• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.494-507
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• 2021

This study conducted a regression analysis on 312 local government officials in Gyeonggi-do to verify the hypothesis on the relationship between job autonomy and organizational citizenship behavior. Path analysis was conducted to analyze the mediating effect of organizational fairness recognition and the moderating effect of self-efficacy. Looking at the analysis results, first, it was confirmed that the higher the degree of job autonomy perceived by the members of the organization, the more positive (+) organizational citizenship behavior was. Second, the partial mediating effect of procedural justice was confirmed in the relationship between autonomy and organizational citizenship behavior. On the other hand, the mediating effect of distributional justice was rejected in the relationship between autonomy and organizational citizenship behavior. Third, self-efficacy controlled the mediating role between job autonomy and organizational citizenship behavior in recognition of procedural justice. Fourth, it was analyzed that self-efficacy plays a controlling role between job autonomy and organizational citizenship behavior. The results of this study are expected to contribute to the development of organizational behavior theory through preceding factors of organizational citizenship behavior to improve the organizational performance of local public officials.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.910-917
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• 2007

Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper. a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process and recognition Phases. In the Pre-Process Phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. Experimental data was generated by six men employing three types of human touching behaviors including 'hitting', 'stroking' and 'tickling'. As the experimental result of a recognizer being generated for each user and being evaluated as cross-validation, the average recognition rate was 82.9% while the result of a single recognizer for all users showed a 74.5% average recognition rate.

• Journal of Digital Convergence
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• v.13 no.1
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• pp.525-533
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• 2015

This study presents a favoured pattern of makeup and psychology according to it by recognition type, through an examination of recognition for it in order to understand interest and awareness regarding it among female college students. The makeup is emotionally effective for confidence and positive thought, and is considered to be a category of social manner. The subjects can be grouped according to difference in recognition of makeup: contemplation, reception, observation and correspondence group. Although there is slight difference in the interest of four types of makeup depending on recognition difference, the behavior of makeup plays a large part in establishing personal relationships. The pattern of makeup favours natural style which places an emphasis on skin tone. The female college students in their 20s, who are at the center of current beauty trend market, were asked to conduct questionnaire of makeup, so interest and awareness of makeup behavior can be understand. If these findings are actively used in the beauty trend, various and effective beauty cultures are considered to be created.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.117-124
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• 2012

This study started why do consumers use the counterfeit goods. We executed the pretest through the depth interview, and made an purchase motivation scale, useful benefit factor and cost factor scale of counterfeit goods. We found out having the recognition difference of China consumers with Korea about these. The results is as follows. First, A counterfeit motivation appeared to the recognition difference appeared in a ostentation, economical efficiency and self-satisfaction factor with korea and china consumers. Second, counterfeit useful benefit factor organized to a personal significantly and economic benefit and the recognition difference appeared. Third, Counterfeit good useful cost composed to the personal good-quality, economic and social image of cost factors, and the recognition significantly difference appeared to all of cost factors with Korea and China consumers. These results are helpful for a depth understanding about the counterfeit use behavior, and will be utilized for the public campaign to reduce such behavior.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.254-263
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• 2019

This study was designed to understand the Education recognition, satisfaction, and job preferences of college students majoring in beauty through the DISC behavior type, and to be used as customized career and beauty education data considering individual tendencies through the DISC behaviour type. From September 20 to October 11, 2017, the final questionnaire was analyzed for students of beauty education who lived in South Chungnam Province and Gyeonggi Province. Significant differences in the Education recognition and job preferences of the study participants were shown, followed by D, I, S, and C, and were found to be satisfactory for their majors. Through this study, it is believed that it will be possible to identify the Education recognition, satisfaction and job preferences of college students in beauty education and use them as basic data that can help them decide their career path after beauty education.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1419-1427
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• 2012

This paper deals with the online privacy policy, which is designed to solve the information asymmetry problem between websites and internet users. We empirically analyze the recognition, confirmation of the online privacy policy, and its effects on online transaction behavior using a rich survey data representing 5,422 Korean internet users. Major results are as follows. First, there exists a significant difference between recognition and confirmation, and confirmation behavior is positively related with the importance of privacy issue and the experience of privacy invasion. Second, binary variable regressions show that internet user tends to participate in online transaction if he/she confirms the online privacy policy positively. Finally, if websites would make online privacy policy easy and short, a yearly online transaction market size of Korea would increase by 0.46 million participants and 22.4 billion KRW.

• The Korean Journal of Ecology
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• v.25 no.6
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• pp.365-369
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• 2002

We had 18 eggs artificially hatched in a mass breeding place of black-tailed gulls and examined the individual recognition between young siblings in a laboratory environment. The results of the experiment showed that the young gulls selectively responded to their siblings and non-siblings at an early stage after hatching. It was shown that they began to recognize the begging call among the voice signals of siblings and non-siblings 15-16 days after hatching, and the chirirah call 11-12 days after hatching. Also, more significant results were shown with the chirirah call than with the begging call. In an experiment of visual recognition between siblings and non-siblings, the young black-tailed gulls approached their siblings significantly 9-10 days after hatching. The recognition between young siblings in a mass breeding place provides an important evolutionary indicator in terms of their social behaviors.

• The Journal of Korea Robotics Society
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• v.6 no.2
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• pp.173-181
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• 2011

Recognition of traffic signs helps an unmanned ground vehicle to decide its behavior correctly, and it can reduce traffic accidents. However, low cost traffic sign recognition using a vision sensor is very difficult because the signs are exposed to various illumination conditions. This paper proposes a new approach to solve this problem using an illuminometer which detects the intensity of illumination. Using the intensity of illumination, the recognizer adjusts the parameters for image processing. Therefore, we can reduce the loss of information such as the shape and color of traffic signs. Experimental results show that the proposed method is able to improve the performance of traffic sign recognition in various weather and lighting conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.