• Journal of Biomedical Engineering Research
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• v.42 no.1
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• pp.25-30
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• 2021

Objective: The aim of this study is to consider the effect of skin tissue necrosis by improving blood flow in animal skin models for low frequency pulsed electromagnetic fields (LF_PEMF) stimulation. Methods: Twenty rats (Wistar EPM-1 male, 280-320 g) were randomly divided into control groups (n=10) and the PEMF groups (n=10). To induce necrosis of the skin tissue, skin flap was treated in the back of the rat, followed by isolation film and skin flap suturing. Subsequently, the degree of necrosis of the skin tissue was observed for 7 days. The control group did not perform any stimulation after the procedure. For the PEMF group, LF_PEMF (1 Hz, 10 mT) was stimulated in the skin flap area, for 30 minutes a day and 7 days. Cross-polarization images were acquired at the site and skin tissue necrosis patterns were analyzed. Results: In the control group, skin tissue necrosis progressed rapidly over time. In the PEMF group, skin tissue necrosis was slower than the control group. In particular, no further skin tissue necrosis progress on the day 6. Over time, a statistically significant difference from the continuous necrosis progression pattern in the control group was identified (p<0.05). Conclusions: It was confirmed that low frequency pulsed electromagnetic fields (LF_PEMF) stimulation can induce relaxation of skin tissue necrosis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.93-106
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• 2020

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.27-33
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• 2021

With increasing interest in key areas of the 4th industrial revolution such as artificial intelligence, deep learning and big data, scientific approaches have developed in order to overcome the limitations of traditional decision-making methodologies. These scientific techniques are mainly used to predict the direction of financial products. In this study, the factors of apartment prices, which are of high social interest, were analyzed through SOM. For this analysis, we extracted the real prices of the apartments and selected a total of 16 input variables that would affect these prices. The data period was set from 1986 to 2021. As a result of examining the characteristics of the variables during the rising and faltering periods of the apartment prices, it was found that the statistical tendencies of the input variables of the rising and the faltering periods were clearly distinguishable. I hope this study will help us analyze the status of the real estate market and study future predictions through image learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1874-1881
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• 2021

Black ice, which occurs mainly on the road, vehicle traffic bridges and tunnel entrances due to the sub-zero temperature due to the slip of the road due to heavy snow, is not recognized because the image of asphalt is transmitted in the driver's view, so the vehicle loses braking power because it causes serious loss of life and property. In this paper, we propose a method to identify the black ice by using infrared camera and to identify the road condition by using deep learning to compensate for the disadvantages of existing black ice detection methods (artificial satellite imaging, checking the pattern of slip by ultrasonic reception, measuring the temperature of the road surface, and checking the difference in friction force of the tire during vehicle driving) and to reduce the size of the sensor to detect black ice.

• Archives of Craniofacial Surgery
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• v.24 no.4
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• pp.167-173
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• 2023

Background: Mandibular split fractures, in which the fracture occurs exclusively in the posterior wall, are uncommon. This study aimed to enhance clinicians' understanding of mandibular split fractures and offer insights for future research. Methods: This study included six patients who visited our hospital between January 2020 and June 2023 and were diagnosed with mandibular split fractures. We retrospectively collected data from patients' medical records on their age, sex, symptoms, mechanism, impact site, associated injuries, and treatment method, as well as the location, pattern, and number of fractures observed on computed tomography (CT) and panoramic images. The frequency of split fractures among all mandibular fractures was calculated. Results: The six patients included three men (50%) and three women (50%), ranging in age from 20 to 71 years (mean age, 49.8 years). The split fractures were located in the symphysis in one patient (16.7%), symphysis to parasymphysis in two patients (33.3%), parasymphysis in one patient (16.7%), and parasymphysis to the body in two patients (33.3%). Four patients (66.7%) had condylar head fractures, while two patients (33.3%) had single split fractures. The mechanism of trauma was a slip-down incident in four cases (66.7%), while two cases (33.3%) were caused by motorcycle traffic accidents. Four patients (67%) underwent intermaxillary fixation, while two patients (33%) improved with conservative treatment. Split fractures were diagnosed in all six patients on CT, whereas the fracture line was not clearly visible on panoramic images. Mandibular split fractures accounted for 5.6% of all mandibular fractures. Conclusion: This study provides insights into the clinical characteristics of rare mandibular split fractures and the diagnostic imaging findings. Furthermore, CT scans and three-dimensional image synthesis-instead of panoramic images-may be essential for accurately diagnosing mandibular fractures, including mandibular split fractures, in the future.

• Journal of the Korean Geotechnical Society
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• v.22 no.7
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• pp.5-12
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• 2006

In this study, 2D shallow tunnel model test using close range photogrammetric technique was conducted with aluminium rods simulating continuum granular material. Numerical analysis was also carried out in order to identify the behaviour of subsurface deformations caused by shallow tunnelling. Direction and magnitude of displacement vectors from the model test were identical to the result of numerical analysis based on the model data. In particular, it is shown that the vector direction was toward a point below the tunnel invert level. A narrow "chimney or tulip like" pattern of vertical displacement was confirmed by both the model test and numerical analysis. This behaviour is consistent with the field data. In addition to the qualitative comparison, the quantitative result of subsurface settlements according to 2D volume loss showed good agreement between the model test and numerical analysis. Therefore, close range photogrammetric technique applied in the model test may be used to validate the result from the continuum numerical analysis.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.101-111
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• 2022

This study analyzed the relationship between water turbidity and its reflectance, as measured using hyperspectral imaging. First, samples of turbid water were generated in boxes. This was followed by drone-based hyperspectral imaging and analysis of the correlation between the samples' measured turbidity and hyperspectral reflectance. The nine boxes for turbidity measurement were made of black acrylic that absorbed all light turbidity was induced using soil collected near Changhacheon, which causes turbidity in Imha Lake. The results indicate that the reflectance of wavelengths in the near-infrared region followed a pattern of increase with increasing soil content for each box. Analysis of this correlation between the turbidity and average reflectance measured in each box yielded a very high R² value of 0.8702, indicating that reflectance is a suitable proxy for turbidity.

• Imaging Science in Dentistry
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• v.53 no.2
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• pp.109-115
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• 2023

Purpose: The aim of this study was to evaluate changes in the trabecular bone through texture analysis and compare the texture analysis characteristics of different areas in patients with medication-related osteonecrosis of the jaw (MRONJ). Materials and Methods: Cone-beam computed tomographic images of 16 patients diagnosed with MRONJ were used. In sagittal images, 3 regions were chosen: active osteonecrosis(AO); intermediate tissue (IT), which presented a zone of apparently healthy tissue adjacent to the AO area; and healthy bone tissue (HT) (control area). Texture analysis was performed evaluating 7 parameters: secondary angular momentum, contrast, correlation, sum of squares, inverse moment of difference, sum of entropies, and entropy. Data were analyzed using the Kruskal-Wallis test with a significance level of 5%. Results: Comparing the areas of AO, IT, and HT, significant differences (P<0.05) were observed. The IT and AO area images showed higher values for parameters such as contrast, entropy, and secondary angular momentum than the HT area, indicating greater disorder in these tissues. Conclusion: Through texture analysis, changes in the bone pattern could be observed in areas of osteonecrosis. The texture analysis demonstrated that areas visually identified and classified as IT still had necrotic tissue, thereby increasing the accuracy of delimiting the real extension of MRONJ.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.53-69
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• 2023

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002-October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.