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Millimeter-wave and Terahertz communications consist of complex analog and mixed-signal transceivers where overall system performance is often limited by the weakest performing subsystem. While analog and mixed-signal integrated circuits have significantly advanced, the future of 5G and 6G transceiver design could be accelerated by including artificial intelligence. In this combination, analog integrated circuit design and operation would harness machine learning to identify, characterize, and act upon variations and anomalies in system performance. Focusing on 5G and 6G, this paper investigates solutions for a unified intelligent integrated transceiver: a conceptual combination of a traditional analog subsystem, a supporting digital subsystem that enables artificial intelligence, and dedicated feedback circuitry or sensors that monitor performance, efficiency, or reliability. Active and passive components and propagation channels are reviewed based on their merits of introducing intelligence. Holistically and for broader applicability, the paper conceptualizes and coins the notion of an “intelligent integrated system (IIS)”, which brings forward a novel unified vision and approach toward context-aware subsystems that dynamically interact with ambient and varying operating conditions. To demonstrate viability, the paper concatenates a select set of measurement results.

Accurate prediction of the remaining useful life (RUL) of an ion mill is vital for optimizing the overall performance of the ion mill etching (IME) process. However, due to the uneven distribution of important information, and the poorly understood failure mechanisms, fault prognosis in this process presents significant challenges. Deep neural networks have shown promising results for extracting, without domain knowledge, relevant features from condition monitoring data. This study proposes a multi-scale and multi-branch Transformer network based on the vanilla Transformer to predict the RUL of ion mills. To extract features on various scales, multi-scale feature extraction first generates receptive fields of various sizes, which are then integrated to obtain feature representations. The multi-branch Transformer uses the parallel attention mechanism and long short-term memory (LSTM) to capture both the adjacent location information and the crucial information of a given timestamp. Handcrafted features are also incorporated as additional input to enhance the prediction accuracy of the model. The proposed model is evaluated on a dataset from a semiconductor IME process. The experimental results demonstrate that the proposed model outperforms other deep neural network and further highlight the practical feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Traditional transfer diagnosis models for internal combustion engines show a decrease in generalization ability due to the multisource features aliasing in vibration signals and the effect of variable operating conditions. To address this problem, this paper proposes a transfer diagnosis model based on the deep subdomain adaptive network framework. To address feature aliasing, based on minimizing amplitude moment and reconstruction loss, a new adaptive decomposition layer is designed and embedded into the framework to decompose complex signals into single-impact components in time domain. To alleviate the effect of operating conditions, a new constraint for minimizing signal feature variance loss is designed and introduced into the framework’s loss function. This constraint calculates the variance of the sample features of the same fault label under variable operating conditions, aiming to excavate invariant features of operating conditions and complete feature mapping of domain adaptation. Validation with experimental data yields an accuracy of 94.81%.

In power systems with a significant hydroelectric component, instances of the Unit Commitment (UC) problem may be much more computationally intensive due to the longer decision horizons and the additional hydro constraints. Therefore, this paper presents a methodology to reduce the solution space to accelerate 168-hour-ahead UC formulated as a Mixed-Integer Linear Program (MILP). First, an offline model maps environment observations to actions in a Multi-Agent Deep Reinforcement Learning (MADRL) model. This mapping uses historical power system operation data to determine the on/off status of specific generation units. Then, the online model uses the binary variable solutions obtained by the offline model to solve a UC problem with a reduced solution space. The Multi-Agent approach allows each agent, based on Artificial Neural Networks (ANN) with a Temporal Convolutional Network (TCN) architecture, to group units that are located in the same region. A shared cumulative reward function is used to adjust simultaneously the different ANN weights during the learning phase. The effectiveness of our method is demonstrated using real operational data of the Chilean National Electricity System, achieving statistically significant lower computation times and a negligible error that is within the integrality gap of the solver.

An exponential growth and complexity in diverse distribution systems have contributed to protection coordination challenges. Initially, protection coordination schemes were achieved by means of conventional techniques; however, the utilisation of such methods is based on trial-and-error principles and laborious. Consequently, current studies have adopted the utilisation of particle swarm optimization, artificial intelligence models, and genetic algorithms to optimise overcurrent relay selectivity and operational speed. Particle swarm optimization, artificial intelligence, and genetic algorithms are optimization techniques that at times converges prematurely due to poor selection of control parameters and lack of optimal values, which results in increased computational time. Therefore, this paper presents a comprehensive review of recent developments in terms of parametric sensitivity analysis, selection of artificial intelligence models based on data availability, and the likelihood of solving overcurrent relay coordination problems. The reviewed literature shows that particle swarm optimization performance is greatly influenced by inertia weight and swarm size, while the number of iterations has insignificant effect. The findings also indicate that crossover rate, mutation probability, and population size affect genetic algorithms behaviour. Artificial intelligence models lack sensitivity study for parametric tuning, that is, number of hidden layers, membership functions, epsilon in support vector machine, and number of fuzzy rules affects the models’ performance.