• Computers and Concrete
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• v.32 no.2
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• pp.119-138
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• 2023

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

• Journal of People, Plants, and Environment
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• v.22 no.1
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• pp.1-14
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• 2019

This study was designed to examine agro-healing services and programs provided and operated by farms in Korea. The results of the analysis of the agro-healing programs and operation of farms were as follows. The purpose of the operation of farms was to raise productivity by managing farms in a cooperative way through agricultural production, education and healing, and to raise income by processing and selling agricultural products. It was difficult to access farms by public transport and thus visitors had to use their own cars. The size of farms varied. The main resources utilized in the surveyed programs were plants, rural environments and landscapes, and agricultural products. The programs were conducted using resources that were commonly found in rural areas. Facilities on each farm were equipped with facilities (indoor and outdoor learning place, gardens, vegetable gardens, orchards, etc.) and convenience facilities (parking lots, drinking fountains, kiosks, etc.) to support program operation. However, facilities for the handicapped and accommodation facilities were insufficient. The programs operated on each farm utilized agricultural resources, farm produce, and rural resources and were classified into activities such as making, feeling, and growing. The average number of people who operated the family-centered program was 2-3, having qualifications such as welfare horticultural therapists, forest interpreters, experience instructors, and social workers. In addition, they had expertise in medicinal food, dietary life, and social welfare, and they also had essential expertise required to operate programs.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Information Systems Review
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• v.22 no.3
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• pp.15-30
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• 2020

In the real estate industry, the latest changes in the Fourth Industrial Revolution, such as big data analytics, machine learning, and VR (virtual reality), combine to bring about industry change. Proptech is a new term combining properties and technology. This study aims to derive and analyze from a comprehensive perspective the quality factors (systems, services, interfaces, information) for mobile real estate brokerage services that are well known and used in the domestic market. The surveys in this study were conducted online and offline and a total of 161 samples were used for statistical analysis. As a result, all hypotheses were approved to except system quality and service quality. The results show that the domestic proptech companies who are mostly focused on real estate brokerage services, peer-to-peer lending, advertising platforms and apartments need to grow in various fields of proptech business of other countries including Europe, USA and China.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.309-316
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• 2024

Facial recognition technology is widely used in various fields but faces challenges due to its vulnerability to fraudulent activities such as photo spoofing. Extensive research has been conducted to overcome this challenge. Most of them, however, require the use of specialized equipment like multi-modal cameras or operation in high-performance environments. In this paper, we introduce LH-FAS v2 (: Lightweight Head-pose-based Face Anti-Spoofing v2), a system designed to operate on a commercial webcam without any specialized equipment, to address the issue of facial recognition spoofing. LH-FAS v2 utilizes FSA-Net for head pose estimation and ArcFace for facial recognition, effectively assessing changes in head pose and verifying facial identity. We developed the VD4PS dataset, incorporating photo spoofing scenarios to evaluate the model's performance. The experimental results show the model's balanced accuracy and speed, indicating that head pose estimation-based facial anti-spoofing technology can be effectively used to counteract photo spoofing.

• Journal of Science Education
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• v.48 no.1
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• pp.63-73
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• 2024

Knowing the learner's image of a subject-related occupation is good data for determining the direction of a teacher's teaching and learning. Existing drawing image analysis tools have the limitation that it takes a long time to analyze images and drawings of a scientist's appearance. The semantic differential method is a widely used method to analyze images of specific objects. However, research using the semantic differential method has the limitation of failing to reflect terms or factors that change over time by using the adjective pairs used in the initial study as they were in accordance with the research content. In this study, we use the semantic differential method to develop a tool to measure middle school students' scientist image and apply it to middle school students to discuss educational implications regarding the usefulness of measuring scientist image.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.347-361
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• 2023

Water utilities are making various efforts to reduce water losses from water networks, and an essential part of them is to recognize the moment when a pipe burst occurs during operation quickly. Several physics-based methods and data-driven analysis are applied using real-time flow and pressure data measured through a SCADA system or smart meters, and methodologies based on machining learning are currently widely studied. Water utilities should apply various approaches together to increase pipe burst detection. The most intuitive and explainable water balance method and its procedure were presented in this study, and the applicability and detection performance were evaluated by applying this approach to water supply pipelines. Based on these results, water utilities can establish a mass balance-based pipe burst detection system, give a guideline for installing new flow meters, and set the detection parameters with expected performance. The performance of the water balance analysis method is affected by the water network operation conditions, the characteristics of the installed flow meter, and event data, so there is a limit to the general use of the results in all sites. Therefore, water utilities should accumulate experience by applying the water balance method in more fields.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.1
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• pp.17-30
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• 2022

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

• The Transactions of the Korea Information Processing Society
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• v.13 no.10
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• pp.553-558
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• 2024

Morphological analysis involves segmenting morphemes, the smallest units of meaning or grammatical function in a language, and assigning part-of-speech tags to each morpheme. It plays a critical role in various natural language processing tasks, such as named entity recognition and dependency parsing. Much of modern natural language processing relies on deep learning-based language models, and Korean morphological analysis can be broadly categorized into sequence-to-sequence methods and sequential labeling methods. This study proposes a morphological analysis approach using the transformer encoder for sequential labeling to perform syllable-level part-of-speech tagging, followed by morpheme restoration and tagging through a pre-analyzed dictionary. Additionally, the CBOW method was used to extract syllable-level embeddings in lower dimensions, designing a lightweight morphological analyzer model with reduced parameters. The proposed model achieves fast inference speed and low parameter usage, making it efficient for use in resource-constrained environments.