• Annual Conference of KIPS
• /
• 2023.05a
• /
• pp.417-419
• /
• 2023

Hypertension is one of the leading causes of mortality worldwide. In recent years, the incidence of hypertension has increased dramatically, not only among the elderly but also among young people. In this regard, the use of machine-learning methods to diagnose the causes of hypertension has increased in recent years. In this study, we improved the prediction of hypertension detection using Mahalanobis distance-based multivariate outlier removal using the KNHANES database from the Korean national health data and the COVID-19 dataset from Kaggle. This study was divided into two modules. Initially, the data preprocessing step used merged datasets and decision-tree classifier-based feature selection. The next module applies a predictive analysis step to remove multivariate outliers using the Mahalanobis distance from the experimental dataset and makes a prediction of hypertension. In this study, we compared the accuracy of each classification model. The best results showed that the proposed MAH_RF algorithm had an accuracy of 82.66%. The proposed method can be used not only for hypertension but also for the detection of various diseases such as stroke and cardiovascular disease.

• Journal of the Microelectronics and Packaging Society
• /
• v.31 no.2
• /
• pp.45-53
• /
• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

• Earthquakes and Structures
• /
• v.27 no.3
• /
• pp.177-189
• /
• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.12
• /
• pp.637-644
• /
• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.216-230
• /
• 2023

Purpose: The purpose is to prevent accidents by predicting disasters through the analysis of near-miss. Method: In this study, a near-miss literature review and data were collected at construction sites, and a questionnaire survey was conducted to use logistic regression analysis and decision tree analysis to classify the possibility of near-miss connection. Result: As a result of analyzing the effects of near-miss types on mental, physical, and safety habits and behaviors, the factor with a high influence on the body is the need for near-miss management, the type of job is electricity·information communication, and health status in order, and the mental factor is the construction scale The influence was high, and the factors with the highest influence on the habit behavior factors were analyzed in the order of experience, number of serious injuries, and occupation in order of illusion, inappropriate work instructions, and body parts. Through decision tree analysis, factors and patterns that affect the possibility of a near-miss being a surprise accident were identified. Conclusion: Construction site officials consider the observation of near-miss and mentally and physically. Specific management of the relevance of physical aspects to near-miss should be implemented, and a work environment in which serious accidents are reduced is expected through personnel allocation, work plans, work procedures and methods, and feedback so that inappropriate work instructions do not lead to near-miss.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.69 no.2
• /
• pp.111-122
• /
• 2024

This study was conducted to evaluate agronomic traits and classify phylogenetic characteristics of Korean wheat landraces (KWLs) collected in Gyeongnam province. We used the squash method for chromosome observation, image analysis to examine seed characteristics, and genotyping using commercial single-nucleotide polymorphism chips to construct a phylogenetic tree. All KWLs contained 42 chromosomes and two pairs of microsatellites as observed in Keumgang, a Korean wheat cultivar. All KWLs showed smaller seed traits compared with those of Keumgang, although KWL-3 had a larger embryo length than that of Keumgang. Among agronomic traits compared with those of Keumgang, all KWLs had a late heading date and ripening period except for KWL-3, which showed the smallest culm and spike length. KWL-1 had the lowest tiller, highest floret, and grain number. All KWLs showed a lower thousand grain weight than that of Keumgang because of their smaller seeds. In the variation of variety and area, the heading date, ripening period, tiller number, and floret number were affected by the cultivation area, whereas the culm length, spike length, and 1000 grain weight were affected by the variety. Correlation distribution analysis showed differences in agronomic traits according to the cultivation area, and the heading date was positively correlated with the culm length and floret number in three cultivation areas. Principal component analysis explained that the heading date had a positive relationship with the ripening period and floret number and a negative relationship with the tiller number. Principal component analysis also revealed that all KWLs had a lower thousand grain weight than that of Keumgang. Phylogenetic tree showed that KWL-1 was near KWL-3, while KWL-2 was near KWL-4. All KWLs were genetically near the Korean wheat cultivars milsung and saeol, whereas they were genetically far from the Korean wheat cultivars goso and olgrue.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.507-516
• /
• 2019

The investigation of land characteristics is an important task for the calculation of officially land prices and standard comparison table of land price. Therefore, it should be done objectively and consistently. However, the current investigation system is mainly done by researcher's subjective judgment. Therefore, the objectivity and consistency of this investigation is not guaranteed and questionable. In this study, we first defined the problem by analyzing the current land topography investigation method. In addition, in order to investigate the land topography, the geometry of the parcel is quantified by spatial information and applied to the decision tree based method(C4.5) to produce the final result. This study intended to extract the parcel characteristics data of the topographic by the use of spatial information and to apply the information to the C4.5, there by suggesting a method for addressing the problems. The findings showed approximately 93.5% between the results of topography classification estimated with rules learned by C4.5.

• Journal of Korean Society of Forest Science
• /
• v.105 no.1
• /
• pp.48-62
• /
• 2016

This study was conducted to classify forest vegetation structure and stand feature of Mt. Deogyusan National Park from Hyangjeukbong to Jungbong, 48 plots were surveyed. The type classification of the vegetation structure was performed with Z-M phytosociological method. As a result, Quercus mongolica community group was classified into the Picea jezoensis community, Carpinus cordata community and Tilia amurensis community in community unit. P. jezoensis community was subdivided into Deutzia glabrata group and Viburnum opulus var. calvescens group in group unit. D. glabrata group was subdivided into Acer mandshuricum subgroup and Ribes mandshuricum subgroup and V. opulus var. calvescens group was subdivided into Hemerocallis dumortieri subgroup and Prunus padus subgroup in subgroup unit. In the result of estimating the importance value, it constituted Q. mongolica (23.9%), Abies koreana (14.7%), Taxus cuspidata (10.2%), P. jezoensis (8.2%) and Betula ermanii (7.4%) in tree layer. It constituted Acer komarovii (18.6%), Acer pseudosieboldianum (18.4%) and Q. mongolica (8.9%) in subtree layer. It constituted Rhododendron schlippenbachii (20.7%), A. pseudosieboldianum (17.4%) and Symplocos chinensis (8.5%) in shrub layer. Indicator species analysis of vegetation unit 1 was consisted of Hydrangea serrata, Fraxinus mandshurica and D. glabrata that species prefer moist valley in subalpine or rocks. In the results of analyzing the species diversity, vegetation unit 1, 4 and 5 represented that there were different and complex local distributions. As in the similarity between the vegetation units, the vegetation units 1, 2, 3 and 4 represented high with 0.5 or above. It represented that there wasn't no differences on composition species in vegetation units.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.42 no.1
• /
• pp.45-56
• /
• 2024

The purpose of this study was to suggest evaluation items and standards for diagnosing the growth status of protected trees designated and managed by the Korea Forest Service. The research results are as follows. First, based on the Cultural Heritage Administration's standards for evaluating the growth status of old trees, which are natural monuments, and related data, items related to the 'growth status of the above-ground part' of the trees were revised and supplemented. Simultaneously new items such as 'location', 'usability', 'artificial cover rate within the crown width', 'soil physical properties', and 'soil chemical properties' were discovered. By combining these items, six items were derived to evaluate the growth status of protected trees. Second, evaluation items made through visual inspection, such as 'tree vigor' and 'leaf color' in the 'growth status of the above-ground part', were replaced with quantifiable items such as measuring the electrical resistance value of the cambium or chlorophyll content. Third, 'artificial cover rate within crown width' was introduced as an item to evaluate the growth environment, and classification criteria for 'soil physical properties' and 'chemical properties' were presented. Fourth, a method to evaluate the health of protected trees was specified by combining 10 above-ground growth conditions, 3 growth environments, and 8 soil environment items. In addition, a record format for diagnosing the growth status was shaped up. The significance of this study is that it proposed an evaluation and recording method for protected trees, which do not have an evaluation system compared to natural monuments, but there were limitations in developing a method that takes into account the importance of each evaluation item. In order to overcome these, research should be conducted to evaluate effectiveness for each item and to replace qualitative evaluation of trees with quantitative evaluation based on scientific data.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.6
• /
• pp.1-14
• /
• 2019

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is important that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with unmanned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and effectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.