• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• Smart Media Journal
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• v.7 no.2
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• pp.34-39
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• 2018

There are many studies on red tide due to the continuous increase in damage to domestic fish and shell farms by the harmful red tide. However, there is insufficient domestic research of identifying harmful red tide algae that automatically recognizes red tide images. In this paper, we propose a red tide image classification method using deep learning based open source. To solve the problem of recognition of various images of red tide algae, the proposed method is implemented by using tensorflow framework and Google image classification model.

• Bulletin of the Korean Chemical Society
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• v.11 no.5
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• pp.450-455
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• 1990

Fifty eight obsidian artifacts and four obsidian source samples have been analyzed by instrumental neutron activation analysis. Artifact samples have been classified into classes by unsupervised learning techniques such as eigenvector projection and nonlinear mapping. The source samples have thereafter been connected to the classes by the supervised learning techniques such as SLDA and SIMCA so as to characterize each class by possible source sites. Some difference attributable to different nonlinear mapping techniques and the elemental effects on the separation between classes have been discussed.

• Journal of the Korea Convergence Society
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• v.8 no.9
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• pp.31-39
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• 2017

The open source software is widely used nowadays so that means developing software without using open source software can hardly imagine. However, the developer's recognition about the license statements that defines the requirements in accordance with the use of open source is relatively low and a few study is associated with this topic. Therefore, this study examines the use of open source in software development in the context of EDISON project in the national research and development project. Furthermore, the study attempts to suggest the advanced model from simply avoiding license conflicts to constructing the open source project ecosystem including the choose of representative open source, the development of open source communities and contributor agreements.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.423-430
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• 2005

In this paper. a microphone array beamforming algorithm that reduces the signal distortion caused by reverberation and near-field effect in car environment is proposed. When reverberation or near-field effect is present, an optimum beamformer should be constructed with a steering vector consisting of transfer functions between source and microphones, but it is generally difficult to estimate transfer functions on-line without knowledge of the source signal. Instead, a sub-optimal beamforming algorithm that reduces signal distortion is proposed. It is constructed with steering vectors consisting of relative transfer functions between reference sensor and other sensors. In order to evaluate the performance of the proposed algorithm. we had recorded noisy speech database in a car, and performed speech recognition experiments with HMM Toolkit (HTK) released by Cambridge University. The recognition rate of the proposed algorithm was 15 percents higher than that of the conventional far-field beamformers in best case.

• Journal of the Korea Fashion and Costume Design Association
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• v.9 no.1
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• pp.221-234
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• 2007

The purposes of this study were to identify the effect of clothing involvement and shopping orientations on the usage of information sources and to investigate the differences of clothing involvement, shopping orientation and clothing purchasing behavior according to the types of information source usage. The study subjects comprised 302 females living in Seoul. The datas were analyzed with factor analysis, regression, ANOVA, discriminant analysis, and $x^2$-test. The results generated from this study are as follows: First, clothing involvement and shopping orientation factors influenced the usage of information source. Among the clothing involvement factors, fashion/clothing involvement was the most important factor to the types of information source. Second, according to usage of information sources, female consumers were classified into four groups, such as active, nonpersonal, personal, and non-active information source usage group. Fashion/clothing involvement was the most significant involvement factor to divide four groups. Third, among the demographic variables, only age was the useful factor which can differ the usage of information source. For example, 30s' were more active than other groups, on the other hand 50s' use personal information source more than other groups. Therefore, marketer should blow consumer's clothing involvement and shopping orientation which are effective to the usage of information source, and use this knowledge on the advertising and marketing plan.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.110-116
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• 2006

In this paper, we implement a speech recognition system to support ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio sensors. The proposed system is consist of the wireless audio sensor, the speech recognition algorithm using psychoacoustic model and LDPC(low density parity check) for correcting errors. The proposed speech recognition system is inserted in a HOST PC to use the sensor energy effectively mil to improve the accuracy of speech recognition, a FEC(Forward Error Correction) system is used. Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance between sensor and the source of voice is less then 1.0m FAR and FRR are 0.126% and 7.5% respectively.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.55-63
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• 2013

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

• Journal of Platform Technology
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• v.8 no.4
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• pp.20-28
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• 2020

In this paper, a voice, object, and optical character recognition platform including voice recognition-based smart wearable devices, smart devices, and web AI servers was proposed as an appropriate technology to help the visually impaired to live independently by learning the life cycle of the visually impaired in advance. The wearable device for the visually impaired was designed and manufactured with a reverse neckband structure to increase the convenience of wearing and the efficiency of object recognition. And the high-sensitivity small microphone and speaker attached to the wearable device was configured to support the voice recognition interface function consisting of the app of the smart device linked to the wearable device. From experimental results, the voice, object, and optical character recognition service used open source and Google APIs in the web AI server, and it was confirmed that the accuracy of voice, object and optical character recognition of the service platform achieved an average of 90% or more.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.