• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.53-59
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• 2008

Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.784-788
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• 2008

In this research, we propose a remote speech input unit, a new method of user-friendly speech input in speech recognition system. We focused the user friendliness on hands-free and microphone independence in speech recognition applications. Our module adopts two algorithms, the automatic speech detection and speech enhancement based on the microphone array-based beamforming method. In the performance evaluation of speech detection, within-200msec accuracy with respect to the manually detected positions is about 97percent under the noise environments of 25dB of the SNR. The microphone array-based speech enhancement using the delay-and-sum beamforming algorithm shows about 6dB of maximum SNR gain over a single microphone and more than 12% of error reduction rate in speech recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1413-1415
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• 2004

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1101-1104
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• 1995

Through welding fabrication, user can feel an surficaial and capable unsatisfaction because of welded defects, Generally speaking, these are called weld defects. For checking these defects effectively without time loss effectively, weldability estimation system setup isan urgent thing for detecting whole specimen quality. In this study, by laser vision camera, catching a rawdata on welded specimen profiles, treating vision processing with these data, qualititative defects are estimated from getting these information at first. At the same time, for detecting quantitative defects, whole specimen weldability estimation is pursued by multifeature pattern recognition, which is a kind of fuzzy pattern recognition. For user friendly, by weldability estimation results are shown each profiles, final reports and visual graphics method, user can easily determined weldability. By applying these system to welding fabrication, these technologies are contribution to on-line weldability estimation.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.321-326
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• 2014

The needs for IT accessibility for disabled persons has increased for recent years. So, it is very important to support multi-modal interfaces, such as voice and vision recognition, TTS, etc. for disabled persons. In this paper, we deal with IT accessibility issues of home networks and show our implemented home network control system model with multi-modal interfaces including voice recognition and animated user interfaces.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2930-2932
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• 2000

In this paper. a robust gesture recognition system is designed and implemented to explore the communication methods between human and computer. Hand gestures in the proposed approach are used to communicate with a computer for actions of a high degree of freedom. The user does not need to wear any cumbersome devices like cyber-gloves. No assumption is made on whether the user is wearing any ornaments and whether the user is using the left or right hand gestures. Image segmentation based upon the skin-color and a shape analysis based upon the invariant moments are combined. The features are extracted and used for input vectors to a radial basis function networks(RBFN). Our "Puppy" robot is employed as a testbed. Preliminary results on a set of gestures show recognition rates of about 87% on the a real-time implementation.

• ETRI Journal
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• v.34 no.3
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• pp.466-469
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• 2012

In this letter, we propose a dual autostereoscopic display platform employing a natural interaction method, which will be useful for sharing visual data with users. To provide 3D visualization of a model to users who collaborate with each other, a beamsplitter is used with a pair of autostereoscopic displays, providing a visual illusion of a floating 3D image. To interact with the virtual object, we track the user's hands with a depth camera. The gesture recognition technique we use operates without any initialization process, such as specific poses or gestures, and supports several commands to control virtual objects by gesture recognition. Experiment results show that our system performs well in visualizing 3D models in real-time and handling them under unconstrained conditions, such as complicated backgrounds or a user wearing short sleeves.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.209-214
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• 2020

VoIP technology has been widely used for exchanging voice or image data through IP networks. VoIP technology, often called Internet Telephony, sends and receives voice data over the RTP protocol during the session. However, there is an exposition risk in the voice data in VoIP using the RTP protocol, where the RTP protocol does not have a specification for encryption of the original data. We implement programs that can extract meaningful information from the user's dialogue. The meaningful information means the information that the program user wants to obtain. In order to do that, our implementation has two parts. One is the client part, which inputs the keyword of the information that the user wants to obtain, and the other is the server part, which sniffs and performs the speech recognition process. We use the Google Speech API from Google Cloud, which uses machine learning in the speech recognition process. Finally, we discuss the usability and the limitations of the implementation with the example.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.39-42
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• 2023

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.954-961
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• 2018

In this paper, a chatbot for emotional stress recognition and management using rule-based method and NLP is designed and developed to tackle various emotional stresses of people through questionnaire. For this, Dialogflow as open chatbot development platform and Facebook messenger as chatting platform are used. We can build natural and resourceful conversational experiences through predefined questions by using powerful tools of Dialogflow, and can use developed chatbot on the Facebook page messenger. Developed chatbot perceives emotional stresses of user by user-input which is either text or choice of predefined answer. It also gives user questions according to the user's feeling, and assess the strength of the emotional stresses, and provide a solution to the user. Further research can improve the developed chatbot by using open Korean NLP library and database of emotions and stresses.