• Proceedings of the KIEE Conference
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• 2001.07c
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• pp.1484-1486
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• 2001

In this study, The underwater object recognition using ultrasonic sensor fabricated with porous PZT-Polymer 3-3 type composites and invariant moment vector and SOFM(Self Organizing Feature Map) neural networks are presented. The recognition rates for the training data and the testing data were 98% and 94%, respectively.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2330-2332
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• 2000

In this study, 3-D underwater object recognition using ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites and invariant moment vector and SOFM(Self Organizing Feature Map) neural networks are presented. The recognition rates for the training data and the testing data were 99% and 93%, respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.39-47
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• 2016

This study is a smart tourism research based on face recognition applied system that manages individual information of foreign tourists to smartphone. It is a way to authenticate by using face recognition, which is biometric information, as a technology applied to identification inquiry, immigration control, etc. and it is designed so that tourism companies can provide customized service to customers by applying algorism to smartphone. The smart tourism system based on face recognition is a system that prepares the reception service by sending the information to smartphone of tourist service company guide in real time after taking faces of foreign tourists who enter Korea for the first time with glasses attached to the camera. The smart tourism based on face recognition is personal information recognition technology, speech recognition technology, sensing technology, artificial intelligence personal information recognition technology, etc. Especially, artificial intelligence personal information recognition technology is a system that enables the tourism service company to implement the self-promotion function to commemorate the visit of foreign tourists and that enables tourists to participate in events and experience them directly. Since the application of smart tourism based on face recognition can utilize unique facial data and image features, it can be beneficially utilized for service companies that require accurate user authentication and service companies that prioritize security. However, in terms of sharing information by government organizations and private companies, preemptive measures such as the introduction of security systems should be taken.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.3
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• pp.135-146
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• 2017

Entrepreneurship based on valuable opportunities has a positive effect on long-term economic growth. This study examines the entrepreneurship education offered to small business entrepreneurs from the perspective of opportunity-driven entrepreneurship in order to suggest improvements and future direction for small business entrepreneurship education. Also, we tried to investigate entrepreneurial behaviors related to opportunity development more effectively by considering entrepreneurial self-efficacy and regulatory focus that play a crucial role in opportunity development process as mediating variables. Conducted was an online survey of small business owners nationwide who have completed entrepreneurship education provided by the government, and 345 online survey data were used for empirical analysis. The results show that entrepreneurship education for small business entrepreneurs positively influences entrepreneurial self-efficacy. Entrepreneurial self-efficacy is positively related to both opportunity recognition and evaluation as well as the promotion and prevention focus. Entrepreneurship education, which includes theoretically and practically oriented courses, has a positive impact on both the promotion and prevention focus. The promotion focus of small business entrepreneurs was found to have a positive effect on opportunity recognition, but not on opportunity evaluation. Also, the preventive focus of small business entrepreneurs does not have a significant effect on both opportunity recognition and evaluation. The current entrepreneurship education for small business entrepreneurs does not have a significant impact on their opportunity recognition and evaluation. These results suggest that the current entrepreneurship education for small business entrepreneurs needs improvements to promote opportunity-driven entrepreneurship.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.763-774
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• 2015

Hand posture recognition has been a wide region of applications in Human Computer Interaction and Computer Vision for many years. The problem arises mainly due to the high dexterity of hand and self-occlusions created in the limited view of the camera or illumination variations. To remedy these problems, a hand posture recognition method using 3-D point cloud is proposed to explicitly utilize 3-D information from depth maps in this paper. Firstly, hand region is segmented by a set of depth threshold. Next, hand image normalization will be performed to ensure that the extracted feature descriptors are scale and rotation invariant. By robustly coding and pooling 3-D facets, the proposed descriptor can effectively represent the various hand postures. After that, SVM with Gaussian kernel function is used to address the issue of posture recognition. Experimental results based on posture dataset captured by Kinect sensor (from 1 to 10) demonstrate the effectiveness of the proposed approach and the average recognition rate of our method is over 96%.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.7
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• pp.821-826
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• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

• Journal of Korean Dental Hygiene Science
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• v.6 no.2
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• pp.161-172
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• 2023

Background: The purpose of this study is to assess the degree of self-awareness, recognition of others, and adaptation to college life of dental hygiene students, and to identify the factors affecting college life adjustment. Methods: The effect size was calculated using the G*power 3.1.9.4 program, based on the data of 191 people who agreed to participate in the questionnaire. Independent samples t-test, one way ANOVA, Post HOC Scheffe, correlation analysis, and multiple regression analysis were conducted to explore the difference and correlation between self-awareness, perception of others, and college life adjustment according to general characteristics. Results: A significant positive correlation was found in self-awareness (r=.301). The highest positive correlations were with social adjustment in both private (r=.340) and public self-awareness (r=.331) (p<0.01). Recognition of others (r=-.051) showed a negative correlation with college life adjustment, though it was not statistically significant. As factors for adapting to college life, private self-awareness (β=.232) and public self-awareness (β=.254) had a positive (+) effect, and internal self-awareness (β=-.205) was found to have a negative (-) effect (p<0.01) Conclusion: The self-awareness and recognition of others among dental hygiene students influence their smooth adjustment to college life. Improvement measures are recommended to enhance adaptability to college life and develop core competencies related to their major

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.254-255
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• 2010