• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.573-581
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• 1999

Although phonocardiography by auscultation has been used in diagnosis long time ago, recognition of heart sound was tried only restricted fields such as the first heart sound, the second heart sound, and specific valve operation for the purpose of analyzing local function or operation of heart and developments of heart sound recognition in full cycle are quite insignificant. in this paper, we proposed a recognition method which extracts features of heart sound in full cycle and classllies heart sounds This proposed recognition algorithm is based on detecting the first and second heart sounds in thme domain. The algorithm classifics heart sound into several classes by extracting the important time blocks and analyzing the peak position, integration values and statistical variables. Heart sounds are classified into normal, early systolic murmur, late systolic mumur, early diastolic murmur, late diastolie murmur, continuous murmur. We can verify our algorithm is useful from the results which show the average recognition rate of heart sounds is 88 perecnt. Recognition error was occurred mainly in early systolic murmur.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1139-1145
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• 2014

User recognition technology, which identifies or verifies a certain individual is absolutely essential under robotic environments for intelligent services. The conventional face recognition algorithm using single distance face image as training images has a problem that face recognition rate decreases as distance increases. The face recognition algorithm using face images by actual distance as training images shows good performance but this has a problem that it requires user cooperation. This paper proposes the LDA-based long distance face recognition method which uses multiple distance face images from a zoom camera for training face images. The proposed face recognition technique generated better performance by average 7.8% than the technique using the existing single distance face image as training. Compared with the technique that used face images by distance as training, the performance fell average 8.0%. However, the proposed method has a strength that it spends less time and requires less cooperation to users when taking face images.

• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.351-358
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• 2015

As smart phones with built-in sensors are developed, research on recognition using wearable devices is increasing. Existing papers are mostly limited on research to personal activity recognition. In this paper, we propose a method to recognize conceptual group activity. Before doing recognition, we generate new data based on the analysis of the conceptual group activity in a classroom. The study focuses on three activities in the classroom environment: Taking Lesson, Doing Presentation and Discussing. With the proposed algorithm, the recognition rate is over 96%. Using this method in real time will make it easy to automatically analyze the activity and the purpose of the classrooms. Moreover, it can increase the utilization of the classroom through the data analysis. Further research will focus on group activity recognition in other environments and the design of an group activity recognition system.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.1-8
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• 2009

There are a lot of influences, such as location of camera, luminosity, brightness, and direction of light, which affect the performance of 2-dimensional image recognition. This paper suggests an adaptive method for face-image recognition in noisy environments using evolvable filtering and feature extraction which uses one sample image from camera. This suggested method consists of two main parts. One is the environmental-adjustment module which determines optimum sets of filters, filter parameters, and dimensions of features by using "steady state genetic algorithm". The other another part is for face recognition module which performs recognition of face-image using the previous results. In the processing, we used Gabor wavelet for extracting features in the images and k-Nearest Neighbor method for the classification. For testing of the adaptive face recognition method, we tested the adaptive method in the brightness noise, in the impulse noise and in the composite noise and verified that the adaptive method protects face recognition-rate's rapidly decrease which can be occurred generally in the noisy environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.528-533
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• 2010

This paper studied the training methods less affected by the emotional variation for the development of the robust speech recognition system. For this purpose, the effect of emotional variation on the speech signal and the speech recognition system were studied using speech database containing various emotions. The performance of the speech recognition system trained by using the speech signal containing no emotion is deteriorated if the test speech signal contains the emotions because of the emotional difference between the test and training data. In this study, it is observed that vocal tract length of the speaker is affected by the emotional variation and this effect is one of the reasons that makes the performance of the speech recognition system worse. In this paper, a training method that cover the speech variations is proposed to develop the emotionally robust speech recognition system. Experimental results from the isolated word recognition using HMM showed that propose method reduced the error rate of the conventional recognition system by 28.4% when emotional test data was used.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.468-474
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• 2010

The major factor that disturbs practical use of speech recognition is distortion by the ambient and channel noises. Generally, the ambient noise drops the performance and restricts places to use. DSR (Distributed Speech Recognition) based speech recognition also has this problem. Various noise cancelling algorithms are applied to solve this problem, but loss of spectrum and remaining noise by incorrect noise estimation at low SNR environments cause drop of recognition rate. This paper proposes methods for speech enhancement. This method uses MMSE-STSA for noise cancelling and ideal binary mask to compensate damaged spectrum. According to experiments at noisy environment (SNR 15 dB ~ 0 dB), the proposed methods showed better spectral results and recognition performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1291-1295
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• 2004

This paper proposed a new approach for endpoint detection of isolated speech, which proves to significantly improve the endpoint detection performance. The proposed algorithm relies on the root mean square energy (rms energy), zero crossing rate and spectral characteristics of the speech signal where the Euclidean distance measure is adopted using cepstral coefficients to accurately detect the endpoint of isolated speech. The algorithm offers better performance than traditional energy-based algorithm. The vocabulary for the experiment includes English digit from one to nine. These experimental results were conducted by 360 utterances from a male speaker. Experimental results show that the accuracy of the algorithm is quite acceptable. Moreover, the computation overload of this algorithm is low since the cepstral coefficients parameters will be used in feature extraction later of speech recognition procedure.

• International Journal of Contents
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• v.11 no.1
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• pp.1-6
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• 2015

The development of information and technology has brought plenty of changes to the educational environment. The prevalence of smart phones is particularly playing a huge role in shaping learning methods. Smart phones provide the opportunity to scan QR codes, which can greatly ease access to information. Due to a high recognition speed, recognition rate, and restoration rate, they can be useful tools for English teachers to use in their class. In this study, we suggest using QR codes for writing and picture descriptions. Based on this study, more research should invest in Technology Embedded English teaching models to create better English classes for students.