• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.11a
• /
• pp.231-232
• /
• 2023

It is important to utilize an efficient quality management platform because the management of deadlines at construction sites requires rapid and accurate processing of numerous defect data in a short time. In this study, 30 domestic patents for defect management and image analysis were analyzed to examine the development status of quality management platforms using mobile devices. As a result of the analysis, research on automatically detecting defects using artificial intelligence has been actively underway in recent years, and advanced IT technologies have been converging in various ways into linked services.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.293-302
• /
• 2024

The rapid emergence of Artificial Intelligence (AI) across diverse sectors has also made its presence felt in the construction sector, where its adoption is gaining momentum at a remarkable pace. The anticipated impact of AI on decision-making processes pertinent to construction project management is considerable, necessitating a holistic understanding of AI's potential applications. As a first step towards that goal, this paper conducts a systematic literature review and in-depth content analysis of existing literature related to the applications of AI in the context of construction project management. The authors selected journal papers, technical papers, and conference proceedings published between 2010 and 2023 on the topic of Artificial Intelligence for construction project management applications. Additionally, the authors also reviewed several industry and trade publications in the same topic area. The search resulted in more than 200 relevant articles, after which the authors conducted a thorough content analysis. The results categorized applications of AI in construction project management across categories: construction productivity, construction safety, construction quality, construction document management, and construction site planning. Additionally, the review identified the current trends of AI applications in construction project management, advantages, and challenges to implementation. Understanding AI applications, advantages, and challenges to implementation helps contractors gain new insights into the efficient implementation of AI for various project management purposes.

• Korean Journal of Radiology
• /
• v.24 no.12
• /
• pp.1179-1189
• /
• 2023

Objective: We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. Materials and Methods: A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. Results: Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. Conclusion: The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.7
• /
• pp.877-883
• /
• 2021

Artificial intelligence technology is widely used in industrial and smart home fields such as manufacturing robots, artificial intelligence speakers, and robot vacuum cleaners. In this paper, we designed and implemented a 1:1 chatbot system based on artificial intelligence for use in RCMS (Real-time Cash Management System). The RCMS chatbot implemented in this paper was constructed with a total of 210 query scenarios in nine areas, including research expenses and system usage, based on 13,500 questions and answers from existing online bulletin boards. The chatbot is expected to solve the problem of insufficient number of counselors and to increase user satisfaction by responding to the researcher's inquiries after working hours, and the recommendation service for the cost of use, which had the most inquiries from researchers, reduces the number of consultations. It is expected to improve the quality of answers to other counseling inquiries.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.2
• /
• pp.119-125
• /
• 2024

This paper investigates the effect of an artificial intelligence attention redirection compensation strategy system on the data labeling work attention concentration by individuals with developmental disabilities. Task accuracy and task performance for each session were used as measures of attention concentration. As a result of the study, after the intervention was applied, a significant improvement in attention concentration was observed in all study subjects compared to self-serving task. These results mean that artificial intelligence technology can have a positive effect on improving the attention span of people with developmental disabilities during data labeling tasks. This study shows that the application of artificial intelligence technology can improve the quality of learning data by improving the accuracy of data labeling tasks for people with developmental disabilities, and is expected to provide important implications for vocational training programs related to data labeling for people with developmental disabilities.

• Journal of Biomedical Engineering Research
• /
• v.43 no.4
• /
• pp.290-297
• /
• 2022

During surgery, Surgical instruments are often left behind due to accidents. Most of these are surgical gauze, so radioactive non-permeable gauze (X-ray gauze) is used for preventing of accidents which gauze is left in the body. This gauze is divided into wire and pad type. If it is confirmed that the gauze remains in the body, gauze must be detected by radiologist's reading by imaging using a mobile X-ray device. But most of operating rooms are not equipped with a mobile X-ray device, but equipped C-Arm equipment, which is of poorer quality than mobile X-ray equipment and furthermore it takes time to read them. In this study, Use C-Arm equipment to acquire gauze image for detection and Build dataset using artificial intelligence and select a detection model to Assist with the relatively low image quality and the reading of radiology specialists. mAP@50 and detection time are used as indicators for performance evaluation. The result is that two-class gauze detection dataset is more accurate and YOLOv5 model mAP@50 is 93.4% and detection time is 11.7 ms.

• Journal of Korean Society of Water and Wastewater
• /
• v.36 no.4
• /
• pp.239-248
• /
• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.2
• /
• pp.121-125
• /
• 2021

Cloud service has the characteristic that it must be always available and that it must be able to respond immediately to user requests. This study suggests a method for constructing a proactive and autonomous quality and performance management system to meet these characteristics of cloud services. To this end, we identify quantitative measurement factors for cloud service quality and performance management, define a structure for applying a time series framework to cloud service application quality and performance management for proactive management, and then use big data and artificial intelligence for autonomous management. The flow of data processing and the configuration and flow of big data and artificial intelligence platforms were defined to combine intelligent technologies. In addition, the effectiveness was confirmed by applying it to the cloud service quality and performance management system through a case study. Using the methodology presented in this study, it is possible to improve the service management system that has been managed artificially and retrospectively through various convergence. However, since it requires the collection, processing, and processing of various types of data, it also has limitations in that data standardization must be prioritized in each technology and industry.

• IE interfaces
• /
• v.7 no.2
• /
• pp.99-105
• /
• 1994

The Artificial Intelligence Lab of Industrial Engineering Department, Korea University is continuing to develop expert systems for quality control methods such as acceptance control, process control and reliability analysis. As a series of these efforts, The Artificial Intelligence Lab of Industrial Engineering Department, Korea University is continuing to develop expert systems for quality control methods such as acceptance control, process control and reliability analysis. As a series of these efforts, this paper concerns an expert system for design of experiment. The system includes factorial experiments, response surface methodology and Taguchi method. PROLOG is used as a language with dBASE III＋ for the data base management system and C for calculations and graphics. This system selecting the appropriate method and analyzing the data obtained can be implemented on an IBM PC 386 or a higher level machine.

• Korean Journal of Artificial Intelligence
• /
• v.2 no.1
• /
• pp.4-6
• /
• 2014

These days, aging, the aged and patients rapidly increased to produce problems, for instance, rapid increase of demand on medical service, higher medical expenses, low quality of the elderly's lives, shortage of physicians and nurses, and others [1]. These days, not only IT technology but also medical technology has taken the lead in settlement of the problems. Patients see a doctor to be given medical treatment and service when they are sick to have difficulty. The study investigated lifestyle monitoring system of chronic disease patients to indicate variation depending upon time. The health care is likely to solve problems of the elderly and chronic disease patients and to satisfy desire of better life quality by living healthy life and to diagnose diseases and give medical treatment and to give solutions in accordance with changes of paradigm of medical services.