• Journal of the Korea Concrete Institute
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• v.17 no.3 s.87
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• pp.369-374
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• 2005

This study proposes a technique for the recognition of crack patterns, which includes horizontal, vertical, diagonal($-45^{\circ}$), diagonal($+45^{\circ}$), and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed image processing algorithm and artificial neural network. Features were determined using total projection technique, and the structure(no. of layers and hidden neurons) and weight of artificial neural network were determined by learning from artificial crack images. In this process, we adopted Bayesian regularization technique as a generalization method to eliminate overfitting Problem. Numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.226-233
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• 2012

In this paper, we propose a 'linear approximation method' which is an accelerated BER (Bit Error Rate) test method for high speed interfaces, based on an analytical BER model. Both the conventional 'Q-factor estimation method' and 'linear approximation method' can predict a timing margin for $10^{-13}$ BER with an error of about 0.03UI. This linear approximation method is implemented on a hardware as an accelerated Built-In Self Test (BIST) with an internal BERT (BET Tester). While a direct measurement of a timing margin in a 3Gbps interface takes about 5.6 hours with $10^{-13}$ BER requirement and 95% confidence level, the accelerated BIST estimates a timing margin within 0.6 second without a considerable loss of accuracy. The test results show that the error between the estimated timing margin and the timing margin from an actual measurement using the internal BERT is less than 0.045UI.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.105-110
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• 2016

To reduce the cost and volume of a digital camera, a subsampled color filter array(CFA) image is used and demosaicking is applied to estimate the missing color values. However, aliasing, the overlaps of signals in the frequency domain, occurs when signals are subsampled. This causes aliasing artifacts such as false colors and zipper effects in demosaicking processes. In this paper, the algorithm estimating high-quality color images by removing aliasing artifacts in them is proposed. The aliasing region map is estimated using the sub-sampled signals of the CFA image. By using the aliasing region map and the estimated luminance image, the least squares problem of the observation models is designed and aliasing artifacts are eliminated. The experiments demonstrate that the proposed algorithm restores color images without aliasing artifacts.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.152-158
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• 2016

In this paper, we implemented the full stack platform design with MongoDB database of open source platform Raspberry PI 3 model. We experimented the triggering of event driven with acceleration sensor data logging with wireless communication. we captured the image of USB Camera(MS LifeCam cinema) with 28 frames per second under the Linux version of Raspbian Jessie and extended the functionality of wireless communication function with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the full stack platform for recognizing the event triggering characteristics of detecting the acceleration sensor action and gathering the temperature and humidity sensor data under IoT environment. Especially we used MEAN Stack for developing the performance of full stack platform because the MEAN Stack is more akin to working with MongoDB than what we know of as a database. Afterwards, we would enhance the performance of full stack platform for IoT clouding functionalities and more feasible web design with MongoDB.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.47-58
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• 1999

As the performance of processors improves, cache memories are used to overcome the difference of speed between processors and main memories. Generally cache memories are embedded and small sizes, fault coverage is a more important factor than test time in testing point of view. A new test algorithm and a new BIST architecture are developed to detect various fault models with a relatively small overhead. The new concurrent BIST architecture uses the comparator of cache management blocks as response analyzers for tag memories. A modified scan-chain is used for pre-testing of comparators which can reduce test clock cycles. In addition several boundary scan instructions are provided to control the internal test circuitries. The results show that the new algorithm can detect SAFs, AFs, TFs linked with CFs, CFins, CFids, SCFs, CFdyns and DRFs models with O(12N), where N is the memory size and the new BIST architecture has lower overhead than traditional architecture by about 11%.

• The Journal of Korean Physical Therapy
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• v.11 no.2
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• pp.81-92
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• 1999

중추신경계의 미주신경동쪽핵(DMV)내 유사핵분열후 신경세포로 외래 유전자를 전달하는 매개체로서 pseudorabies 바이러스(PRV)의 유전자 조작기술은 흰쥐의 결장내로 PRV를 주입시킨 후 복잡한 신경로 추적에 관한 연구에서 하나의 바이러스에 의해 얻어지는 것보다 더욱 유용한 결과산출이 가능하게 하였다. 본 연구에서는 흰쥐의 생체내 실험모델로 하나의 바이러스 또는 이중 바이러스 주입에 PRV의 유전자 조작된 2종 바이러스를 사용하였다. 이 2종의 바이러스는 PRV의 Bartha 종에서 유래되었지만 면역조직화학적으로 검출할 수 있는 동일한 유전산물을 산출할 수 있도록 구성되었다. PRV-BaBlu는 PRV 게놈의 Us 구역 중 gC 자리에 lacZ 유전자를 삽입하여 산출되었는데 $\beta-galactosidase$ 발현은 이 바이러스에 감염된 신경원의 독특한 표시자로 나타났다. PRV-D는 2가지 단계에 의해 조성되었는데 첫째, PRV-Bartha의 Us 구역의 일부 유전자를 제거하고, 야생형인 PRV-Be DNA로 복구시켰는데 이로써 PRV-D는 PRV-Bartha 또는 PRV-Bablu에 존재하고 있지 않는 외피 당단백질인 gE와 gI를 지니게 되었다. 본 연구의 결과는 다음과 같았다. 첫째, PRV-D의 개별적 접종에 의해 얻어진 감염은 PRV-BaBlu에 의한 동일 신경회로의 감염보다 유의하게 빨랐다. 둘째, 유전자 조작된 PRV의 변이종은 변이종 상호간 및 부모 바이러스와 상이하였다. PRV-D는 PRV-Bartha 또는 PRV-BaBlu보다 감염독성이 더 강했고, PRV-BaBlu는 PRV-Bartha보다 감염독성이 약했다. 셋째, 결장을 지배하는 미주신경동쪽핵내 신경원은 변이종 바이러스들을 동시에 접종하였을 경우 이중감염을 나타내었다.

• Korean Journal of Food Science and Technology
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• v.24 no.1
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• pp.37-41
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• 1992

Starch solid system was used to investigate the effects of lipoxygenase, linoleic acid and water activity on the oxidation of ${\beta}-carotene$ or ${\alpha}-tocopherol$. ${\beta}-carotene$ or ${\alpha}-tocopherol$ was co-oxidized severely with linoleic acid by lipoxygenase, and these were reduced to 19% and 5% of initial concentration, respectively, after 2 days storage at $a_w$ 0.72 in the system. The concentration of ${\beta}-carotene$ and the destruction rates were linearly correlated. However, the ${\beta}-carotene$ was very stable in the system without linoleic acid and lipoxygenase. The oxidation products of ${\alpha}-tocopherol$ were considered as ${\alpha}-tocopheryl$ quinone and ${\alpha}-tocopheryl$ dimer, and the level of ${\alpha}-tocopherol$ quinone increased as the reaction time increased.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.1-8
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• 2002

The color interpolation scheme can play an important role in overcoming the physical limitation of the CCD image sensor and in increasing the resolution of color signals, while most conventional approaches result in blurred edges and false color artifacts. In this paper, we have proposed an improved edge-adaptive color interpolation scheme for a progressive scan CCD image sensor with RGB color filter array The edge indicator function proposed utilizes not only the within-channel correlation but also the cross-channel correlation, and reflects the edge characteristics of an image adaptively. The color components unavailable for at each channel are interpolated along the edge direction, not across the edges, so that aliasing artifacts are supressed. Furthermore, we eliminated false color artifacts resulting from the color image formation model in the edge-adaptive color interpolation scheme by adopting the switching algorithm based on the color edge detection. Simulation results of the proposed algorithm indicate that the improved edge-adaptive color interpolation scheme produces quantitatively better and visually more pleasing results than conventional approaches.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.