• 대한안전경영과학회지
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• 제9권1호
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• pp.77-84
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• 2007

This study was planned to investigate what the main factor of the regulation compliance of inspection on harmful machine, instrument and equipment by industrial safety and health act is. This study subject was composed of three groups as employers, employees of manufacturing and using the harmful machine and safety inspectors. Manufacturing workplace were 236 places, using workplace were 201 places and the safety inspectors were 100 people. The study subject was sampled by stratified random sampling considering the type of harmful Machine. Data for analysis is collected from each sample using interview with structured questionnaires. Compliance is measured by 2, 3, and 4 point scale composed by 8 sub items such as general perception, understanding, clearness, necessity, relevancy, implementation, penalty, and general compliance of the regulation. The level of 8 items of employer's compliance are not differentiated among three groups. The determining factors for inspection observance of the workplace using the harmful Machine were understanding, penalty and cognized compliance. The determining factors for inspection observance of the workplace manufacturing the harmful Machine were understanding and object conformity. These results show that the strategy to adapt the regulated group to inspection regulation will be the elevation of understanding for regulation first of all.

• 한국산업보건학회지
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• 제15권1호
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• pp.19-26
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• 2005

In previous report, we presented that petroleum based solvents used in dry cleaning shop was almost similar to stoddard solvent defined by ACGIH and NIOSH, and the occupational exposure standard of stoddard solvent could be used in total exposure assessment of those solvents. The specific aim of the this study was to evaluate of the solvent exposure used in commercial dry cleaning shops by using occupational exposure standard of stoddard solvent. We conducted first survey of 8 self-employed dry cleaning shops and 5 factory type dry cleaning shops from July to August, and second survey of the same shops from October to November in 2002. The exposure concentration to the solvent during loading and unloading activity of vented dry cleaning machine was 489.2ppm(GM), 270.3ppm(GM), respectively, which was almost excursion limit(500ppm) of ACGIH, and exceed the ceiling limit(312ppm) of NIOSH. The time-weighted average (TWA) worker exposure to the solvent was 21.3ppm(GM) at self-employed shops, 20.7ppm(GM) at factory type shops on first survey, and 31.1ppm(GM), 33.7ppm(GM), respectively on second survey. The TWA exposure concentration of workers with spotting and cleaning machine operating job was 25.4ppm(GM), which was 2.9 times higher than the TWA exposure concentration, 8.8ppm(GM) of press workers. All TWA exposure concentrations was lower than OEL(100ppm) of stoddard solvent. We found that the most heavy exposure process at dry cleaning was loading, unloading process, and the vent of dry cleaning machine was the main emission source for workers exposure to petroleum based solvent.

• 대한임상약리학회:학술대회논문집
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• 대한임상약리학회 2006년도 제15차 추계학술대회
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• pp.37-38
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• 2006

• 월간산업보건
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• 통권198호
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• pp.21-24
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• 2004

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• 디지털콘텐츠학회 논문지
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• 제19권9호
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• pp.1819-1824
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• 2018

본 논문에서는 사용자의 스트레스 해소와 주의력 향상에 도움이 될 수 있는 ASMR(autonomous sensory meridian response) 콘텐츠 제어 기법을 제안한다. 제안된 기법은 뇌파 측정 디바이스로부터 EEG(electroencephalography), 집중도, 명상도, 눈 깜빡임 데이터를 측정하고 안드로이드 IoT(internet of things) 앱을 통해 oneM2M 표준을 준용한 IoT 서버 플랫폼으로 전송한다. 서버 플랫폼에 수집된 EEG, 집중도 및 명상도 데이터를 사용하여 사용자의 정신건강상태를 분류하기 위한 SVM(support vector machine) 모델을 생성하고, 이 모델을 통해 분류된 사용자의 정신건강상태와 눈 깜빡임 데이터에 따라 ASMR 콘텐츠를 제어한다. 데이터 사용형태에 따라 SVM 모델을 비교한 결과, 집중도와 명상도 데이터를 사용하는 SVM 모델이 85.7%의 정확도를 나타내었고 이 SVM 모델이 분류한 정신건강상태와 눈 깜빡임 데이터의 변화에 따라 ASMR 콘텐츠 제어 알고리즘이 정상적으로 동작하는 것을 확인하였다.

• Journal of Radiation Protection and Research
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• 제47권1호
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• pp.8-15
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• 2022

Background: We developed a machine vision technology program that tracks patients' real-time breathing and automatically analyzes their breathing patterns. Materials and Methods: To evaluate its potential for clinical application, the image tracking performance and accuracy of the program were analyzed using a respiratory motion phantom. Changes in the stability and regularity of breathing were observed in healthy adult volunteers according to whether the breathing pattern mirrored the breathing guidance. Results and Discussion: Displacement within a few millimeters was observed in real-time with a clear resolution, and the image tracking ability was excellent. This result was consistent even in the sections where breathing patterns changed rapidly. In addition, the respiratory gating method that reflected the individual breathing patterns improved breathing stability and regularity in all volunteers. Conclusion: The findings of this study suggest that this technology can be used to set the appropriate window and the range of internal target volume by reflecting the patient's breathing pattern during radiotherapy planning. However, further studies in clinical populations are required to validate this technology.

• Journal of Biosystems Engineering
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• 제25권1호
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• pp.55-62
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• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.

• ETRI Journal
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• 제45권4호
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• International journal of advanced smart convergence
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• 제13권3호
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• pp.80-88
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• 2024

Breast cancer remains a significant global health burden, necessitating accurate and timely detection for improved patient outcomes. Machine learning techniques have demonstrated remarkable potential in assisting breast cancer diagnosis by learning complex patterns from multi-modal patient data. This study comprehensively evaluates several popular machine learning models, including logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-nearest neighbors (KNN), XGBoost, and ensemble methods for breast cancer prediction using the Wisconsin Breast Cancer Dataset (WBCD). Through rigorous benchmarking across metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), we identify the naive Bayes classifier as the top-performing model, achieving an accuracy of 0.974, F1-score of 0.979, and highest AUC of 0.988. Other strong performers include logistic regression, random forests, and XGBoost, with AUC values exceeding 0.95. Our findings showcase the significant potential of machine learning, particularly the robust naive Bayes algorithm, to provide highly accurate and reliable breast cancer screening from fine needle aspirate (FNA) samples, ultimately enabling earlier intervention and optimized treatment strategies.