• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.86-86
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• 2018

인공재배한 매실나무겨우살이를 이용한 기능성 막걸리를 개발하기 위해, 겨우살이, 기장과 쌀을 활용하여 원료의 배합비율 및 발효방법에 따른 총 폴리페놀, 플라보노이드 함량 및 DPPH free radical 소거능을 비교하였다. 최적의 배합비율을 찾기 위해 백미 100%, 백미와 기장 7:3, 5:5, 3:7 및 기장 100%의 비율로 막걸리를 제조하였다. 발효방법은 Shaking incubator($30{\sim}35^{\circ}C$, 100 rpm, 19 days), Hand Shaking($21^{\circ}C$, per 12hr, 19days)하였고, 발효가 끝난 막걸리는 80 mesh 망으로 여과하였다. 총 폴리페놀 함량은 Machine shaking과 Hand shaking 모두 쌀과 기장의 비율 7:3에 겨우살이를 10% 첨가한 막걸리가 각각 $795.83{\mu}g/ml$, $757.87{\mu}g/ml$로 가장 높았고, 기장 100% 막걸리가 각각 $503.73{\mu}g/ml$, $435.3{\mu}g/ml$로 가장 낮았다. 총 플라보노이드 함량은 Machine shaking, Hand shaking 모두 쌀과 기장의 비율이 7:3에 겨우살이를 10% 첨가한 막걸리가 $93.48{\mu}g/ml$, $84.56{\mu}g/ml$로 가장 높았고, 기장 100% 막걸리가 각각 $37.75{\mu}g/ml$, $21.86{\mu}g/ml$로 가장 낮았다. DPPH free radical 소거능은 겨우살이를 10% 첨가한 막걸 리가 가장 높았는데 Machine shaking은 평균 92.08%, Hand shaking은 평균 91.63%로 가장 높았으며 통계적으로 유의한 차이가 없었다. Machine shaking은 쌀과 기장의 비율이 7:3일 때, 73.37%, Hand shaking은 기장 100% 막걸리가 64.03%로 가장 낮았다. 결론적으로, 총 폴리페놀 및 플라보노이드 함량은 기장의 비율이 증가함에 따라 감소하는 경향을 보였고, DPPH free radical 소거능은 겨우살이를 첨가한 모든 막걸리에서 비교적 높았다. 발효방법은 Machine shaking이 Hand shaking 보다 총 폴리페놀 및 플라보노이드 함량, DPPH free radical 소거능이 비교적 높아 발효방법으로 더 적합하였다.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.139-159
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• 2022

As interest in health functional foods has increased since COVID-19, the importance of imported food safety inspections is growing. However, in contrast to the annual increase in imports of health functional foods, the budget and manpower required for inspections for import and export are reaching their limit. Hence, the purpose of this study is to propose a machine learning model that efficiently detects unsuitable food suitable for the characteristics of data possessed by government offices on imported food. First, the components of food import/export inspections data that affect the judgment of nonconformity were examined and derived variables were newly created. Second, in order to select features for the machine learning, class imbalance and nonlinearity were considered when performing exploratory analysis on imported food-related data. Third, we try to compare the performance and interpretability of each model by applying various machine learning techniques. In particular, the ensemble model was the best, and it was confirmed that the derived variables and models proposed in this study can be helpful to the system used in import/export inspections.

• Journal of the Korean Society of Physical Medicine
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• v.3 no.4
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• pp.235-246
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• 2008

Purpose : The purpose of this study was to evaluate the effects of indoor horseback-riding machine(SLIM $RIDER^{(R)}$) exercise on balance of the elderly with dementia. Methods : Subjects over 65 years of age in the nursing home were divided into three groups : Alzheimer's dementia group(n=7), vascular dementia group(n=6), and general elderly group(n=6). All groups(n=19) practiced indoor horseback-riding machine exercise for 20 min a day, three days a week during 6 weeks, and their balance were evaluated at before and 2, 4, 6 weeks after intervention, using the BPM. The level of statistical significance was .05. Results : After the 4weeks indoor horseback-riding machine exercise, balance was significantly increased in the all groups(p<.05). Conclusion : Indoor Horseback-riding machine exercise had a positive effect on subjects' balance.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.1-7
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• 2021

With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.81-90
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• 2023

Climate change has resulted in increased frequency and intensity of heat waves, which poses a significant threat to the health and safety of construction workers, particularly those engaged in labor-intensive and heat-stress vulnerable working environments. To address this challenge, this study aimed to propose an interpretable machine learning approach for forecasting personal heat strain by considering the cumulative effect of heat exposure as a situational variable, which has not been taken into account in the existing approach. As a result, the proposed model, which incorporated the cumulative working time along with environmental and personal variables, was found to have superior forecast performance and explanatory power. Specifically, the proposed Multi-Layer Perceptron (MLP) model achieved a Mean Absolute Error (MAE) of 0.034 (℃) and an R-squared of 99.3% (0.933). Feature importance analysis revealed that the cumulative working time, as a situational variable, had the most significant impact on personal heat strain. These findings highlight the importance of systematic management of personal heat strain at construction sites by comprehensively considering the cumulative working time as a situational variable as well as environmental and personal variables. This study provided a valuable contribution to the construction industry by offering a reliable and accurate heat strain forecasting model, enhancing the health and safety of construction workers.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.213-220
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• 2015

This research was study the accuracy of three-dimensional stereotactic breast biopsy, using a core Needle Biopsy and to assess the accuracy of Stereotactic biopsy and Sono guided biopsy. Using Stereotactic QC phantom to measure the accuracy of the 3D sterotactic machine. CT Scan and equipment obtained in the measured X, Y, Z and compares the accuracy of the length. Using Agar power phantom compare the accuracy of the 3D sterotactic machine and 2D ultrasound machine. Z axis measured by the equipment to compare the accuracy and reliability. Check the accuracy by using visual inspection and Specimen Medical application phantom. The accuracy of the 3D sterotactic machine measured by Stereotactic QC phantom was 100%. Accuracy as compared to CT, all of X, Y, Z axis is p > 0.05. The accuracy of the two devices was 100% as measured by Agar powder phantom. There was no difference between t he t wo d evices as C T and p > 0.05. 3D sterotactic machine of the ICC was 0.954, 2D ultrasound machine was 0.785. 2D ultrasound machine was different according to the inspector. Medical application phantom experiments in 3D sterotactic machine could not find the Sliced boneless ham. 2D ultrasound machine has not been able to find a small chalk powder group. The reproducibility of the three-dimensional stereotactic breast biopsy was better than effect of Sono guided biopsy.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.257-258
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• 2017

Modern society tends to bring excessive labor to people and, therefore, further health management is required. In this paper, by using the clustering technique, one of machine learning methods, we try to bring out the measure of fatigue from heart rate (HR) variation during daytime work, helping people to get high-quality of healthy and calm life.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.