• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.433-438
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• 2016

This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.295-300
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• 2021

This study aims to present new chest AP examination exposure conditions through a study on the effect on image quality and patient dose by applying high tube voltage and scatter ray post-processing software during chest AP examination in digital radiography equipment. This study was used a human body phantom and in the chest AP position, the dosimeter was placed horizontally at the thoracic spine 6. The experiment was conducted by dividing into a low tube voltage (70 kVp, 400 mA, 3.2 mAs) group and a high tube voltage (100 kVp, 400 mA, 1.2 mAs) group. The collimation size (14″× 17″) and the source to image receptor distance(110 cm) were same applied to both groups. Radiation dose was presented to dose area product and entrance surface dose. Image quality was compared and analyzed by comparing the difference between the signal-to-noise ratio and the contrast-to-noise ratio of the image according to the application of the scatter ray post-processing software under each condition. The average value of the entrance surface dose in the low and high tube voltage conditions was 93.04±0.45 µGy and 94.25±1.51 µGy, which was slightly higher in the high tube voltage condition, but the dose area product was 0.97±0.04 µGy and 0.93±0.01 µGy. There was a statistically significant difference in the group mean value(p<0.01). In terms of image quality, the values of the signal-to-noise ratio and the contrast noise ratio were higher in the high tube voltage than in the low tube voltage, and decreased when the scattering line post-processing function was used, but the contrast resolution was improved. If there is a scatter ray post-processing function during chest AP examination, it is helpful to actively utilize it to improve the image quality. However, when this function is not available, I thought that applying a higher tube voltage state than a low tube voltage state will help to realize images with a large amount of information without changing the dose.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Analytical Science and Technology
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• v.35 no.3
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• pp.93-115
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• 2022

Potential impurities in pharmaceuticals could be produced during manufacture, distribution, and storage and affect quality and safety of pharmaceuticals. In particular, highly reactive impurities could result in carcinogenic (mutagenic) effects on human body. International Conference on Harmonisation (ICH) has provided M7(R1) guideline for "Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk" and recommended an adoption of this guideline to the authorities. ICH M7(R1) guideline provides classification, accepted intakes, and controls of potential impurities in pharmaceuticals. However, since appropriate and unified analytical methods for impurities in pharmaceuticals have not been provided in this guideline, most potential impurities in pharmaceuticals are still difficult to manage and supervise by pharmaceutical companies and regulatory authorities, respectively. In this review, we briefly described definition of unintended mutagenic impurities, basic information in ICH M7(R1) guideline, and analytical methods to determine potential impurities. This review would be helpful to manage and supervise potential impurities in pharmaceuticals by pharmaceutical companies and regulatory authorities.

• Clean Technology
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• v.28 no.2
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• pp.123-130
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• 2022

Nanoparticles are used in various fields such as chemistry, medicine, the environment, and information and communication. With the increasing use of engineered nanomaterials, exposure to nanoparticles is expected to increase in the workplace and the environmental media. However, while nanotechnology industries are expanding, research on the exposure assessment of nanomaterials to humans and the environment is only at a beginning stage. Especially, if nanoparticles with a size of 100 nm or less that are contained in nano-products are released unintentionally, they may pose potential risks to the human body through breathing or skin exposure. Therefore, in this work, the possibility of potential exposure of nanoparticles moving from the laboratory to the office was confirmed, and nanoparticle safety guidelines are proposed. A nano-collector was used to detect nanoparticles in the atmosphere, and through use of a scanning mobility particle sizer it was found that nanoparticle concentrations in the laboratory and the office tended to be similar. On the assumption that nanoparticles attached to a lab-coat move out of the laboratory, a lab-coat to which nanocarbon black was attached was shaken and the concentration of the remaining particles on the lab-coat determined. The results confirmed that sufficient amounts of nanoparticles attached to the lab-coat could move from the laboratory to the office along the path of a researcher; thus, safety guidelines for the handling of lab-coat nanoparticles are required.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.215-220
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• 2023

Network technology used as a physical interface to retrieve, store, and exchange data is leading the era of data capitalism in the 21st century. The capacity of network technology dominates almost all communication in everyday life, and makes social understanding and experiences in the physical world visible in cyberspace. The movements of human bodies and objects in cyberspace are placed in a social context. This paper paid attention to these phenomena and examined the cases of activism that raised real problems through cyberspace. In particular, the focus of the study is the digital activism of the Electronic Disturbance Theater, which combines critical art and thinking for democracy with the realm of information and demonstrates aesthetic imagination. The first chapter of the main body briefly outlines the meaning activism as a social movement in cyberspace. The second chapter looks back on the alternatives of <FloodNet>, which represents the early activism performance of EDT. And then in the last chapter, the poetic significance of the <Transborder Immigrant Tool> is analyzed. Through this process, this paper demonstrates that the activism performance of the EDT is a critical aesthetics that encourages imagination for alternatives. It also argues that Electronic Disturbance Theater has contemporary value as an avant-garde art that actively utilizes the medium of network technology and integrates performance art and politics.

• Journal of Advanced Technology Convergence
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• v.3 no.1
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• pp.51-58
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• 2024

The reality of this study is that, in accordance with the development trend of human body art, art makeup and semi-permanent makeup are emerging as promising industries in the beauty industry among beauticians, and awareness of professional skill improvement is gradually increasing over time. Accordingly, interest in semi-permanent makeup has increased not only among beauticians who specialize in beauty industry or learn semi-permanent makeup, but also at beauty academies where they learn many beauty techniques, and this trending technology has been promoted at international beauty competitions by holding skill competitions for beauty technicians who specialize in semi-permanent techniques. As a venue for exchanging information about education, it is expected that synergistic effects such as dissemination of the education system can be expected. Korea's rapid industrial development has brought about great changes in the supply and demand of professional and detailed skilled manpower and in the formation of manpower in terms of technical level according to industrial development, and the skills and professional skills of beauty beauticians have improved due to the excellence of the professional education qualifications of beauticians and high skill evaluations. This had a significant impact on self-development and led to a re-recognition of the importance of efforts to achieve skilled skills.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.12
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• pp.1560-1570
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• 2007

The purpose of this study was to investigate the differences according to lifestyle in anthropometric measurement, dietary attitude, health-related behaviors and nutrient intake among the college students. The subjects were 994 nation-wide college students (male: 385, female: 609) and divided into 7 clusters (PEAO: passive economy/appearance-oriented type, NCPR: non-consumption/pursuit of relationship type, PTA: pursuit of traditional actuality type, PAT: pursuit of active health type, UO: utility-oriented type, POF: pursuit of open fashion type, PFR: pursuit of family relations type). A cross-sectional survey was conducted using a self administered questionnaire, and the data were collected via Internet or by mail. The nutrient intake data collected from food record were analyzed by the Computer Aided Nutritional Analysis Program. Data were analyzed by a SPSS 12.0 program. Average age of male and female college students were 23.7 years and 21.6 years, respectively. Most of the college students had poor eating habits. In particular, about 60% of the PEAO group has irregularity in meal time. The students in PAH and POF groups showed significantly higher consumption frequency of fruits, meat products and foods cooked with oil compared to the other groups. As for exercise, drinking and smoking, there were significant differences between PAH and the other groups. Asked for the reason for body weight control, 16.2% of NCPR group answered "for health", but 24.8% of PEAO group and 26.3% of POF group answered "for appearance". Calorie, vitamin A, vitamin $B_2$, calcium and iron intakes of all the groups were lower than the Korean DRIs. Female students in PTA group showed significantly lower vitamin $B_1$ and niacin intakes compared to the PFR group. Therefore, these results provide nation-wide information on health-related behaviors and nutrient intake according to lifestyles among Korean college students.

• Journal of Korean Home Economics Education Association
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• v.25 no.3
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• pp.19-38
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• 2013

This study focuses on the differences in general self-concept, academic self-concept, significant others self-concept and emotional-physical self-concept in relation to appearance management behavior. It goes on to show that appearance management behaviors such as styles in clothing, makeup, skin care, hair care, cosmetic surgery and body shaping, weight control management are strongly influenced by self-concept. Therefore, this study was carried out with the aim of providing basic understanding and information on the appearance management behavior of middle school students. It was also done in an effort to find ways of improving the self-concept of students through education as a part of the domestic science curriculum. The results obtained in this study are as follows: On average, the middle school students who took part in this study showed low self-concept and appearance management behavior which indicates a negative image of themselves. This suggests that efforts need to be made so that students can see themselves in a positive way and improve their self-concept through appearance management behavior. Middle school students with a positive self-concept try to present themselves by keeping their skin clean and their hair attractive. They express their self-esteem and personality through fashion and by keeping and maintaining their clothing, shoes and bags. They also tend to show a positive attitude towards their studies and are more likely to understand and get along with others. The students who showed positive attitudes towards their bodies and emotions have a higher interest in clothing and try to express the image that they want for themselves. They are also less likely to change their bodies unnaturally through cosmetic surgery and body shaping. Appropriate appearance management behavior can help middle school students see themselves in a more positive way.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.