• Clinical and Experimental Pediatrics
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• v.50 no.12
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• pp.1165-1172
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• 2007

Self/non-self discrimination and unresponsiveness to self is the fundamental properties of the immune system. Self-tolerance is a state in which the individual is incapable of developing an immune response to an individual's own antigens and it underlies the ability to remain tolerant of individual's own tissue components. Several mechanisms have been postulated to explain the tolerant state. They can be broadly classified into two groups: central tolerance and peripheral tolerance. Several mechanisms exist, some of which are shared between T cells and B cells. In central tolerance, the recognition of self-antigen by lymphocytes in bone marrow or thymus during development is required, resulting in receptor editing (revision), clonal deletion, anergy or generation of regulatory T cells. Not all self-reactive B or T cells are centrally purged from the repertoire. Additional mechanisms of peripheral tolerance are required, such as anergy, suppression, deletion or clonal ignorance. Tolerance is antigen specific. Generating and maintaining the self-tolerance for T cells and B cells are complex. Failure of self-tolerance results in immune responses against self-antigens. Such reactions are called autoimmunity and may give rise to autoimmune diseases. Development of autoimmune disease is affected by properties of the genes of the individual and the environment, both infectious and non-infectious. The host's genes affect its susceptibility to autoimmunity and the environmental factors promote the activation of self-reactive lymphocytes, developing the autoimmunity. The changes in participating antigens (epitope spreading), cells, cytokines or other inflammatory mediators contribute to the progress from initial activation to a chronic state of autoimmune diseases.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.80-88
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• 1994

In the automation of rigid parts mating process with the intelligent robots, Peg-In-Hole is the most available task since inserting is some analytic and needs suitable range of forces that can be controlled by induatrial manipulators. In this Peg-In-Hole process, it is very important to identify the contact state between tow parts, peg and hole, to build the strategies for robot motion that leads to avoid the jamming condition occurs during insertion process. In this paper, we adpopted 3 parameters for identification, lFzl, lFxy/Fzl, and lMxy/Fxyl, derived from axes value of Whitney's jamming diagram. Also, we defined the fuzzy membership functions for these parameters and developed the identification algorithm based on fuzzy inference method of max-product. As an experimental result, we obtained about 96% of identification ratio that could be raised up to industrial requirements by further research.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Journal of KIISE
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• v.41 no.11
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• pp.927-934
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• 2014

In this paper, we propose an effective method for recognizing daily human activities from a stream of three dimensional body poses, which can be obtained by using Kinect-like RGB-D sensors. The body pose data provided by Kinect SDK or OpenNI may suffer from both the view variance problem and the scale variance problem, since they are represented in the 3D Cartesian coordinate system, the origin of which is located on the center of Kinect. In order to resolve the problem and get the view-invariant and scale-invariant features, we transform the pose data into the spherical coordinate system of which the origin is placed on the center of the subject's hip, and then perform on them the scale normalization using the length of the subject's arm. In order to represent effectively complex internal structures of high-level daily activities, we utilize Hidden state Conditional Random Field (HCRF), which is one of probabilistic graphical models. Through various experiments using two different datasets, KAD-70 and CAD-60, we showed the high performance of our method and the implementation system.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.77-87
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• 2013

In this paper, we present a novel method for local image descriptor called exact order based descriptor (EOD) which is robust to illumination changes and Gaussian noise. Exact orders of image patch is induced by changing discrete intensity value into k-dimensional continuous vector to resolve the ambiguity of ordering for same intensity pixel value. EOD is generated from overall distribution of exact orders in the patch. The proposed local descriptor is compared with several state-of-the-art descriptors over a number of images. Experimental results show that the proposed method outperforms many state-of-the-art descriptors in the presence of illumination changes, blur and viewpoint change. Also, the proposed method can be used for many computer vision applications such as face recognition, texture recognition and image analysis.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.621-625
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• 2010

This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.97-103
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• 2001

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. Although the ML method has good performance, it dose not take account into discrimination to other words. To complement this problem, a word verification method by re-recognition of the recognized word and its similar word using the discriminative function of the two words. To find the similar word, the probability of other words to the HMM is calculated and the word showing the highest probability is selected as the similar word of the mode. To achieve discrimination to each word the weight to each state is appended to the HMM parameter. The weight is calculated by genetic algorithm. The verificator complemented discrimination of each word and reduced the error occurred by similar word. As a result of verification the total error is reduced by about 22%

• Research in Community and Public Health Nursing
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• v.17 no.2
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• pp.242-252
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• 2006

Purpose: This study was to identify the state of recognition and experience on love, sex knowledge and self-esteem in youths who attended middle and high schools. Method: Data were collected from 785 Youths of those schools from Aug. 2004 to Nov. 2004. Collected data were analysed through $x^2-test$ and ANOVA. Result: The average flee of the subjects was $16.87{\pm}1.17$(girls) and $16.64{\pm}75$ (boys) years old, and 24.3% of them discussed sex with their parents. The youths' most frequent love experiences showed hand in hand(boys 73.6%, girls 80.8%), and followed by shoulder in shoulder(boys 60.4%, girls 68.5%), arm in arm(boys 57.6%, girls 67.8%), hug(boys 53.3%, girls 57.0%) and light kiss(boys 50.0%, girls 37.9%). There were differences in sexual experiences between boys and girls coitus and pregnancy in boys(23.6%, 5.8%) and girls( .5%, .3%). The scores of sex knowledge were 68.78(girls) and 62.50(boys), and self-esteem 61.05(boys) and 74.38(girls). Sex knowledge were related to gender, and self-esteem were related to sender, age, and discussion with their parents regarding sex. Conclusion: With the results above, majority of Youths were not a hindrance mostly about friendship and love expressions. Support and encouragement from school. home, and society are required so that eye-level sex education by age and positive self-esteem may be formed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.347-358
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• 2016

The paper presents a software architecture for a wearable device to measure vital signs with the real-time user's behavior recognition. Taking vital signs with a wearable device help user measuring health state related to their behavior because a wearable device is worn in daily life. Especially, when the user is running or sleeping, oxygen saturation and heart rate are used to diagnose a respiratory problems. However, in measuring vital signs, continuosly measuring like the conventional method is not reasonable because motion artifact could decrease the accuracy of vital signs. And in order to fix the distortion, a complex algorithm is not appropriate because of the limited resources of the wearable device. In this paper, we proposed the software architecture for wearable device using a simple filter and the acceleration sensor to recognize the user's behavior and measure accurate vital signs with the behavior state.