• International journal of advanced smart convergence
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• v.10 no.2
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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.

• The Korean Journal of Emergency Medical Services
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• v.6 no.1
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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.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.251-260
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• 2016

Face recognition is a technology to extract feature from a facial image, learn the features through various algorithms, and recognize a person by comparing the learned data with feature of a new facial image. Especially, in order to improve the rate of face recognition, face recognition requires various processing methods. In the training stage of face recognition, feature should be extracted from a facial image. As for the existing method of extracting facial feature, linear discriminant analysis (LDA) is being mainly used. The LDA method is to express a facial image with dots on the high-dimensional space, and extract facial feature to distinguish a person by analyzing the class information and the distribution of dots. As the position of a dot is determined by pixel values of a facial image on the high-dimensional space, if unnecessary areas or frequently changing areas are included on a facial image, incorrect facial feature could be extracted by LDA. Especially, if a camera image is used for face recognition, the size of a face could vary with the distance between the face and the camera, deteriorating the rate of face recognition. Thus, in order to solve this problem, this paper detected a facial area by using a camera, removed unnecessary areas using the facial feature area calculated via a Gabor filter, and normalized the size of the facial area. Facial feature were extracted through LDA using the normalized facial image and were learned through the artificial neural network for face recognition. As a result, it was possible to improve the rate of face recognition by approx. 13% compared to the existing face recognition method including unnecessary areas.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.359-362
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• 2016

Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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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.

• Proceedings of the KIEE Conference
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• 2004.11c
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.4
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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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• v.3 no.2
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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.

• Proceedings of the IEEK Conference
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• 2003.07e
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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)