Dong‐Eun Lee 교수

This review focuses on recently proposed photocatalyst designs for maximizing the efficiency of photogenerated charge carriers to improve the photocatalytic performance of photocatalysts. These designs include doping with metal and non-metal ions, surface grafting, noble metal deposition, dye sensitization, and heterostructure fabrication. In addition, this review thoroughly examines all the prominent parameters that can influence the degradation efficiency of photocatalysts. Furthermore, this r

Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole d

This paper introduces a software, Stochastic Project Scheduling Simulation (SPSS), developed to measure the probability to complete a project in a certain time specified by the user. To deliver a project by a completion date committed to in a contract, a number of activities need to be carried out. The time that an entire project takes to complete and the activities that determine total project duration are always questionable because of the randomness and stochastic nature of the activities’ du

This paper presents a model that quantifies the causal relations among safety variables (latent variables) and workers' safety behavior (indicator) using statistical data and hypotheses obtained from construction workers and existing literatures, respectively. The safety variables that affect workers' safety behaviors are identified from existing studies and operationalized to measure their causal relations with the workers' behaviors. The model identifies the directions and degrees of the effec

This paper describes a stochastic simulation-based scheduling system (S3) that: (1) integrates the deterministic critical path method (CPM), the probabilistic program evaluation and review technique (PERT), and the stochastic discrete event simulation (DES) approaches into a single system and lets the scheduler make an informed decision as to which method is better suited to the company’s risk-taking culture; (2) automatically determines the minimum number of simulation runs in DES mode and ther

Magnetic nanoparticles (MNPs) are widely used materials for biomedical applications owing to their intriguing chemical, biological and magnetic properties. The evolution of MNP based biomedical applications (such as hyperthermia treatment and drug delivery) could be advanced using magnetic nanofluids (MNFs) designed with a biocompatible surface coating strategy. This study presents the first report on the drug loading/release capability of MNF formulated with methoxy polyethylene glycol (referre

Conducting polymers (CPs) have been proved to be instrumental in enhancing photocatalytic efficacy owing to their unique physicochemical properties and energy levels. In this regard, titanium dioxide (TiO2) has been investigated in transdisciplinary research areas (like catalysis, energy technologies, health, and environment) due to its desirable characteristics. In the process of developing advanced photocatalysts, novel inorganic–organic heterojunction design based materials have been explored

This paper presents a smart artificial neural network (ANN)-based slip-trip classification method, which integrates a smart sensor and an ANN. It was trained to identify the slip and trip events that occur while a worker walks in a workplace. It encourages preventive and collective actions to reduce construction accidents by identifying the type of near miss, i.e., slip or trip, and the exact time that it occurs. The variation in the energy released by a worker is measured using a triaxial accel

The adoption of artificial intelligence in post-earthquake inspections and reconnaissance has received considerable attention in recent years, owing to its exponential increase in computation capabilities and inherent potential in addressing disadvantages associated with manual inspections. Herein, we present the effectiveness of automated deep learning in enhancing the assessment of damage caused by the 2017 Pohang earthquake. Six classical pre-trained convolutional neural network (CNN) models