• Current Optics and Photonics
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• v.4 no.1
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• pp.31-43
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• 2020

The feasibility of the application of terahertz electromagnetic waves in the diagnosis of prostate cancer was examined. Four samples of incomplete cancerous prostatic paraffin-embedded tissues were examined using terahertz spectral imaging (TPI) system and the results obtained by comparing the absorption coefficient and refractive index of prostate tumor, normal prostate tissue and smooth muscle from one of the paraffin tissue masses examined were reported. Three hundred and sixty cases of absorption coefficients from one of the paraffin tissues examined were used as raw data to classify these three tissues using the Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). An excellent classification with an accuracy of 92.22% in the prediction set was achieved. Using the distribution information of THz reflection signal intensity from sample surface and absorption coefficient of the sample, an attempt was made to use the TPI system to identify the boundaries of the different tissues involved (prostate tumors, normal and smooth muscles). The location of three identified regions in the terahertz images (frequency domain slice absorption coefficient imaging, 1.2 THz) were compared with those obtained from the histopathologic examination. The tissue tumor region had a distinctively visible color and could well be distinguished from other tissue regions in terahertz images. Results indicate that a THz spectroscopy imaging system can be efficiently used in conjunction with the proposed advanced computer-based mathematical analysis method to identify tumor regions in the paraffin tissue mass of prostate cancer.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Economic and Environmental Geology
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• v.14 no.2
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• pp.93-102
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• 1981

Studies of model potential fields continued upward and downward show differences depending on the method of continuation. Beginning with a magnetic field computed over a buried vertical cylinder, the field was continued to various levels by a method introduced by Henderson (Lagrangian interpolation) and by a spectral method (frequency domain analysis). Resultant fields show (1) no significant differences in upward continued values, (2) in downward continuation, accurate values are obtained with the spectral method over the central part of the anomaly, and (3) accurate values are obtained with Henderson's method on the flanks of the anomaly, while oscillations usually characterize the spectral method in this region. Essentially the same observations are made for derivative calculations. Field oscillations are empirically predicted at levels continued to approximately two-thirds of the depth of the source. Our spectral computer program output yields marked oscillations at one-half of the depth of the source. Henderson's method shows no oscillations at this depth and only minor oscillations at the top of the body (some negative values appear on the flanks of the anomaly). The Henderson output is a smooth field even if continued below the top of the body. These results suggest that the presence of oscillations cannot be used to identify the top of a buried source without careful consideration of the method used to continue the field. Use of the derivative to outline and isolate anomalies must similarly include consideration of the method of calculation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.293-303
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• 2002

Recently, the use of the high strength materials and development of construction techniques have resulted in more flexible and longer spanning in the stadium systems. So the natural frequency of stadium structures are became low. Stadium stand could be led to significant dynamic response as like resonance due to spectator rhythmical activities. The accurate analysis of dynamic behavior of stadium systems and the precise investigation of the dynamic loads on stadium structures are demanded for effective design. It is desirable to apply measured dynamic loads created by spectator activities because these dynamic loads are not easy to express numerical formula. As the floor mesh of stadium stand is refined, the number of divided elements increases in numerical analysis. the rise of the number of elements makes the numbers of nodal points increased and numerous computer memory required. So it is difficult to analysis refine full model of stadium structures by using the commercial programs. In this study, the various dynamic loads induced by spectator movements are measured and analyzed. And a new modeling method that reduce the nodal points are introduced. Vibration analysis of stadium stands is executed to inspect accuracy and efficiency of proposed method in this paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11B
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• pp.1501-1509
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• 2001

Recently, the use of wireless communication systems has been rapidly increasing, which results in a difficult problem in efficient control of limited frequency resources. As a way of solving this problem, the ultra wideband time hopping impulse radio system attracts much attention. The impulse radio system communicates pulse position modulated data using Gaussian monocycle pulses of very short duration less than 1 nsec. Thus the transmitted signal has very low power spectral density and ultra wide bandwidth from near D.C. to a few GHz. It is blown that it hardly interferes with the existing communication systems because of its very low power spectral density. The purpose of this paper is to characterize multipath propagation of the impulse radio signal and to evaluate the performance of the correlator-based receiver for the multipath environments. In this paper, we consider the deterministic two-path model and the statistical indoor multipath model of Saleh and Valenzuela. For the two-path model the output of the correlator with the ideal reference waveform varies according to the relative difference between the indirect path delay and the time interval of PPM, and to the indirect path gains. In addition, the characteristics of bit error rates is measured for the two models through computer simulation. The simulation results indicate that the performance of the impulse radio system depends both on the relative difference between the indirect path delay and the time interval of PPM, and on the indirect path gains. Furthermore, it is observed that the reference signal designed for the AWGN channel can not be applied to the multipath channels.

• Journal of Fashion Business
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• v.3 no.3
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• pp.27-37
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• 1999

The purpose of this study is to provide for the basic data useful to the effective production and marketing of the brassiere befitting adult women's body shapes and preferences, and thereby, help them improve their apparel life. For this purpose, 563 Korean adult women aged between 20-59 were sampled to survey their practices of purchasing the brassieres and positively identify the factors affecting the practices and thereupon, determine the correlations among them by age group. Data is processed by a computer(SAS) and analyzed by using frequency, percentage, $\chi^2$-test, ANOVA, Duncan-test. The main results of this study are as follows; 1. The adult women tend to use such mass media as TV, magazines, catalogues and DM to decide for themselves which brassiere befits them most, and to check the brand-name(78.8%) or the sizes(93.4%), but more than 90% of them purchase their brassieres without trying on them. 2. The places of purchasing on which adult women rely most for their brassiere are department stores(32.6%), agencies(26.1%) and discount or pension shops(25.4%), while more than 90% of the sample women often visit bargain sale shops. The average number of brassieres possessed by our adult women is 5.7, and an adult women buys 2.6 brassiere costing 10-30 thousand wons a year on average and consumes a brassiere for the period from 6 months to 2 years. 3. It had been disclosed that the brand favored most by adult women is Venus(56.2%), followed by Vivien (17.6%), Wacoal(6.8%), Amie(2.5%) and Body Guard(2.3%). The most influential factors for the popularity of brands are fitting condition(40.3%) and design(23.8%), which suggests that consumers appreciate functionality and aesthetics. The most important reference affecting our women's choice of brassiere is size(64.4%), followed by design and functionality. The brassiere style favored most by adult women is a wire-type 3/4 cup brassiere made of thin material with sewing lines, while the most favorite color is white. In all, it has been found through this study that adult women's practices of buying their brassieres differ by age group, which may well suggest that brassiere production need to take such age-wise practices into consideration in setting up their brassiere production and marketing strategies.

• Journal of the Korean Applied Science and Technology
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• v.37 no.5
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• pp.1409-1417
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• 2020

The purpose is to increase the likelihood of physical education students for employment in public institutions by examining the priority for hiring sports-related public institutions. The subject of the study was purposeful sampling of a total of 11 persons including 4 sports professors, 3 NCS experts in sports field, 2 judges from public sports institutions, and 2 personnel in charge of hiring public institutions. Through this process, from January 3 to March 12, 2020, the importance of priority was analyzed using hierarchical structure analysis using the main factors of NCS vocational basic competency. All data are coded so that statistical processing can be performed. Using SPSS/PC (ver. 21.0) for Windows, the hierarchical structure analysis was used for frequency analysis and priority determination. First, communication skills (.231), organizational comprehension skills (.177), resource management skills (.128), interpersonal skills (.110), vocational ethics (.082), problems in the major areas of recruitment of sports-related public institutions Solving ability (.061), information ability (.056), mathematical ability (.054), self-development ability (.052), and description ability (.049) were analyzed in order. Second, in terms of evaluation items, communication is communication skills (.442), mathematical skills are basic computation skills (.512), problem solving skills are thinking skills (.722), self-development skills are self-management skills (.587), Resource management ability was analyzed in order of time management ability (.531), interpersonal relationship ability as teamwork ability (.382), information ability in computer use ability (.677), technical ability in technology understanding ability (.599).

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.81-90
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• 1999

This paper describes an EEG(electroencephalogram) software for two-channel cerebral function monitoring system to detect the cerebral ischemia. In the software, two-channel bipolar analog EEG signals are digitized and from the signals various EEG parameters are extracted and displayed on a monitor in real-time. Digitized EEG signal is transformed by FFT(Fast Fourier transform) and represented as CSA(compressed spectral array) and DSA(density spectral array). Additional 5 parameters, such as alpha ratio, percent delta, spectral edge frequency, total power, and difference in total power, are estimated using the FFT spectra. All of these are effectively merged in a monitor and displayed in real-time. Through animal experiments and clinical trials on men, the software is modified and enhanced. Since the software provides raw EEG, CSA, DSA, simultaneously with additional 5 parameters in a monitor, it is possible to observe patients multilaterally. For easy comparison of patient's status, reference patterns of CSA, DSA can be captured and displayed on top of the monitor. And user can mark events of surgical operation and patient's conditions on the software, this allow him jump to the points of events directly, when reviewing the recorded EEG file afterwards. Other functions, such as forward/backward jump, gain control, file management are equipped and these are operated by simple mouse click. Clinical tests in a university hospital show that the software responds accurately according to the conditions of patients and medical doctors can use the software easily.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.233-247
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• 2023

Recently, research on detecting objects in hidden spaces beyond the direct line-of-sight of observers has received attention. Most studies use optical equipment that utilizes the directional of light, but sound that has both diffraction and directional is also suitable for non-line-of-sight(NLOS) research. In this paper, we propose a novel method of detecting objects in non-line-of-sight (NLOS) areas using acoustic signals in the audible frequency range. We developed a deep learning model that extracts information from the NLOS area by inputting only acoustic signals and predicts the properties and location of hidden objects. Additionally, for the training and evaluation of the deep learning model, we collected data by varying the signal transmission and reception location for a total of 11 objects. We show that the deep learning model demonstrates outstanding performance in detecting objects in the NLOS area using acoustic signals. We observed that the performance decreases as the distance between the signal collection location and the reflecting wall, and the performance improves through the combination of signals collected from multiple locations. Finally, we propose the optimal conditions for detecting objects in the NLOS area using acoustic signals.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.503-512
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• 2011

Recently in HCI research, emotion recognition is one of the core processes to implement emotional intelligence. There are many studies using bio signals in order to recognize human emotions, but it has been done merely for the basic emotions and very few exists for the other emotions. The purpose of present study is to confirm the difference of autonomic nervous system (ANS) response in three emotions (boredom, pain, and surprise). There were totally 217 of participants (male 96, female 121), we presented audio-visual stimulus to induce boredom and surprise, and pressure by using the sphygmomanometer for pain. During presented emotional stimuli, we measured electrodermal activity (EDA), skin temperature (SKT), electrocardiac activity (ECG) and photoplethysmography (PPG), besides; we required them to classify their present emotion and its intensity according to the emotion assessment scale. As the results of emotional stimulus evaluation, emotional stimulus which we used was shown to mean 92.5% of relevance and 5.43 of efficiency; this inferred that each emotional stimulus caused its own emotion quite effectively. When we analyzed the results of the ANS response which had been measured, we ascertained the significant difference between the baseline and emotional state on skin conductance response, SKT, heart rate, low frequency and blood volume pulse amplitude. In addition, the ANS response caused by each emotion had significant differences among the emotions. These results can probably be able to use to extend the emotion theory and develop the algorithm in recognition of three kinds of emotions (boredom, surprise, and pain) by response measurement indicators and be used to make applications for differentiating various human emotions in computer system.