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

Sound of democracy:towards the democratisation of standards for soundscapes

With this paper and the accompanying audio paper, our aim is to explore the juxtaposition of sound and democracy. Recognizing the multifaceted nature of this field, we approach it through the notion of care and an empirical analysis of democratic implications of trying to regulate sound in public spaces. We examine standards as one example of the many attempts to control sound in both public and private spaces. Standards are used to categorize, govern, and control. They are also tools to assess and characterise. As policy tools they raise democratic questions relating to who developed them, when and how they are used. Using the example of a shopping mall, we explore the democratic values reflected in the standards through their development and application through an inter-disciplinary perspective. We used the audio paper format to move from discussing the process of developing the soundscape standards to applying them in a real-world setting, ultimately exploring the consequences of such standards from the perspective of democratic values. The present written paper is a longer elaboration on methodology and theory as a compliment to the audio paper which develops the discussion and analysis

Dynamic Coupling Mechanism Analysis Between Voltage and Frequency in Virtual Synchronous Generator System

This letter investigates the dynamic coupling mechanism between voltage and frequency in a virtual synchronous generator (VSG) system, where a unified model is proposed for the analysis of voltage stability, frequency stability and voltage-frequency coupled stability. The results show that voltage/frequency stability is impacted by both self- and coupled-dynamics. Moreover, voltage and frequency oscillate at the same oscillation frequency if the voltage-frequency coupled instability occurs. However, the frequency oscillation occurs while voltage magnitude maintains stability if voltage self-dynamics behaves a negative feedback effect. The time-domain simulation is performed to validate the rationality of the theoretical analysis

A global S&amp;OP perspective: managing cannibalization effect at the early phase of product transition

In today’s highly competitive market, introduction of new products is a critical strategic capa-bility, this results in frequent product transitions. Managing product transition is a complex task.In the product transition period companies face a set of problems: accurate demand forecasts, con-trolling production and inventory costs and providing high quality delivery performance. Literatureconsiders that managing the interplay between product generations can increase the chances forsuccess. S&amp;OP has been recognized as a tactical supply chain planning tool fostering integrationand coordination within and across organizations to reconcile the demand and supply and to bridgestrategy and operations. Scholars have delved into the impact of S&amp;OP mechanisms on improvingthe success of new product introduction and integration of decisions related to new products, suchas determining optimal launch times.Drawing on data from an interaction study, we propose an approach that leverages sales data toestimate the peak time for customer switching between product generations. This enables S&amp;OP tofunction as an early warning and decision support system, facilitating smooth coordination duringthe transition period between two product generations. Our contribution extends beyond the devel-opment of this approach; it also sheds light on our interactions with stakeholders across differentdivisions. These interactions highlight the evolving role of S&amp;OP in managing product transitions,indicating a directional shift towards a more integrated and data-driven approach in handling thecomplexities of product lifecycle managemen

Techno-economic analysis of blue ammonia synthesis using cryogenic CO2 capture Process-A Danish case investigation

Ammonia is a vital chemical with numerous applications. Currently, the primary methods for generating the necessary reactants for ammonia production involve steam methane reforming (SMR) and cryogenic air separation unit (CASU), while the Haber-Bosch process converts these reactants into ammonia. However, the SMR process releases substantial amounts of CO 2, making it imperative to employ an efficient and cost-effective CO 2 capture technology to mitigate emissions. This investigation focuses on evaluating the cryogenic CO 2 capture (CCC) process for blue ammonia production and provides a thorough economic analysis, estimating both the initial investment costs and operational expenses involved in producing blue ammonia. The results indicated that the CCC process can capture 90% of the CO 2 content in the flue gas emitted by the SMR, incurring an energy penalty of 0.724 MJ e/kg CO 2 while capturing CO 2 in the liquid phase with purities exceeding 99.9%. In this case, the estimated CO 2 capture costs would be 18.05, 45.1, and 16.65 USD/ton in 2021, 2022, and 2023, respectively. This represents a 40% reduction compared to the CO 2 capture costs associated with conventional amine-based technology. The results of this study indicate that the annual electricity costs for ammonia production increase by 38.5% and 64.2% when employing the CCC and amine-based processes, respectively. This investigation employed an isothermal reactor for ammonia synthesis, using the heat from the exothermic reaction in a water ammonia absorption refrigeration cycle (ARC) to condense and purify ammonia. The results show that the ARC system can effectively condense ammonia at −6 °C, producing a liquid ammonia stream with 99.3% purity. This leads to a 95% reduction in power consumption compared to a vapor compression refrigeration cycle (VCRC). Consequently, this method has the potential to decrease the annual operational costs for ammonia production by 2.92%, 2.69%, and 3.13% in 2021, 2022, and 2023, respectively. This study indicated that the hydrogen production unit incurs the highest initial investment costs, as well as operating costs, in the blue ammonia production process, followed by CASU and the Haber-Bosch process.</p