• International journal of advanced smart convergence
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• 제10권2호
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• pp.79-85
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• 2021

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of "numbers" and secondly, the recognition rate of "Ga Na Da Hangul" are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.

• 한국응급구조학회지
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• 제6권1호
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• pp.5-14
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• 2002

The purpose of the study was to compare on the cognition and attitudes toward the mentally ill person among EMT College Student before and after Psychiatric Nursing Course Work. The data was collected twice before and after Psychiatric Nursing course work during one semester 16-weeks from 71 EMT department Students. Used measurements were self-reported questionnaires about cognition and CAMI questionnaires about attitudes. Analysis of data was done by frequence, percentage and t-test with SAS program. The cognition was changed over positively after then before Psychiatric Nursing Course. Especially, It was answered that needed to learning, caring and curing for mental illness. The study of attitudes for mentally ill person was that authoritarianism, benevolence and social restrictiveness were changed over positively but community mental health ideology was not changed. In conclusion, follwing the results of this study, the psychiatric nursing course work was influenced very much to changing of attitudes and cognition toward mentally ill person. Accordingly, psychiatric nursing curriculum will be offered and psychiatric educators have to emphasize the understanding of attitudes and cognition toward mentally ill person.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권5호
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• pp.251-260
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• 2016

얼굴 인식은 얼굴 영상에서 특징을 추출하고, 이를 다양한 알고리즘을 통해 학습하여 학습된 데이터와 새로운 얼굴 영상에서의 특징과 비교하여 사람을 인식하는 기술로 인식률을 향상시키기 위해서 다양한 방법들이 요구되는 기술이다. 얼굴 인식을 위해 학습 단계에서는 얼굴 영상들로 부터 특징 성분을 추출해야하며, 이를 위한 기존 얼굴 특징 성분 추출 방법에는 선형판별분석(Linear Discriminant Analysis, LDA)이 있다. 이 방법은 얼굴 영상들을 고차원의 공간에서 점들로 표현하고, 클래스 정보와 점의 분포를 분석하여 사람을 판별하기 위한 특징들을 추출하는데, 점의 위치가 얼굴 영상의 화소값에 의해 결정되므로 얼굴 영상에서 불필요한 영역 또는 변화가 자주 발생하는 영역이 포함되는 경우 잘못된 얼굴 특징이 추출될 수 있으며, 특히 일반 카메라 영상을 사용하여 얼굴인식을 수행하는 경우 얼굴과 카메라간의 거리에 따라 얼굴 크기가 다르게 나타나 최종적으로 얼굴 인식률이 저하된다. 따라서 본 논문에서는 이러한 문제점을 해결하기 위해 일반 카메라를 이용하여 얼굴 영역을 검출하고, 검출된 얼굴 영역에서 Gabor Filter를 이용하여 계산된 얼굴 외곽선을 통해 불필요한 영역을 제거한 후 일정 크기로 얼굴 영역 크기를 정규화하였다. 정규화된 얼굴 영상을 선형 판별 분석을 통해 얼굴 특징 성분을 추출하고, 인공 신경망을 통해 학습하여 얼굴 인식을 수행한 결과 기존의 불필요 영역이 포함된 얼굴 인식 방법보다 약 13% 정도의 인식률 향상이 가능하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.51.5-51
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• 2002

In this paper, we propose and developed a keyglove using the touch-typing method as new solutions to the problem of text input into the mobile computing devices. This device recognizes that character is typed in though the hand's movements analysis and requires no additional space on a person's desktop or work surface, and can be easily used with computers of any size, even the smallest mobile computer, and is designed as an input device for wearable computers and virtual environment. The concept of the wearable input device based on human hand-molies recognition.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.359-362
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• 2016

얼굴은 홍채, 지문 등과 같은 사람마다 가진 특수한 정보이다. 얼굴 인식에 대한 연구들은 과거부터 현재까지 지속적으로 진행되고 있으며, 이러한 연구들을 통해 여러 가지의 얼굴 인식 방법들이 나타났다. 이 중에는 스테레오로 구성된 얼굴 데이터를 이용하여 얼굴 인식을 진행하는 알고리즘들이 있다. 본 논문에서는 기계학습의 방법인 Convolutional Neural Network를 이용하여 스테레오로 구성된 얼굴 이미지를 하나의 신경망으로 학습을 진행하였다. 또한 스테레오로 구성된 얼굴 이미지는 카메라 2대를 이용하여 취득하였다. 이 방법은 얼굴 인식에서 보편적으로 많이 사용되는 알고리즘인 PCA를 이용한 스테레오 얼굴 인식의 결과보다 더욱 좋은 성능을 보였다.

• 한국지능시스템학회논문지
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• 제17권6호
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• pp.813-818
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• 2007

This paper is concerned with the problem of recognizing definition sentences. Given a definition question like "Who is the person X?", we are to retrieve the definition sentences which capture descriptive information correspond variously to a person's age, occupation, of some role a person played in an event from the collection of news articles. In order to retrieve as many relevant sentences for the definition question as possible, we adopt a centroid based statistical approach which has been applied in summarization of multiple documents. To improve the precision and recall performance, the weight measure of centroid words is supplemented by using external knowledge resource such as Wikipedia and redundant candidate sentences are removed from candidate definitions. We see some improvements obtained by our approach over the baseline for 20 IT persons who have high document frequency.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.187-189
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• 2004

This paper presents the system widely used for extraction of human body recognition system in the field of bio-metric identification. The Human body recognition system is used in many fields. This biological is appled to the human recognition in banking and the access control with security. The important algorithm of the identification software usese hand lines and hand shape geometry. We used the simple algorithm and recognizing the person by their hand image from the input camera. The geometrical characteristics in hand shape such as length of finger to whole hand length thickness of finger to length, etc are used.

• 대한임베디드공학회논문지
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• 제4권4호
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• pp.190-194
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. Recently, using iris information is used in many fields such as access control and information security. But Perform complex operations to extract features of the iris. Because high-end hardware for real-time iris recognition is required. This paper is appropriate for the embedded environment using local gradient histogram embedded system with iris feature extraction methods based on USN(Ubiquitous Sensor Network). Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform noticeably improves recognition performance and it is noted that the processing time of the local gradient histogram transform is much faster than that of the existing method and rotation was also a strong attribute.

• Journal of International Society for Simulation Surgery
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• 제3권2호
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• pp.46-48
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• 2016

Purpose In face reocognition area, estimating occlusion in face images is on the rise. In this paper, we propose a new face recognition algorithm based on dual sparse representation to solve this problem. Method Each face image is partitioned into several pieces and sparse representation is implemented in each part. Then, some parts that have large sparse concentration index are combined and sparse representation is performed one more time. Each test sample is classified by using the final sparse coefficient where correlation between the test sample and training sample is applied. Results The recognition rate of the proposed algorithm is higher than that of the basic sparse representation classification. Conclusion The proposed method can be applied in real life which needs to identify someone exactly whether the person disguises his face or not.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)