Hayoung Kim

Among all diseases affecting rice production, rice blast disease has the greatest impact. Thus, monitoring and precise prediction of the occurrence of this disease are important; early prediction of the disease would be especially helpful for prevention. Here, we propose an artificial-intelligence-based model for rice blast disease prediction. Historical data on rice blast occurrence in representative areas of rice production in South Korea and historical climatic data are used to develop a regi

Compressive strength is a critical indicator of concrete quality for ensuring the safety of existing concrete structures. As an alternative to existing nondestructive testing methods, image-based concrete compressive strength estimation models using three deep convolutional neural networks (DCNNs), namely AlexNet, GoogLeNet, and ResNet, were developed for this study. Images of the surfaces of specially produced specimens were obtained using a portable digital microscope, after which the samples

Defects in residential building façades affect the structural integrity of buildings and degrade external appearances. Defects in a building façade are typically managed using manpower during maintenance. This approach is time-consuming, yields subjective results, and can lead to accidents or casualties. To address this, we propose a building façade monitoring system that utilizes an object detection method based on deep learning to efficiently manage defects by minimizing the involvement of man

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market‐neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs‐trading strategy using deep reinforcement learning—particularly with the deep Q‐network—utilizing various trading and stop‐loss boundaries. More specifically, if s

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural netwo

This paper reports the synthesis of core–shell structured poly(2-ethylaniline) (PEAN) coated cross-linked poly(methyl methacrylate) (PEGDMA) particles and their electrorheological property under an applied electric field. Primarily, monodisperse poly(methyl methacrylate) nanoparticles (∼700 nm) were synthesized by dispersion polymerization. The PEAN–PEGDMA microspheres with an average diameter of 1.6 μm were then prepared by an oxidative polymerization process. The application of a suspension of