• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.453-458
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• 2016

One of the most common applications of machine vision is quality inspections in automated production. In this study, a welding inspection system that is controlled by a PC and a PLC equipped with a multi-camera setup was developed. The system was designed to measure the primary dimensions, such as the length and width of the welding areas. The TCP/IP protocols and multi-threading techniques were used for parallel control of the optical components and physical distribution. A coaxial light was used to maintain uniform lighting conditions and enhance the image quality of the weld areas. The core image processing system was established through a combination of various algorithms from the OpenCV library. The proposed vision inspection system was fully validated for an actual weld production line and was shown to satisfy the functional and performance requirements.

• Textile Coloration and Finishing
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• v.25 no.3
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• pp.223-231
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• 2013

This research has a main focus on providing fundamental data for on-the-spot industrial fields by comparing and contrasting physical properties of fluffy spun-like material. The fluffy spun-like yarn is developed as fluffy yarn similar to natural spun-like yarn by treating polyester(FDY and + type shaped DTY) with ATY machine. In this experiment, using ATY machine for raw material texturing, we produced two fluffy yarns: (i) + type shaped(50d/36f, DTY) as core yarn and 100d/192f FDY as effect yarn[ATY(D)], (ii) FDY(75/36) as core yarn and 100d/192f FDY [ATY(F)] as effect yarn. After producing thous yarns, we twisted them with 500T/M, 700T/M, 1000T/M, respectively. produced yarns through this process were used as the samples for this experiment. Even though the shrinkage of fluffy yarn ATY(F) and ATY(D) becomes high as treated temperature rises and treated time lengthens, it is more affected by treated temperature then by treated time. In this experiment, produced fluffy yarn[ATY(D)] shows a little high values for temperature, but almost same values for higher temperatures. When we compare ATY(F) with ATY(D) fluffy yarn shows more natural fluffy yarn surface structure like natural cotton. The shrinkage of 700T/M twisted ATY(D) fluffy yarn show about 11% under treated temperature $180^{\circ}C$ and treated time 30min, and about 7% under $120^{\circ}C$ and 30min, respectively. But the shrinkage of 1000T/M fluffy yarn shoes about 9% and 6% under same conditions. Regarding treated time, tenacity and initial modulus of ATY(D) fluffy yarn rise high until 30min, but do not show much increase above 30min. Regarding treated temperature, tenacity and initial modulus of it rise high aboyer $140^{\circ}C$.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.188-196
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• 2005

Accident reports from 1991 to 2000 of a steel company were used to analyze which factors induce industrial accidents. The subjects were 8,311 blue-collar workers, who sustained 114 occupational injuries and work on a continuous full-day 3-team 3-shift system of backward rotation(mornings to afternoons to nights). With respect to marital status, the occupational injury rate(OIR) on the married workers was significantly higher compared to unmarried workers. With respect to no, the OIR of those in their early 20s was significantly higher when compared to other age groups. The OIR of highly educated workers showed a reduction when compared with lower educated workers. The OIR of shift workers were significantly higher compared with the daytime workers. The OIR of type of work decreased across the steel manufacturing process, rolling process, machine maintenance section forwarding products section to the field management section. With respect to the block of shift work(morning, afternoon, and night shifts) by the type of work, the OIR of the night shift was higher than those of the morning shift in the steel manufacturing process or forwarding products section. The OIR of the machine maintenance section was slightly higher in the morning shift than those of the night shift. The OIR of the time of day of the shift workers reached a peak between 09:00 and 11:00. The OIR of a slight injury to shift workers decreased across the night, afternoon, to morning shifts whereas the OIR of a serious injury tended to decrease across the night, morning, to afternoon shift. The body parts most commonly injured were the arm and the crus.

• Advances in aircraft and spacecraft science
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• v.3 no.3
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• pp.351-366
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• 2016

Macroperforations improve the sound absorption performance of porous materials in acoustic cavities and in waveguides. In an acoustic cavity, enhanced noise reduction is achieved using porous materials having macroperforations. Double porosity materials are obtained by filling these macroperforations with different poroelastic materials having distinct physical properties. The locations of macroperforations in porous layers can be chosen based on cavity mode shapes. In this paper, the effect of variation of macroporosity and double porosity in porous materials on noise reduction in an acoustic cavity is presented. This analysis is done keeping each perforation size constant. Macroporosity of a porous material is the fraction of area covered by macro holes over the entire porous layer. The number of macroperforations decides macroporosity value. The system under investigation is an acoustic cavity having a layer of poroelastic material rigidly attached on one side and excited by an internal point source. The overall sound pressure level (SPL) inside the cavity coupled with porous layer is calculated using mixed displacement-pressure finite element formulation based on Biot-Allard theory. A 32 node, cubic polynomial brick element is used for discretization of both the cavity and the porous layer. The overall SPL in the cavity lined with porous layer is calculated for various macroporosities ranging from 0.05 to 0.4. The results show that variation in macroporosity of the porous layer affects the overall SPL inside the cavity. This variation in macroporosity is based on the cavity mode shapes. The optimum range of macroporosities in poroelastic layer is determined from this analysis. Next, SPL is calculated considering periodic and nodal line based optimum macroporosity. The corresponding results show that locations of macroperforations based on mode shapes of the acoustic cavity yield better noise reduction compared to those based on nodal lines or periodic macroperforations in poroelastic material layer. Finally, the effectiveness of double porosity materials in terms of overall sound pressure level, compared to equivolume double layer poroelastic materials is investigated; for this the double porosity material is obtained by filling the macroperforations based on mode shapes of the acoustic cavity.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.359-373
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• 2019

Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.161-167
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• 2020

In the process of cutting large aircraft parts, the tool may be abnormally worn or damaged due to various factors such as mechanical vibration, disturbances such as chips, and physical properties of the workpiece, which may result in deterioration of the surface quality of the workpiece. Because workpieces used for large aircrafts parts are expensive and require strict processing quality, a maintenance plan is required to minimize the deterioration of the workpiece quality that can be caused by unexpected abnormalities of the tool and take maintenance measures at an earlier stage that does not adversely affect the machining. In this paper, we propose a method to indirectly monitor the tool condition that can affect the machining quality of large aircraft parts through real-time monitoring of the current signal applied to the spindle motor during machining by comparing whether the monitored current shows an abnormal pattern during actual machining by using this as a reference pattern. First, 30 types of tools are used for machining large aircraft parts, and three tools with relatively frequent breakages among these tools were selected as monitoring targets by reflecting the opinions of processing experts in the field. Second, when creating the CNC machining program, the M code, which is a CNC auxiliary function, is inserted at the starting and ending positions of the tool to be monitored using the editing tool, so that monitoring start and end times can be notified. Third, the monitoring program was run with the M code signal notified from the CNC controller by using the DAQ (Data Acquisition) device, and the machine learning algorithms for detecting abnormality of the current signal received in real time could be used to determine whether there was an abnormality. Fourth, through the implementation of the prototype system, the feasibility of the method proposed in this paper was shown and verified through an actual example.

• Journal of Adhesion and Interface
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• v.23 no.1
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• pp.8-16
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• 2022

In this study, self-healable waterborne polyurethane (SH-WPU) as shoes and coating materials with self-healable disulfide functionalities was synthesized by mixing polyether polyol to impart excellent durability and heat resistance and polycarbonate polyol to impart excellent mechanical properties. The synthesized SH-WPU was characterized by fourier transform-infrared spectroscopy (FT-IR), and physical and self-healing properties were confirmed through universal testing machine (UTM) and scanning electron microscope (SEM) measurements. Tensile strength and hardness were increased and elongation was decreased by using polycarbonate polyol. In addition, as a result of comparison of thermal properties, thermal stability has been increased as the content of polycarbonate polyol increased. The healing efficiency showed the highest efficiency when poly(tetramethylene ether)glycol : polycarbonate polyol = 0.75 : 0.25, and it was confirmed that the damaged part was healed through surface observation using a microscope and SEM.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.17-23
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• 2023

In this study, the thermoluminescence kinetics of electron trap in Li₆Y_0.5Gd_0.5(BO₃)₃ (LY_0.5G_0.5BO) scintillator for neutron detection composed of Li, Gd, and B with a high neutron response cross-section were investigated. The thermoluminescence glow curve of the LY_0.5G_0.5BO scintillation single crystal was measured and analyzed using the peak shape method, the initial rise method, and the machine learning algorithm to evaluate the physical parameters of the electron trap. The glow curve of the LY_0.5G_0.5BO scintillation single crystal consisted of a single peak. As a result of analyzing this peak, the activation energy, emission order, and frequency factor of the electron trap were 0.61 eV, 1.1, and 1.7×10⁷ s^-1, respectively. In addition, the possibility of thermoluminescence analysis of scintillators using machine learning was confirmed.