• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Analytical Science and Technology
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• v.20 no.4
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• pp.308-314
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• 2007

The analytical method of HPLC with the radial-flow electrochemical cell (RFEC) has been developed to determine doxorubicin, epirubicin, nogalamycin, daunorubicin and idarubicin simultaneously by employing a reversed-phase chromatography. Anthracyclines were detected at -0.74 V vs. a Ag/AgCl (0.01 M NaCl) reference electrode, a potential of diffusion current plateau in the mobile phase. At a $V_f$ of 1.0 mL/min doxorubicin, epirubicin, daunorubicin and idarubicin appeared at a retention time ($t_r$) of 6.4 min, 7.4 min, 12.7 min and 18.4 min, respectively, while at a $V_f$ of 0.6 mL/min, doxorubicin, epirubicin, nogalamycin, daunorubicin and idarubicin appeared at a $t_r$ of 9.9 min, 11.5 min, 13.5 min, 19.6 min and 28.7 min, respectively. The linearity between each anthracycline injected ($2.40{\times}10^{-7}M{\sim}1.42{\times}10^{-5}M$) and peak area (charge) was excellent with the square of the correlation coefficient ($R^2$) higher than 0.999. The detection limits were $1.0{\times}10^{-8}M{\sim}1.5{\times}10^{-7}M$ for the five anthracyclines. Within-day precision for the five anthracyclines were in reasonable relative standard deviations less than 3 % ($1.00{\times}10^{-6}M{\sim}1.42{\times}10^{-5}M$) except the lower concentrations less than $0.7{\mu}M$. Solid phase extractions of $1.00{\times}10^{-5}M$ epirubicin, $0.48{\times}10^{-5}M$ nogalamycin and $1.52{\times}10^{-5}M$ daunorubicin from human serum with a $C_{18}$ cartridge resulted in 97 %, 100 % and 90 % of recoveries, respectively.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.247-263
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• 2023

Purpose: This study was conducted to update the Nutrition Quotient for Adolescents (NQ-A), which is used to assess the overall dietary quality and food behavior among Korean adolescents. Methods: The first 30 candidate items of the measurable eating behavior checklist were obtained based on a previous NQ-A checklist, the results of the seventh Korea National Health and Nutrition Examination Survey data, Korea Youth Risk Behavior Survey data, national nutrition policies and dietary guidelines, and literature reviews. A total of 100 middle and high school students residing in Seoul and Gyeonggi Province participated in a pilot study using the 25-item checklist. Factor analysis and frequency analysis were conducted to determine if the checklist items were organized properly and whether the responses to each item were distributed adequately, respectively. As a result, 22 checklist items were selected for the nationwide survey, which was applied to 1,000 adolescent subjects with stratified sampling from 6 metropolitan cities. The construct validity of the updated NQ-A 2021 was assessed using confirmatory factor analysis. Results: Twenty checklist items were determined for the final NQ-A 2021. The items were composed of three factors: balance (8 items), moderation (9 items), and practice (3 items). The standardized path coefficients were used as the weights of items to determine the nutrition quotients. NQ-A 2021 and 3-factor scores were calculated according to the weights of questionnaire items. The weight for each of the 3 factors was determined as follows: balance, 0.15; moderation, 0.30; and practice, 0.55. Conclusion: The updated NQ-A 2021 is a useful instrument for easily and quickly evaluating the dietary qualities and eating behaviors of Korean adolescents.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.361-372
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• 2023

Organotin pesticide is used as an acaricide in agriculture and may contaminate livestock products. This study aims to develop a rapid and straightforward analytical method for detecting organotin pesticides, specifically azocyclotin, cyhexatin, and fenbutatin oxide, in various livestock products, including beef, pork, chicken, egg, and milk, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The extraction process involved the use of 1% acetic acid in a mixture of acetonitrile and ethyl acetate (1:1). This was followed by the addition of anhydrous magnesium sulfate (MgSO₄) and anhydrous sodium chloride. The extracts were subsequently purified using octadecyl (C₁₈) and primary secondary amine (PSA), after which the supernatant was evaporated. Organotin pesticide recovery ranged from 75.7 to 115.3%, with a coefficient of variation (CV) below 25.3%. The results meet the criteria range of the Codex guidelines (CODEX CAC/GL 40). The analytical method in this study will be invaluable for the analysis of organotin pesticides in livestock products.

• Journal of Life Science
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• v.34 no.7
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• pp.485-492
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• 2024

Sea cucumbers contain more than 50% protein in their solid content, and they also possess various bioactive substances such as saponins and mucopolysaccharides. This study analyzed the activities of various enzymes derived from Bacillus and lactic acid bacteria and determined to degrade the components of sea cucumbers. Among the analyzed strains, B. subtilis K26 showed the highest activities in protease and xylanase and relatively high activity in cellulase. Accordingly, samples of sea cucumber and water were mixed in equal proportions, sterilized, and then fermented by inoculating them with B. subtilis K26. Following this, a higher amino acid content was observed between 1.5 and 7.5 hr, a lower residual solid content in this time, and a lesser fermentation odor. The saponin content in fermented sea cucumber powder extracted with butanol was measured to be 1.12 mg/g. The chondroitin sulfate content was evaluated to be 5.11 mg/g in raw sea cucumber. The total polyphenol content, flavonoid content, and antioxidant activities were 6.95 mg gallic acid equivalent/g, 3.69 mg quercetin equivalent/g, and 3.69 mg quercetin equivalent/g in raw sea cucumber, respectively. Moreover, the DNA damage protective effect of fermented sea cucumber extract was found to be concentration-dependent, with a very strong effect at very low concentrations. Overall, we suggest that sea cucumber fermented with B. subtilis K26 has a high potential as a food for inhibiting oxidation, enhancing immunity, and improving muscle function in the human body thanks to its high free amino acid content.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Dental Rehabilitation and Applied Science
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• v.40 no.3
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• pp.149-158
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• 2024

Purpose: This study aims to investigate the effects of provisional restoration fabrication methods and surface polishing on surface roughness and microbial adhesion through in vitro experiments. Materials and Methods: 120 cylindrical provisional restoration resin blocks (10 × 10 × 2.5 mm) were manufactured according to four fabrication methods, and 30 specimens were assigned to each group. Afterwards, they were divided into non-polishing group, #400 grit SiC polishing group, and #800 grit SiC polishing group and polished to a 10 × 10 × 2 mm specimen size (n = 10). The surface roughness Ra and Ry of the specimen was measured using a Surface Roughness Tester. Three specimens were extracted from each group and were coated with artificial saliva, and then Streptococcus mutans were cultured on the specimens at 37℃ for 4 hours. The cultured specimens were fixed to fixatives and photographed using a scanning electron microscope. For statistical analysis, the two way of ANOVA was performed for surface roughness Ra and Ry, respectively, and the surface roughness was tested post-mortem with a Scheffe test. Results: The fabrication method and the degree of surface polishing of the provisional restorations had a significant effect on both surface roughness Ra and Ry, and had an interaction effect. There was no significant difference in Ra and Ry values in all polishing groups in DLP and LCD groups. Conclusion: The fabrication method and surface polishing of the provisional restoration had a significant effect on surface roughness and showed different adhesion patterns for S. mutans adhesion.