• The Korean Journal of Ecology
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• 제13권2호
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• pp.83-92
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• 1990

Two-dimensional analysis provides a comprehensive description of the structure, scales of pattern and directional components in a spatial data set. In spectral analysisi, four functions are illustrated,; the autocorrelation, the periodogram, the R-spectrum and the $\theta$ -spectrum. The R-spectrum and $\theta$ -spectrum function respectively summarize the periodogram in term of scale of pattern and directional components. Sampling is measured in the Naejang National Park area where the Daphniphyllum trees grow. 320 contiguous (15$\times$15)m plots are located along the transect and density of all trees over DBH 3 cm recorded respectively. 12 species of vascular plant are recorded in this survey area. The trend surface of density of all plant are estimated using polynomial regression and are exhibited in 3-dimensional graph and density contour map. Transformation to the corresponding polar spectrum from the periodogram emphasized the directional components and the scales to pattern. R-spectrum corresponding to the scale of pattern of periodogram showed a large peak 15.47 in the interval 9$\theta$-spectrum corresponding to directional components have two peaks 8.28 and 11.05 in the interval $35^{\circ}\theta <45^{\circ}and 125^{\circ}\theta< <135^{\circ}, respectively. Programs to compute all the analyses described in this study was obtained from Dr. Ranshow and was translated to BASIC by the author.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 한국측량학회 2004년도 추계학술발표회 논문집
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• The Journal of the Acoustical Society of Korea
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• 제26권5호
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• pp.207-213
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• 2007

In this research, we propose an efficient Speech/Music discrimination method that uses spectrum analysis and neural network. The proposed method extracts the duration feature parameter(MSDF) from a spectral peak track by analyzing the spectrum, and it was used as a feature for Speech/Music discriminator combined with the MFSC. The neural network was used as a Speech/Music discriminator, and we have reformed various experiments to evaluate the proposed method according to the training pattern selection, size and neural network architecture. From the results of Speech/Music discrimination, we found performance improvement and stability according to the training pattern selection and model composition in comparison to previous method. The MSDF and MFSC are used as a feature parameter which is over 50 seconds of training pattern, a discrimination rate of 94.97% for speech and 92.38% for music. Finally, we have achieved performance improvement 1.25% for speech and 1.69% for music compares to the use of MFSC.

• Journal of Korea Multimedia Society
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Phonetics and Speech Sciences
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• 제2권4호
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• pp.67-74
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• 2010

The purpose of this study was to explore the cross-linguistic difference in the spectral movement pattern of American English and Korean vowels. Eight American vowels /a/, /e/, /$\varepsilon$/, /i/, /I/, /o/, /u/, and /$\mho$/, and five Korean vowels, /a/, /e/, /i/, /o/ and /u/ in a fricative-vowel environment produced by adult speakers of each language were analyzed. The spectral movement patterns of the first two formant frequency values were measured and analyzed. The results showed that Korean vowels had minimal spectral movement, both in F1 and F2 values, as compared to American English vowels. Moreover, no consistent direction of movement was found in the three corner Korean vowels, while American English vowels showed consistent direction of movement for each vowel of the same phonemic category.

• International Journal of Fluid Machinery and Systems
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• 제2권3호
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• pp.215-222
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• 2009

Jets issuing through small holes in a wall into a freestream has proven effective in the control of flow separation. This technique is known as the vortex generator jet (VGJs) method. If a precursor signal of separation is found, the separation control system using VGJs can be operated just before the onset of separation and the flow field with no separation is always attained. In this study, we measured the flow field and the wall static pressure in a two-dimensional diffuser to find a precursor signal of flow separation. The streamwise velocity measurements were carried out in the separated shear layer and spectral analysis was applied to the velocity fluctuations at some angles with respect to the diffuser. The pattern of peaks in the spectral analysis changes as the divergence angle increases over the angle of which the whole separation occurs. This change in the spectral pattern is related to the enhancement of the growth of shear layer vortices and appears just before the onset of separation. Therefore, the growth of shear layer vortices can be regarded as a precursor signal to flow separation.

• Journal of Biomedical Engineering Research
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• 제33권1호
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.211-214
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• 1996

This paper propose a method for pattern recogniton using spectrum analyzer and fuzzy ARTMAP. Contour sequences obtained from 2-D planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The Fourier transform of contour sequence and spectrum analyzer are used as a means of feature selection and data reduction. The three dimensional spectral feature vectors are extracted by spectrum analyzer from the FFT spectrum. These Spectral feature vectors are invariant to shape translation, rotation, and scale transformations. The fuzzy ARTMAP neural network which is combined with two fuzzy ART modules is trained and tested with these feature vectors. The experiments include 4 aircrafts and 4 industrial parts recognition process are presented to illustrate the high performance of this proposed method in the ion problems of noisv shapes.

• Journal of the Korean Society for Nondestructive Testing
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• 제12권2호
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• pp.21-29
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• 1992

Acceleration signal characteristics of a steel plate, impacted by steel balls, were studied in an attempt to apply the experimental results to the impact location and mass estimation of metallic loose parts in the cooling system of nuclear power plants. Experimental results show that the variation of maximum acceleration amplitude and impact contact time due to the change of ball mass and impact velocity can be well explained by the Hertz impact theory. The frequency spectral pattern shifted slightly in spite of the increase of impact velocity and impact location. Ball mass, however, strongly affected the frequency spectral pattern. Hence the frequency spectrum can be used for estimation of the mass of unknown loose parts in the cooling system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• 제45권5호
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• pp.429-436
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• 2017

Since the telemetry performance using the time-delayed data dissipates the wireless channel resources, we propose the spectral efficient retransmission scheme in this paper. In the proposed scheme, the telemetry data is retransmitted based on triggered memory to improve the spectral efficiency. The proposed scheme minimizes the error caused by multipath fading, antenna pattern as well as the error caused by the flight events. In the flight simulation data, we show the proposed scheme improves the telemetry performance based on spectral efficient retransmission.