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The fault management process is facilitated by equipping power distribution systems with automated devices, especially remote-controlled switches (RCSs). Although RCS plays a key role in improving the system's reliability, it imposes significant investment, installation, and maintenance costs. Hence, RCSs should be optimally located in distribution feeders for the highest profit. In previous works, reliability-oriented mathematical optimization models have been formulated to reach this goal. However, the number of test solutions exponentially grows with the problem size to find the globally optimal solution. This paper uses machine learning to propose a scalable and easy-to-implement model for optimal switch placement in real power distribution systems. At first, the features of candidate points for installing RCSs are introduced. Then, a learning model is applied to deeply explore the relationship between these features and optimal locations for installing RCSs. After training, the learning-based surrogate model directly determines the optimal RCS placement strategy in real power distribution systems by leveraging knowledge gained from past experiences. Simulation results demonstrate that the proposed surrogate model is approximately 29 times faster than the integer programming-based mathematical model, without a significant loss of accuracy, when implemented on a modified 11 kV network connected to Bus 4 of the Roy Billiton test system. This paper also employs explainable artificial intelligence (XAI) tools to select the most important features, where the Hamming loss is decreased by approximately 5%. [ABSTRACT FROM AUTHOR]

Automatic ship detection from spaceborne systems such as satellites or aircrafts, raises considerable attention in sea surface monitoring because of the several applications in military and civilian field. In this context, processing satellite images on-board would reduce the latency time especially for emergency situations. In this paper, an hardware-oriented (HO) ship detection system based on a customized Convolutional Neural Network (CNN), here referred to as HO-ShipNet , is proposed and tested on a revised version of the "Ships in Satellite Imagery" (SSI) Kaggle dataset, reporting detection accuracy of up to 95%. Furthermore, the explainability of HO-ShipNet is investigated by means of explainable Artificial Intelligence (xAI) techniques (i.e., Local Interpretable Model-Agnostic Explanation (LIME) and Occlusion Sensitivuty Analysis (OSA)), in order to understand the reasoning behind the HO-ShipNet decisions by detecting the most important input features and consequently ensure the trustworthiness of the model itself. Finally, HO-ShipNet is also implemented on the heterogeneous Xilinx xc7z045ffg900-2 SoC Field Programmable Gate Array (FPGA) outperforming state-of-the-art FPGA-based accelerators dealing with high-resolution frames. The promising results encourage the potential deployment of the proposed system for on-board applications. [ABSTRACT FROM AUTHOR]

Object detection is one of the most important domains in computer vision tasks, which is an important branch of artificial intelligence. It aims at finding and locating the accurate position of objects in given pictures or videos. With the development of deep learning techniques, more powerful and robust algorithms have emerged to deal with multi-scale, high-level features to overcome the limitations of traditional pipeline of object detectors. The popularity of transformer framework enables larger capacity datasets by processing self-attention mechanism, and the object detection methods have evolved into a new era. This paper first reviews traditional object detection pipeline and brief history of deep learning, afterwards it focuses on the classification of deep learning-based object detection methods covering Convolution Neural Network based and transformer-based methods. Commonly used datasets and metrics are also covered in the next part. The Convolution Neural Network based methods mainly contain two-stage and one-stage detectors, Convolution Neural Network is the underlying structure of these methods convolutional stages are fundamental parts. Transformer-based models convert traditional object detection issues into end-to-end detection, which is widely used in dealing with images. Finally, the promising future of object detection areas are listed to show guidance on future work. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper introduces a novel integrated microstructure design methodology that replaces the common existing design approaches for multifunctional composites: 1) reconstruction of microstructures, 2) analyzing and quantifying material properties, and 3) inverse design of materials. The problem of microstructure reconstruction is addressed using the diffusion-based generative model (DGM), which is a state-of-the-art generative model formulated with a Markovian diffusion process. Then, the conditional formulation of DGM is introduced for guidance to the embedded desired material properties with a transformer-based attention mechanism, which enables the inverse design of multifunctional composites. Furthermore, a convolutional neural network (CNN)-based surrogate model is utilized to facilitate the prediction of linear/nonlinear material properties for building microstructure-property linkages. Combined, the proposed artificial intelligence-based design framework enables large data processing and database construction that is often not affordable with resource-intensive finite element method (FEM)-based direct numerical simulation (DNS) or iterative reconstruction methods. It is worth noting that the proposed DGM-based methodology is not susceptible to unstable training or mode collapse, which are common issues in generative models that are often difficult to address even with extensive hyperparameter tuning. An example case is presented to demonstrate the effectiveness of the proposed approach, which is designing mechanoluminescence (ML) particulate composites. The results show that the designed ML microstructure samples with the proposed generative and surrogate models meet the multiple design requirements (e.g., volume fraction, elastic constant, and light sensitivity). This assessment demonstrates that the proposed integrated methodology provides an end-to-end solution for practical material design applications. [Display omitted] • An integrated microstructure design framework utilizing the diffusion-based generative model (DGM) is proposed. • The problem of microstructure reconstruction is addressed using the DGM with forward/reverse Markovian diffusion processes. • The conditional formulation of DGM is introduced to enable the inverse design of multifunctional composites. [ABSTRACT FROM AUTHOR]

The antinuclear antibody (ANA) test plays the role of screening, diagnosis, treatment and prognosis for autoimmune diseases. However, the ANA test is time-consuming, and strongly depends on the interpretation of experienced medical examiners. With the rise of artificial intelligence, a lot of convolutional neural networks (CNN) have been used to recognize the features of ANA images, such as GoogLeNet, ResNet, and MobileNet. By extracting the disease-related features from the entire ANA image, these models achieve acceptable accuracy, but none of them can replace the manual ANA test completely. This is because they ignore the key features of ANA images. Accordingly, this paper aims to design an object detection model that can directly identify the key features of ANA images, i.e., the Metaphase cells. This process is critical in the manual ANA test performed by the medical examiners. Based on the YOLO model, our design first applies various image preprocessing and data augmentation techniques to enhance the critical features in the training data. We modify the original YOLO model, in which the residual block is replaced with SENet module to improve the classification performance by emphasizing critical features and reducing the influence of low-level features. Moreover, we use the different activation function to prevent the modified model from partial judgment. The experimental results show that our design can effectively improve the performance of ANA metaphase identification. Compared to the related CNN models, our design can improve F1-score and mAP by range 5%–14% and 11%–13%, respectively. In particular, the Kappa value of our design is 0.82 that is almost perfect agreement with the ANA experts. [ABSTRACT FROM AUTHOR]