• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.607-610
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• 2009

The abnormal combustion in the running engine results to knocking which increases the pressure and temperature in the cylinder, thereby decreasing the generated power by reducing the thermal efficiency. When the temperature and pressure in the cylinder increased rapidly by knocking, abnormal combustion takes place and the engine power is decreased. To investigate the knocking phenomenon, accelerometers are installed in the cylinder head to monitor and diagnose the vibration signal. As method of signal analysis, the time-frequency analysis method was adapted for acquisition of vibration signal and analyzes engine combustion in the short time. In this experiment, after analyzing time data which is stored in the signal recorder in one unit work (4 strokes: 2 revolutions), the signal with frequency and Wavelet methods with extracted one engine combustion data was also analyzed. Then, normal condition with no knocking signal is analyzed at this time. Hereafter, the experiments made a standard for distinguishing normal and abnormal condition to be carried out in acquisition of vibration signal at all cylinders and extracting knocking signal. In addition, analyzing methods can be diverse with Symmetry Dot Patterns (SDP), Time Synchronous Average (TSA), Wigner-Ville Distribution (WVD), Wigner-Ville Spectrum (WVS) and Mean Instantaneous Power (MIP) in the cold test [2]. With signal processing of vibration from engine knocking sensor, the authors adapted a part of engine /rotor vibration analysis and monitoring system for marine vessels to prevent several problems due to engine knocking

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.1-8
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• 2018

In the field of biometric security such as face and iris recognition, it is essential to extract facial features such as eyes and lips. In this paper, we have studied a method of detecting eye and lip region in face image using faster R-CNN. The faster R-CNN is an object detection method using deep running and is well known to have superior performance compared to the conventional feature-based method. In this paper, feature maps are extracted by applying convolution, linear rectification process, and max pooling process to facial images in order. The RPN(region proposal network) is learned using the feature map to detect the region proposal. Then, eye and lip detector are learned by using the region proposal and feature map. In order to examine the performance of the proposed method, we experimented with 800 face images of Korean men and women. We used 480 images for the learning phase and 320 images for the test one. Computer simulation showed that the average precision of eye and lip region detection for 50 epoch cases is 97.7% and 91.0%, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.1
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• pp.1-8
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• 2011

Efficient power management plays a crucial role to strengthen competitiveness in the market of portable mobile commodities. This paper presents a proactive power management technique, called by Energy-Aware Scheduling policY (EASY), to exploit the sleep time information of running applications. Different from previous power management approaches focusing on power conservation in standby mode, the proposed scheme characterizes each application program's operational characteristic in active mode by observing how long the task stays in sleep state of CPU scheduler. Based on the measured sleep time, the proposed EASY speculates an adequate CPU clock frequency according to the current CPU workload and scales the frequency directly to the predicted one. Experimental results show that the proposed scheme reduces the power consumption by 10-30% on average compared to traditional DPM approach, with a minimal impact on the performance overhead.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.389-396
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• 2003

In this paper, we present a Korean partial parser based on rules, which is used for running applications such as a grammar checker and a machine translation. Basically partial parsers construct one or more morphemes and/or words into one syntactical unit, but not complete syntactic trees, and accomplish some additional operations for syntactical parsing. The system described in this paper adopts a set of about 140 manually-written rules for partial parsing. Each rule consists of conditional statements and action statement that defines which one is head node and also describes an additional action to do if necessary. To observe that this approach can improve the efficiency of overall processing, we make simple experiments. The experimental results have shown that the average number of edges generated in processing without the partial parser is about 2 times more than that with the partial parser.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.476-485
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• 2014

This study was to examine the effectiveness of the Solution Focused Group Counseling Program for elementary school students by meta-analysis. For the purpose of the study, master's theses, doctoral dissertations, and journal articles published in Korea up to May, 2014 were systematically reviewed. As a result, a total of 20 studies were eligible for the inclusion criteria. The mean effect sizes and test for homogeneity of effect size (Q-statistic) were analyzed by using Comprehensive Meta-Analysis software 2.0. The main findings of the study were as follows: First, the average effect sizes for Solution Focused Group Counseling Program were ES=1.61 in self-esteem, ES=1.35 in school adjustment capacity, ES=1.07 in interpersonal relationship and ES=1.03 in self-efficacy, Second, the statistically significant moderate variables of the effect size for the Solution Focused Group Counseling Program were 'sessions' of self-esteem and 'sessions', 'running times' in school adjustment capacity. Based on the study results, the research and practice implications were discussed.

• The Journal of Korean Society for School & Community Health Education
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• v.11 no.1
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• pp.117-134
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• 2010

Background & Objectives: Recently, problems such as in-school violence of teenagers, gang fight, habitual drinking, smoking, running away from home and group isolation can be regarded as the most fundamental causes of teenagers' deviation. It is said that such fundamental causes are originated from their home, the base for the psychological and behavioral development. Methods: In this study, the data for 3,188 students studying in the 3rd grade at middle school, which have been obtained from the second year survey for the panel of the 2nd grade students in 2004 by to Korea Youth Panel Survey, have been used for the analysis of the study. Results: According to the result of the study, there seems to be no difference between two genders for differences of average values. Regarding the analysis of correlation coefficients between such related variables as domestic violence, self-control, self-worth, self-trust and family relationship (sociability), and teenagers' deviation, When variables increase, teenagers' deviation decreases. Regarding the relationship between domestic violence and teenagers' deviation, physical violence is more related than lingual violence. The relationship between self-control and teenagers' deviation is the highest. By studying the deviation of teenagers exposed to domestic violence and carrying out the regression analysis, physical violence influences, teenagers' deviation was greater. As self-control, self-worth and family relationship(sociability) increase, teenagers' deviation decreases. Regarding the analytic results of this study, the experience of domestic violence seems to influence teenagers' deviation greatly. Among various types of domestic violence, physical violence is analyzed to influence teenagers' deviation greatly. Self-control, self-worth and family relationship(sociability) increase, teenagers' deviation decreases. Conclusions: In order to prevent teenagers' deviation and decrease the second conviction rate, it is necessary to provide a method of decreasing the domestic violence. It is necessary for schools and local communities to provide social support for teenagers.

• Journal of Microbiology and Biotechnology
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• v.20 no.7
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• pp.1092-1100
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• 2010

Bacteria, fungi, and protozoa inhabiting the rumen, the largest chamber of the ruminants' stomach, release large quantities of hydrogen during the fermentation of carbohydrates. The hydrogen is used by coexisting methanogens to produce methane in energy-yielding processes. This work shows, for the first time, a fundamental possibility of using a hydrogen-rich fermentation gas produced by selected rumen ciliates to feed a low-temperature hydrogen fuel cell. A biohydrogen fuel cell (BHFC) was constructed consisting of (i) a bioreactor, in which a hydrogen-rich gas was produced from glucose by rumen ciliates, mainly of the Isotrichidae family, deprived of intra- and extracellular bacteria, methanogens, and fungi; and (ii) a chemical fuel cell of the polymer-electrolyte type (PEFC). The fuel cell was used as a tester of the technical applicability of the fermentation gas produced by the rumen ciliates for power generation. The average estimated hydrogen yield was ca. 1.15 mol $H_2$ per mole of fermented glucose. The BHFC performance was equal to the performance of the PEFC running on pure hydrogen. No fuel cell poisoning effects were detected. A maximum power density of $1.66\;kW/m^2$ (PEFC geometric area) was obtained at room temperature. The maximum volumetric power density was $128\;W/m^3$ but the coulombic efficiency was only ca. 3.8%. The configuration of the bioreactor limited the continuous operation time of this BHFC to ca. 14 h.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1418-1426
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• 2009

There has been a lot of interest in an effective method for background subtraction in an effort to separate foreground objects from a predefined background image. Promising results on background subtraction using statistical methods have recently been reported are robust enough to operate in dynamic environments, but generally require very large computational resources and still have difficulty in obtaining clear segmentation of objects. We use a simple running-average method to model a gradually changing background, instead of using a complicated statistical technique. We employ a single global threshold vector, optimized by a genetic algorithm, instead of pixel-by-pixel thresholds. A new fitness function is defined and trained to evaluate segmentation result. The system has been implemented on a PC with a webcam, and experimental results on real images show that the new method outperforms an existing method based on a mixture of Gaussian.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of the Korean GEO-environmental Society
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• v.16 no.3
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• pp.49-55
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• 2015

The rainfall pattern, rainfall intensity as well as topographical conditions used for the analysis of debris flow affect, in general, the magnitude of debris flow and flow velocity, when debris flow occurs. The consideration of topographical conditions implies that the topography is equally divided into grids and the slope of inside the grid is computed as an average, leading to, in turn, obtain the closer results to the reality as the grid is smaller in the case of the severely bended topography. Although the size of grid should be as small as possible so as for more accurate analysis of debris flow, the analysis of debris flow has been so far conducted by using sparsely divided grids due to the limitation of analysis algorithm, computational ability and running time. So, it is necessary to suggest an appropriate grid size for the practical approaches. Therefore, this study presents the evaluation of the effect of the size of a grid on the debris flow besides the factors which referred to the previous studies such as accumulated rainfall, rainfall intensity and rainfall duration time. From this, it enables to suggest a rational and practical grid size for topography to be divided.