• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4484-4492
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• 2011

The purpose of this study was to analyze dental laboratory's self health recognition level and health behavior level and to examine its correlation. 250 dental technicians in Seoul, Incheon, and Jeonbuk area were selected. Survey was carried out from October 11, 2010 to November 25, 2010 by using self-administered questionnaire. The following are the analytical results in the collected data. The average in the self- health recognition was indicated to be high with 2.83, The average of health behavior was indicated to be high with 2.76. As for correlation between self health recognition and health behavior, most of variables were indicated to have reverse correlation, it was concluded that higher health behavior likely led to lower self health recognition. From the result of this study, the work environment of dental technician must be improved and do more Practice of Health Behavior to improve and maintain their health.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.478-484
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• 2018

The choice of the motion features influences the result of the human action recognition method directly. Many factors often influence the single feature differently, such as appearance of the human body, environment and video camera. So the accuracy of action recognition is restricted. On the bases of studying the representation and recognition of human actions, and giving fully consideration to the advantages and disadvantages of different features, the Dense Trajectories(DT) algorithm is a very classic algorithm in the field of behavior recognition feature extraction, but there are some defects in the use of optical flow images. In this paper, we will use the improved Dense Trajectories(iDT) algorithm to optimize and extract the optical flow features in the movement of human action, then we will combined with Support Vector Machine methods to identify human behavior, and use the image in the KTH database for training and testing.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.87-94
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• 2018

This study was conducted to determine the recognition and behavior of female workers on irradiated food. A total of 326 surveys have been analyzed. The results of the study showed that the level of knowledge was 3.411 points and the level of recognition was 2.542 points. There was a significant difference in purchase of irradiated food by level of education, affiliation, and job type; in consumption of irradiated food by affiliation, level of education, job type and work experience; in preventive behavior of irradiated food by affiliation, work experience and job type. In a correlation analysis on the factors of irradiated food, those with more knowledge and better recognition had more experience of purchase and consumption. However, preventive behavior had no significant correlation with knowledge, recognition, purchase and consumption. Therefore, for a rational selection of irradiated food, it is important to provide information and safety training on them.

• Korean journal of food and cookery science
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• v.26 no.5
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• pp.521-529
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• 2010

The purpose of this study was to research healthy food behavior and food recognition for each Asian country after subjects had visited Asian restaurants. The subjects of the study were university students from Griffith university and Queensland university, Australia. The survey was conducted from June 1 to 28, 2010. The summary of the analysis is as follows. Firstly, for dietary behavior related to healthy food, 'average' was the most common answer at 41.0% (102 respondents). Regarding the standard of selecting healthy food, 'if it is good for health' was the most common answer, regarding the reasons to like healthy food, 'because it is good for health', was the most common, and for information about healthy food, 'obtain from TV or media' was the most common. Regarding eating healthy food at home or dining out, most respondents answered 'once or twice a week', whereas regarding thinking of eating healthy food while dining out, 'average' was the most common answer. Secondly, the recognition of six Asian cuisines were ranked in the order of Chinese, Japanese, Korean, Thai, Indian, and Vietnamese. Representative well-being food by country, Bibimbap of Korea, Sushi of Japan, Shark's Fin of China, Tom Yum Kung of Thailand, Curry of India and Goi Cuon of Vietnam were selected. Thirdly, regarding recognition of well-being food, disease effect factor, health-oriented factor, nutrition factor and vegetarian diet factor were extracted. We found that disease effect factor and nutrition factor had positive (+) effects on visiting Asian restaurants due to recognition of well-being foods. Therefore, it is expected that more local people will eat at Asian restaurants if the public relations for Asian restaurants emphasizes harmony between well-being food and Asian food.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.465-477
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• 2018

The purpose of this study is how it affect them what the effect of skin recognition on the purchasing behavior and propensity to buy facial cleanser in 311 women in their 20s and 50s. This study analyzed by importance, interest, and satisfaction of skin recognition, and type of impulse buying, type of depending on brand, type of planning buying. The group with high interest in skin recognition had a long time to clean. As they got a purchasing information the group with low knowledge had the information from nearby, and the group with high knowledge got information from internet. At the view of purchasing propensity the women who are highly interested in the skin have a tendency of type of impulse buying and type of planning buying, and the women with high skin importance are less inclined to type of impulse buying. In conclusion, Skin recognition uses purchasing behavior and propensity to buy facial cleanser.

• Journal of Korean Academy of Nursing
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• v.13 no.3
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• pp.1-33
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• 1983

The role and function of nursing care nowadays, tend to acquire sophisicated technology because specialization has expended due to increase of the medical population and the improvement of national health standards. To implement nursing care independently as a professional nurse, the apprehension of specific knowledge and skill should be acquired during basic nursing education. So it is important for nursing education not only to include theory and actual techniques, but also to strengthen the practical training in the actual clinical setting. This study was carried out with the following objectives; 1. To survey the detailed content and frequency of actual nursing students display during their clinical training. 2. To investigate the detailed content and frequency of actual nursing behavior which students display in each clinical a area. 3. To identify the motive for selection of nursing as their major and to determine the degree of self confidence, extent of knowledge and recognition of nursing responsibility. 4. To observe the relationship between actual nursing behavior and each of the following; 1) Motive for selecting nursing as a major 2) Self confidence 3) Knowledge of nursing care 4) Recognition of nursing responsibility The conclusions of this study were as follows; 1. Among the detailed nursing behavior which junior nursing college students carry out in clinical training; taking respiration's showed the highest frequency, and taking body temperatures, blood pressures, and pulses and making beds were next in frequency in this order. 2. In detailed nursing behaviors according to clinical area; taking vital signs showed the highest frequency in the emergency room, pediatric ward, orthopedic ward, general surgical ward and internal medicine ward. However, in the operating room, assisting with endotracheal tube insertion and sterile techniques were showen to have the highest frequencies. In nursery, umbilical cord care and the measurement of body weight were the highest in frequency In neurosurgical ward, the measurement of vital signs, changing position and tracheostomy care were the highest in frequency. In obstetric and gynecological ward and in the delivery room, checking duration, intensity and frequency of contractions was the highest in frequency. 3. In regard to the motive for majoring in nursing, the aptitude and interest of the student had the highest percentage(32.86%), and self-confidence in nursing activities (M=3.36), knowledge in nursing activities.(M=3. 09), and the recognition of the nursing activity (M= 3.76) wire in the middle range. 4. When the detailed nursing behaviors were compared with motive, self confidence, knowledge and recognition, it was found that when the nursing behavior was difficult and regarding much endeavor although the motive was high, the frequency of the nursing behavior was rather low. But in the cases in which there was much self confidence and a high level of skill was required, nursing behavior was carried more frequently. When there was muck self confidence and skill was not required, the frequency of nursing behavior was rather low. In the cases of a high level of knowledge, the frequency of nursing behavior was low and when recognition for nursing behavior was given the frequency of nursing behavior was low.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.47-54
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• 2022

Recently, with the development of deep learning technology, research on recognizing human behavior is in progress. In this paper, a study was conducted to recognize risky behaviors that may occur in a single-person household environment using deep learning technology. Due to the nature of single-person households, personal privacy protection is necessary. In this paper, we recognize human dangerous behavior in privacy protection video with Gaussian blur filters for privacy protection of individuals. The dangerous behavior recognition method uses the YOLOv5 model to detect and preprocess human object from video, and then uses it as an input value for the behavior recognition model to recognize dangerous behavior. The experiments used ResNet3D, I3D, and SlowFast models, and the experimental results show that the SlowFast model achieved the highest accuracy of 95.7% in privacy-protected video. Through this, it is possible to recognize human dangerous behavior in a single-person household environment while protecting individual privacy.

• The Research Journal of the Costume Culture
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• v.16 no.3
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• pp.533-546
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• 2008

This study aims on observing the recognition and attitude of caring for outer appearance by deducing the factors of men caring for their outer appearance at current point where male position is being emphasized in the appearance related market and their interest for outer appearance care is increasing. As a result of conducting a survey, male appearance caring behavior was distinguished into fashion, skin and cosmetic, cosmetic surgery, physical image, and hair factors, and among them, physical image factor showed the highest average, which proves that men generally have positive attitude towards caring for their looks. Concerning skin and cosmetics, the results show that interest is high regardless of age and occupation, and the lower the age is, the higher average for fashion, cosmetic surgery, and hair factors, proving that young men have more interest for caring after their looks. However, there was difference in outer appearance caring behavior pursued or favored by each age bracket according to the higher average for physical image in the age bracket higher than 30. Also, each factor of outer appearance caring behavior turned out to have significant correlation to each other.