• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.6
• /
• pp.392-399
• /
• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.28 no.2
• /
• pp.27-34
• /
• 2024

This study was conducted to examine the feasibility of using Fe-activated wood-derived biochar as a conductive filler for manufacturing cement-based strain sensor. To evaluate the compressive and electrical properties of cement composite with 3% Fe-activated biochar, three cubic specimens of size 50 x 50 x 50mm³ and three prismatic cement-based sensors of size 40 x 40 x 80mm³ were prepared respectively. The four-probe method of electrical resistance measurement was used for cement-based sensors. For cement-based sensors with FE-activated biochar, the conductive performance such as electrical resistance and impedance under different water content and repeated compression was investigated. Results showed that the fractional changes in the DC electrical resistivity of cement-based sensors increase with increasing time and the maximum fractional changes in the resistivity decrease with increasing the moisture contents during 900s. At moisture content of 7.5% range, the conductive performance of cement composite including 3% Fe-activated biochar as a conductive filler showed the most stable, while the strain detection ability tended to decrease somewhat as the repeated compressive stress increased between repeated compressive strain and fractional change in resistivity (FCR).

• Information Systems Review
• /
• v.24 no.1
• /
• pp.21-37
• /
• 2022

Drug repositioning, one of the methods of developing new drugs, is a useful way to discover new indications by allowing drugs that have already been approved for use in people to be used for other purposes. Recently, with the development of machine learning technology, the case of analyzing vast amounts of biological information and using it to develop new drugs is increasing. The use of machine learning technology to drug repositioning will help quickly find effective treatments. Currently, the world is having a difficult time due to a new disease caused by coronavirus (COVID-19), a severe acute respiratory syndrome. Drug repositioning that repurposes drugsthat have already been clinically approved could be an alternative to therapeutics to treat COVID-19 patients. This study intends to examine research trends in the field of drug repositioning using machine learning techniques. In Pub Med, a total of 4,821 papers were collected with the keyword 'Drug Repositioning'using the web scraping technique. After data preprocessing, frequency analysis, LDA-based topic modeling, random forest classification analysis, and prediction performance evaluation were performed on 4,419 papers. Associated words were analyzed based on the Word2vec model, and after reducing the PCA dimension, K-Means clustered to generate labels, and then the structured organization of the literature was visualized using the t-SNE algorithm. Hierarchical clustering was applied to the LDA results and visualized as a heat map. This study identified the research topics related to drug repositioning, and presented a method to derive and visualize meaningful topics from a large amount of literature using a machine learning algorithm. It is expected that it will help to be used as basic data for establishing research or development strategies in the field of drug repositioning in the future.

• Journal of the Korean Geotechnical Society
• /
• v.40 no.3
• /
• pp.93-100
• /
• 2024

Pore water freezes in low-temperature compaction, which leads to different compaction characteristics compared to room temperature conditions. In regions like Alberta, Canada, where organic soils are prevalent, compaction performance is influenced by the high water retention and compressibility of organic soils, as well as their sensitivity to freezing and thawing. Alberta's strict environmental regulations demand the reuse of excavated soil for backfill, and the long winter season creates challenging conditions for civil engineering projects. In this study, a laboratory compaction test was conducted to evaluate the low-temperature compaction characteristics of organic soils with varying organic content. The results indicate that the optimum moisture content increases as the organic content increases, and the maximum dry unit weight decreases by up to 21.9%. In addition, under temperature conditions below -4℃, no optimum moisture content was observed, and the dry unit weight decreased as the moisture content increased.

• Advanced Industrial SCIence
• /
• v.3 no.2
• /
• pp.8-16
• /
• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Clean Technology
• /
• v.30 no.2
• /
• pp.145-156
• /
• 2024

Carbon has a large specific area and excellent chemical stability, so research on its use as a catalyst support is actively conducted. When using carbon as a support, the pretreatment process is essential. Through pretreatment of carbon, the growth of metal nanoparticles can be controlled and the bonding strength between the support and metal particles can be improved. In this study, carbon was pretreated for surface modification and 5 wt% Pd/C catalysts were synthesized using it as a support. Catalytic activity was evaluated through phenol hydrogenation. To compare with nitric acid, which is commonly used in carbon pretreatment, carbon pretreatment was performed using organic acid. Pd/C treated with gluconic acid showed the highest activity, with 94.93% phenol conversion and 92.76% cyclohexanone selectivity. Therefore, it is expected that pretreatment of the carbon support using organic acid will not only overcome the disadvantages of inorganic acid treatment but also improve catalyst performance.

• The Korean Journal of Applied Statistics
• /
• v.37 no.2
• /
• pp.225-237
• /
• 2024

In the analysis of competing risks data, some of covariates may not be fully observed for some subjects. In such cases, excluding subjects with missing covariate values from the analysis may result in biased estimates and loss of efficiency. In this paper, we studied multiple imputation and the augmented inverse probability weighting method for regression parameter estimation in the cause-specific proportional hazards model with missing covariates. The performance of estimators obtained from multiple imputation and the augmented inverse probability weighting method is evaluated by simulation studies, which show that those methods perform well. Multiple imputation and the augmented inverse probability weighting method were applied to investigate significant risk factors for the risk of death from breast cancer and from other causes for breast cancer data with missing values for tumor size obtained from the Prostate, Lung, Colorectal, and Ovarian Cancer Screen Trial Study. Under the cause-specific proportional hazards model, the methods show that race, marital status, stage, grade, and tumor size are significant risk factors for breast cancer mortality, and stage has the greatest effect on increasing the risk of breast cancer death. Age at diagnosis and tumor size have significant effects on increasing the risk of other-cause death.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.44 no.1
• /
• pp.41-48
• /
• 2024

In 2017, the Ministry of the Interior and Safety conducted research for the revision of seismic design standards and performed studies on standard design response spectra. As a result, the Common Application Guidelines for Seismic Design Standards were introduced, and these guidelines have been implemented in the national design standards of the Ministry of Land, Infrastructure, and Transport for practical use. However, it should be noted that the research for proposing standard design response spectra during the 2017 revision was conducted before the occurrence of the significant seismic events in South Korea, such as the 2016 Gyeongju Earthquake and the 2017 Pohang Earthquake. To account for these recent major earthquakes, this study adjusted the standard design spectra based on the records of the 2016 Gyeongju Earthquake and the 2017 Pohang Earthquake and conducted ground response analyses accordingly. The results revealed variations in peak ground acceleration (PGA) at the ground surface even within the same ground classification. It was confirmed that this variation can lead to overestimation or underestimation of seismic loads.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.44 no.1
• /
• pp.109-116
• /
• 2024

The Rapid Hardening Composite Mat (RHCM) is a product that improves the initial strength development speed of conventional Geosynthetic Cementitious Composite Mats (GCCM). It offers the advantage of quickly securing sufficient strength in railway slopes with insufficient formation level, and provides benefits such as preventing slope erosion and inhibiting vegetation growth. In this study, an analysis of the practical applicability of RHCM in railway settings was conducted through experimentation. The on-site applicability was assessed by categorizing it into fire resistance, durability, and stability, and conducting combustibility test, ground contact pressure test, and daily displacement analyses. In the case of South Korea, where a significant portion of the territory is composed of forested areas, the prevention of slope fires is imperative. To analyze the fire resistance of RHCM, combustibility tests were conducted as an essential measure. Durability was assessed through ground contact pressure tests to analyze the deformation and potential damage of RHCM caused by the inevitable use of small to medium-sized equipment on the construction surface. Furthermore, daily displacement analysis was conducted to evaluate the structural stability by comparing and analyzing the displacement and behavior occurring during the application of RHCM with railway slope maintenance criteria. As a result of the experiments, the RHCM was analyzed to meet the criteria for heat release rate and gas toxicity. Furthermore, the ground contact pressure was observed to be consistently above 50 kPa during the curing period of 4 to 24 hours under all conditions. Additionally, the daily displacement analyzed through field site experiments ranged from -1.7 mm to 1.01 mm, confirming compliance with the criteria.

• The Journal of Korean Society for Radiation Therapy
• /
• v.35
• /
• pp.15-21
• /
• 2023

Purpose: The purpose of this study is to compare the performance of the anisotropic analytical algorithm (AAA) and portal dose image prediction (PDIP) for patient-specific quality assurance based on electronic portal imaging device, and to evaluate the clinical feasibility of portal dosimetry using AAA. Subjects and methods: We retrospectively selected a total of 32 patients, including 15 lung cancer patients and 17 liver cancer patients. Verification plans were generated using PDIP and AAA. We obtained gamma passing rates by comparing the calculated distribution with the measured distribution and obtained MLC positional difference values. Results: The mean gamma passing rate for lung cancer patients was 99.5% ± 1.1% for 3%/3 mm using PDIP and 90.6% ± 5.8% for 1%/1 mm. Using AAA, the mean gamma passing rate was 98.9% ± 1.7% for 3%/3 mm and 87.8% ± 5.2% for 1%/1 mm. The mean gamma passing rate for liver cancer patients was 99.9% ± 0.3% for 3%/3 mm using PDIP and 96.6% ± 4.6% for 1%/1 mm. Using AAA, the mean gamma passing rate was 99.6% ± 0.5% for 3%/3 mm and 89.5% ± 6.4% for 1%/1 mm. The MLC positional difference was small at 0.013 mm ± 0.002 mm and showed no correlation with the gamma passing rate. Conclusion: The AAA algorithm can be clinically used as a portal dosimetry calculation algorithm for patientspecific quality assurance based on electronic portal imaging device.