• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.481-483
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• 1989

This paper is concentrated on the recognition of Korean Digits. The speech signals of each of digits are fed into computer through the 18 bandpass filters, AD converter. Spectrum input data are analyzed and used. BASIC program language is used for recognition performance and the result of recognition is outputed to computer screen and printer. In this paper, the strength and weakness of filter-bank analysis method is described and the technique of real-time recognition is argued. In this experiment, Ratio of recognition for speaker dependent recognition was about 97% and recognition time was also satisfied. Therefore, A way of speaker independent recognition will be presented and using for special communication in the future.

• 전자통신동향분석
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• 제33권3호
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• 대한통합의학회지
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• 제10권1호
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• pp.119-128
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• 2022

Purpose : The purpose of this study was to examine the effects of applying occupational therapy that uses motion recognition on the physical self-efficacy, visual-motor integration ability, and play skills of children who have neurodevelopmental disorder before and after treatment. Methods : This The study chose 16 children with neurodevelopmental disorder as research subjects who were randomly and evenly allocated into an experimental group and a control group. The experiment followed a pretest-posttest design. As an intervention, the experimental group received motion recognition-based occupational therapy and a separate sensory integration program. The control group only participated in the separate sensory integration program. The eight-week experiment duration included 24 intervention sessions where the a 50-minute session was implemented three times a week for eight weeks. To compare the physical self-efficacy, visual-motor integration ability, and play skills before and after the intervention, measurement tools including the Physical self efficacy, Beery VMI-6, and Penn interactive peer play scale were used. All measured variables were analyzed and expressed as mean, standard deviation and percentage. Results : The motion recognition-based occupational therapy demonstrated a significant effect on improving the physical self-efficacy, visual-motor integration ability, and play skills of the experimental group. The intervention also caused a significant difference between the experimental group and control group in terms of the physical self-efficacy, visual-motor integration ability, and play skills. Conclusion : We confirmed the possibility motion recognition-based occupational therapy could be effective in improving the physical self-efficacy, visual-motor integration ability, and play skills for patients who have neurodevelopmental disorder. Based on the study result, further future studies are expected based on this study result that prove the application effect of the motion recognition-based occupational therapy using disabled and non- disabled children as subjects are expected in the future.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 하계종합학술대회 논문집(3)
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• pp.29-32
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• 2001

In this paper, we applied the high-performance PGAP(Parallel Genetic Algorithm Processor) to recognizing rotated pattern. In order to perform this research efficiently, we used Multi-PGAP system consisted of four PGAP. In addition, we used mental rotation based on the rotated pattern recognition mechanism of human to reduce the number of operation. Also, we experimented with distinguishing specific pattern from similar coin patterns and determine rotated angle between patterns. The result showed that the development of future artificial recognition system is feasible by employing high performance PGAPS.

• 만화애니메이션 연구
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• 통권35호
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• pp.347-374
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• 2014

본 논문은 증강현실 기술을 사용한 21C 영화들의 영화적 상상과 증강현실 예술의 예술적 상상을 중심으로 증강현실 콘텐츠 산업화의 미래에 관하여 연구하였다. 영화에서는 영화적 상상을 통해 미래의 기술을 보여주는 동시에 미래상을 제시하는 역할을 하고, 증강현실 예술은 기술적으로 구현할 수 있는 현재의 기술을 통해 미래 예술과 사회의 거시적인 문화를 포착하는 역할을 하고 있다. 조사된 영화들을 증강현실 기술별로 분류한 결과, 제스처 인식은 투명 디스플레이어 관련 기술과 연계하여 발전하고, 홀로그램 기술은 개별화된 커뮤니케이션 방식으로 변화되며, 생체인식은 다중 생체인식으로의 발전 가능성이 있고, 웨어러블 컴퓨터는 물리적인 신체증강 측면으로도 발전하여 산업화될 것으로 전망된다. 증강현실 예술에서 아바타의 다양한 활용은 홀로그램과 관련이 있고, 인체의 생체 현상의 활용은 생체인식으로 연계되며, 현실세계와 가상세계의 혼합은 제스처 인식을 통해 디스플레이어를 활용하고, HMD의 새로운 시도는 웨어러블 컴퓨터의 다변화와 함께 산업화될 것으로 보인다. 영화와 예술에서 상상과 구현을 통해 나타난 증강현실 콘텐츠는 인간 생활의 조력자 역할을 담당하고, 동시에 인간과 환경 간에 매개 없이 융화되는 방식으로 산업화되는 미래상을 제시한다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.155-162
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• 2021

With the global COVID-19 pandemic, the tourism sector and all consumption have contracted with the untact era. Wine will also be sold and developed in various ways non-face-to-face in the future. Therefore, it is necessary to develop apps and web servers that focus on health in the era of single-person households and non-face-to-face. This study used facial recognition data based on photos of adult men and women in their 40s and 50s to analyze the Sasang constitution through a mobile app and web server, and suggested wine recommendations suitable for their constitution. First, the user's body information is entered. And through the facial recognition mobile app, recommend the right wine after analyzing the body type. if it's not like the first recommended wine, it is configured to receive another wine recommendation. In the future, the number of single-person households will increase further, and in the age of well-being, wine recommendations that fit my body will be useful. Wine recommendation suitable for Sasang constitution will be a useful mobile application to manage personal healt

• ETRI Journal
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• 제36권5호
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• pp.696-703
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• 2014

In this paper, we present a walking guidance system for the visually impaired for use at subway stations. This system, which is based on environmental knowledge, automatically detects and recognizes both exit numbers and arrow signs from natural outdoor scenes. The visually impaired can, therefore, utilize the system to find their own way (for example, using exit numbers and the directions provided) through a subway station. The proposed walking guidance system consists mainly of three stages: (a) sign detection using the MCT-based AdaBoost technique, (b) sign recognition using support vector machines and hidden Markov models, and (c) three verification techniques to discriminate between signs and non-signs. The experimental results indicate that our sign recognition system has a high performance with a detection rate of 98%, a recognition rate of 99.5%, and a false-positive error rate of 0.152.

• 보건의료산업학회지
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• 제9권3호
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• pp.267-276
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• 2015

Objectives : In this study, we were to investigated the recognition of the hospital services by college students with variable majors. Methods : In survey questionnaire included were 9 general questions, 18 items recognition of hospital services in questionnaire. For the statistical analysis of this study, we were used the IBM SPSS 21.0; t-test, ANOVA and multiple regression analysis were performed. Results : (1) The general characteristics of perception was as follows; recognition of hospital services in accordance with grade showed significant differences.(F=2.638, p<0.05) (2) Effect of the recognition was analyzed from the general characteristics; F value is 2.638(p<0.05) and 3.678(p<0.01) as shown; the regression model showed a significant expression. Conclusions : In this study, we observed the perception of the hospital service by college students. As a result, we could not see a statistically significant difference. These results can be interpreted as a show the direction of health education in the future.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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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.