativized constituent is an adjunct and in some types of adverbial clauses