• Journal of Sasang Constitutional Medicine
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• v.31 no.3
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• pp.48-65
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• 2019

Background Several attempts have been made to accurately diagnose the Sasnag Constitution. One of these attempts is to use a questionnaire. Questionnaire for the Sasang Constitution Classification(QSCC) has been revised several times and now used as QSCC II+. This study was designed to improve the accuracy of the revised Questionnaire for the Sasang Constitution Classification(QSCC II+). Method 1,054 people were gathered for this study and analyzed to check discrimination ability of current discriminant function of QSCC II+. They were outpatients who visited the hospital and the constitution was confirmed by the specialist of Sasang Constitutional Medicine. Results Accuracy of QSCC II+ at Soeumin was improved from 74.9% to 79.3%, and there were no significant difference at Soyangin and Taeumin. Conclusion New discriminant function was constructed through discriminant analysis. And the accuracy of QSCC II+ was generally improved, especially in Soeumin.

• Journal of IKEEE
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• v.24 no.4
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• pp.921-930
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• 2020

The Counter-battery radar estimates the origin and impact point of the artillery by tracking the trajectory of the shell. In addition, it has the ability of identifying the type of weapon. Depending on the position between the shell and the radar, the detected signals appear differently. This has ambiguity to distinguish the type of shells. This paper compares fuzzy logic and artificial intelligence, which classifies type of shell using the parameter of signal processing step. According to the research result, artificial intelligence can improve identification rate of type of shell. The data used in the experiment was obtained from a live fire detection test.

• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.17-26
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• 2020

In Korea, primary school (also junior high school) is compulsory and free since 2004. But it looks high school is also compulsory, as over 80% of adults has a high school diploma currently. For 20 years improving high school education is only education-oriented, rather than occupation-oriented. And, the mismatch between the occupational requirement and the lesson from school is getting larger. To resolve this issue, the Korean government builds and utilizes National Competency Standards(NCS) to realize a competence-oriented society. With NCS, the government enables to run of a work-study program and tries to suggest the fundamental solution to improve occupational ability and the unemployment of young people. However, the prejudice against the education level and occupation is still engrained, and it is hard to match the education-career-qualification based on NCS. Therefore, we study NCS from the definition to the utilization, suggest an improving method to flexibly utilized the standards in the fields, and continuously improve and develop the NCS.

• Journal of Korea Game Society
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• v.21 no.4
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• pp.73-84
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• 2021

In this paper, we propose an analysis method that categorizes the Core that essential behaviors in game, the Primary that solves the game problem, and the Secondary that helps the Core and the Primary. The proposed method can analyze the genre similarity and characteristics of the game, the richness of the content, and the proficiency level of the game. case study were conducted to confirm whether the analysis items were consistent with the objective game experience. The results of this study are expected to be helpful in improving game design ability.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• Archives of Craniofacial Surgery
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• v.24 no.4
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• pp.145-158
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• 2023

Vascular anomalies encompass a variety of malformations and tumors that can result in severe morbidity and mortality in both adults and children. Advances have been made in the classification and diagnosis of these anomalies, with the International Society for the Study of Vascular Anomalies establishing a widely recognized classification system. In recent years, notable progress has been made in genetic testing and imaging techniques, enhancing our ability to diagnose these conditions. The increasing sophistication of genetic testing has facilitated the identification of specific genetic mutations that help treatment decisions. Furthermore, imaging techniques such as magnetic resonance imaging and computed tomography have greatly improved our capacity to visualize and detect vascular abnormalities, enabling more accurate diagnoses. When considering reconstructive surgery for facial vascular anomalies, it is important to consider both functional and cosmetic results of the procedure. Therefore, a comprehensive multidisciplinary approach involving specialists from dermatology, radiology, and genetics is often required to ensure effective management of these conditions. Overall, the treatment approach for facial vascular anomalies depends on the type, size, location, and severity of the anomaly. A thorough evaluation by a team of specialists can determine the most appropriate and effective treatment plan.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.389-398
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• 2006

The present study compositely applied the self-organizing map (SOM), which is a kind of artificial neural networks (ANNs), and the back propagation algorithm (BPA) for the rainfall-runoff prediction model taking account of the irregular variation of the spatiotemporal distribution of rainfall. To solve the problems from the previous studies on ANNs, such as the overestimation of low flow during the dry season, the underestimation of runoff during the flood season and the persistence phenomenon, in which the predicted values continuously represent the preceding runoffs, we introduced SOM theory for the preprocessing in the prediction model. The theory is known that it has the pattern classification ability. The method proposed in the present research initially includes the classification of the rainfall-runoff relationship using SOM and the construction of the respective models according to the classification by SOM. The individually constructed models used the data corresponding to the respectively classified patterns for the runoff prediction. Consequently, the method proposed in the present study resulted in the better prediction ability of runoff than that of the past research using the usual application of ANNs and, in addition, there were no such problems of the under/over-estimation of runoff and the persistence.