• Genomics & Informatics
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• 제1권2호
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• pp.113-118
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• 2003

Among a number of antigens characterized in M leprae, an etiological agent of Leprosy, the 18 kDa antigen, is unique to M leprae. We have previously determined a sequence specific element in the 18 kDa gene of M leprae, which confers transcriptional repression. In this report, we have examined if the element could be applied to genes other than the 18 kDa gene of M leprae. To identify the roles of the regulatory sequence in heterologous promoter, we have constructed pB3 vector series, which contains BCG hsp65 promoter and the M leprae 18 kDa transcription repression responsive element in tandem using LacZ gene as a reporter gene. Cloning of hsp65 promoters of M bovis BCG or M smegmatis in front of LacZ gene resulted in normal $\beta$­galactosidase activity as expected. However, when the sequence element was placed between the promoter and the LacZ gene, $\beta$-galactosidase activity was reduced 10-fold less. Also we have examined with pB3(-) vector, that harbors the transcription repression responsive element in a reversed orientation, the $\beta$-galactosidase activity was found to be similar to pB3(+) vector. Thus, these results further confirm that M leprae 18 kDa transcription repression responsive element could regulate BCG hsp65 heterologous promoter and that the element could act as an operator for the transcription of mycobacteria.

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제52권3호
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• pp.165-172
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• 2003

This paper presents an operation scheme to enhance the power system security by applying FACTS on Power systems. Three main generic types of FACTS devices are suggested an illustrated. Flow congestions over lines have been solved by controlling active power of series-compensated FACTS devices and low voltages at buses have been solved by controlling reactive power of shunt-compensated FACTS devices. Especially, Especially, UPFC has been applied in both line congestion and low voltages. Two kinds of indices which indicate the power system security level related to line flow and bus voltage are utilized in this paper. They have been minimized to enhance the power system security level through the iterative method and the sensitivity vector of security index is derived to determine the direction to minimum. The proposed algorithm has been tested on the IEEE 57-bus system with FACTS devices in a normal condition and a line-faulted contingency.

• Speech Sciences
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• 제8권3호
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• pp.133-142
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• 2001

In the area of speech processing, raw signals used to be presented in 2D format and different kinds of algorithms use the format to solve their problems. However, such kinds of presentation methods have limitations to extract characteristics from the signal, even though the algorithms are quiet good. The basic reason is that not much information can be detected from the 2D signal. Strange attractor in the field of chaos theory provides the 3D presentation method. In the area of the recognition problem, signal construction method is very important because good features can be detected from a good shape of attractors. This paper discusses a new presentation method that can be used to construct strange attractor in a different way. Normal strange attractor uses time-delay idea while the new method uses time-delay and vector average. This method provides us good information to be applied to speaker recognition problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제22권5호
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제45권3호
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• pp.160-174
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• 2008

In this paper, we propose new coding techniques for photometry data of three-dimensional(3-D) mesh models. We make a good use of geometry and connectivity information to improve coding efficiency of color, normal vector, and texture data. First of all, we determine the coding order of photometry data exploiting connectivity information. Then, we exploit the obtained geometry information of neighboring vortices through the previous process to predict the photometry data. For color coding, the predicted color of the current vertex is computed by a weighted sum of colors for adjacent vortices considering geometrical characteristics between the current vortex and the adjacent vortices at the geometry predictor. For normal vector coding, the normal vector of the current vertex is equal to one of the optimal plane produced by the optimal plane generator with distance equalizer owing to the property of an isosceles triangle. For texture coding, our proposed method removes discontinuity in the texture coordinates and reallocates texture image segments according to the coding order. Simulation results show that the proposed compression schemes provide improved performance over previous works for various 3-D mesh models.

• Journal of the Korean Institute of Intelligent Systems
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• 제13권3호
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• pp.266-274
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• 2003

This paper considers an incremental support vector learning for the abnormality detection problems. One of the most well-known support vector learning methods for abnormality detection is the so-called SVDD(support vector data description), which seeks the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to modify the SVDD into the direction of utilizing the relation between the optimal solution and incrementally given training data. After a thorough review about the original SVDD method, this paper establishes an incremental method for finding the optimal solution based on certain observations on the Lagrange dual problems. The applicability of the presented incremental method is illustrated via a design example.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제66권10호
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• pp.1516-1522
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• 2017

We propose a novel monitoring system for diagnosing crack faults of the wind turbine using image information. The proposed method classifies a normal state and a abnormal state for the blade parts of the wind turbine. Specifically, the images are input to the proposed system in various states of wind turbine rotation. according to the blade condition. Then, the video of rotating blades on the wind turbine is divided into several image frames. Motion vectors are estimated using the previous and current images using the motion estimation, and the change of the motion vectors is analyzed according to the blade state. Finally, we determine the final blade state using the Support Vector Machine (SVM) classifier. In SVM, features are constructed using the area information of the blades and the motion vector values. The experimental results showed that the proposed method had high classification performance and its $F_1$ score was 0.9790.

• Journal of KSNVE
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• 제9권4호
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• pp.769-777
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• 1999

Research on vibration of a vehicle with a transversely mounted 4-cylinder engine was performed using a vector synthesis method, Data of the engine vibration for the vector synthesis method was obtained experimentally and the data was ODS-fitted to calculate vibration level on any engine location assuming that the engine is rigid body in the frequency range of interest. In order to derive the excitation force on the vehicle body, the displacements were converted from the acceleration of engine. The transfer functions from engine mounts to toe pan on the floor were obtained experimentally. The vibration level on the toe pan was predicted by multiplying the excitation force by the transfer function. The predicted vibration level was compared with experimental data and the result was reasonable. Using the developed method, analysis was made for the effect of body fixture conditions of the vehicle when testing the engine vibration and for the effect of the transfer functions when the engine is installed or when the engine is removed. Finally the degree of contribution for 12 transfer paths was calculated.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• 제12권6호
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Journal of Microbiology and Biotechnology
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• 제16권8호
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• pp.1185-1191
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• 2006

The expression of a pullulanase gene in Pichia pastoris was investigated. The gene encoding pullulanase was cloned by PCR using the chromosomal DNA of Bacillus naganoensis as the template. The expression vector pPIC9K-Pu was constructed by inserting the pullulanase gene into plasmid pPIC9K and then transformed into Pichia pastoris SMD 1168 by electroporation. Activity determination, SDS-PAGE, and PCR amplification indicated that the gene of the pullulanase from B. naganoensis had successfully been expressed in SMD 1168 and the molecular size of the expressed recombinant product was about 119.9 kDa. This is the first report on the successful expression of the pullulanase from B. naganoensis in P. pastoris. The transformant secreted recombinant pullulanase with the activity of 350.8 IU/ml in shake-flask culture. The properties of the recombinant pullulanase were characterized.