• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2927-2934
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• 2023

The influence of the atmospheric radon background on the airborne gamma spectrum can seriously affect researchers' judgement of ground radiation information. However, due to load and endurance, unmanned aerial vehicle (UAV)-borne gamma-ray spectrometry is difficulty installing upward-looking detectors to monitor atmospheric radon background. In this paper, an advanced spectral-ratio method was used to correct the atmospheric radon background for a UAV-borne gamma-ray spectrometry in Inner Mongolia, China. By correcting atmospheric radon background, the ratio of the average count rate of U window in the anomalous radon zone (S5) to that in other survey zone decreased from 1.91 to 1.03, and the average uranium content in S5 decreased from 4.65 mg/kg to 3.37 mg/kg. The results show that the advanced spectral-ratio method efficiently eliminated the influence of the atmospheric radon background on the UAV-borne gamma-ray spectrometry to accurately obtain ground radiation information in uranium exploration. It can also be used for uranium tailings monitoring, and environmental radiation background surveys.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.201-212
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• 2003

Recently, the ability of earthquake detection in the Kyungsang Basin of southeastern Korean Peninsula is greatly improved since seismic stations including seismic network of KIGAM(Korea Institute of Geoscience and Mineral Resources) have been significantly increased. However, a large number of signals from explosions are recorded because of frequent medium to large chemical explosions. The discrimination between natural earthquakes and explosions in the Basin has become an important issue. High frequency local records from 43 earthquakes and 43 explosions with comparable magnitude are selected to establish a reliable discrimination technique in the Basin. Several discrimination techniques in spectral domain using spectral amplitude ratios among Pg, Sg, and Lg waves are widely examined with tile selected data. Among them the Pg/Lg spectral ratio method is appeared to be a good discrimination technique to improve the discrimination power. Multivariate discriminant analysis is also applied to the Pg/Lg spectral ratios. The discrimination power of the Pg/Lg ratios for distance corrected three component record compared to uncorrected vertical component one shows distinct improvement. In the frequency band 4 to 14 Hz, Pg/Lg spectral ratio for distance corrected three component record provides discrimination power with a total misclassification probability of only 0.89%.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3
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• pp.169-174
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• 2003

A novel subspace speech enhancement using subband whitening filter is proposed. Previous subspace speech enhancement method either assumes additive white noise or uses whitening filter as a pre-processing for colored noise. The proposed method tries to minimize the signal distortion while reducing residual noise by processing the signal using subband whitening filter. By incorporating the notion of subband whitening filter, spectral resolution in Karhunen-Loeve(KL) domain is improved with the negligible additional computational load. The proposed method outperforms both the subspace method suggested by Ephraim and the spectral subtraction suggested by Boll in terms of segmental signal-to-noise ratio (SNRseg) and perceptual evaluation of speech quality (PESQ).

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.79-94
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• 1986

TM data, which have better resolution in spatial and spectral than MSS data, were used for this study, and several Image Processing Techniques (IPT) were examined for finding the best IPT to fit to lineament extraction and mineralized zone mapping. The Ryeongnam area was selected as test area, because the area is one of major mineralized zones in Korea and its hydrothermal alteration zone is wider and deeper than other areas. The spatial filtering method is most optimum one for limeament extraction: that is, the directional spatial filtering is most efficient to detect N-S, E-W direction lineaments on the image, and the high boost filtering can be applied for mapping all direction lineaments. The ratio method was selected for detecting altered zone. It is possible to make several tens combinations in ratio with 7 bands of TM data, but considering spectral characteristics of each band of TM to the geological meterials and vegetation, the band 4/band 3(A), band 5/band 7(B), and B/A ratio methods were chosen among them. The 5/7 ratio image did not show clearly the altered area due to noise from vegetation cover, so the 4/3 ratio imae was used for trying to decrease the effect of vegetation. As a result the B/A ratio image showed quite nicely the altered zone of the test area. In conclusion, the spatial filtering is the best image processing techniques for lineament mapping, and the B/A ratio image in TM data is useful for the mineralized zone mapping.

• ETRI Journal
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• v.31 no.4
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• pp.451-453
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• 2009

This letter proposes the use of vowel sound detection for voice activity detection. Vowels have distinctive spectral peaks. These are likely to remain higher than their surroundings even after severe corruption. Therefore, by developing a method of detecting the spectral peaks of vowel sounds in corrupted signals, voice activity can be detected as well even in low signal-to-noise ratio (SNR) conditions. Experimental results indicate that the proposed algorithm performs reliably under various noise and low SNR conditions. This method is suitable for mobile environments where the characteristics of noise may not be known in advance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.817-822
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• 2011

This paper first proposes the speech recognition algorithm by detection of the speech and noise sections at each frame using a neural network training by back-propagation algorithm, then proposes the spectral subtraction method which removes the noises at each frame according to detection of the speech and noise sections. In this experiment, the performance of the proposed recognition system was evaluated based on the recognition rate using various speeches that are degraded by white noise and car noise. Moreover, experimental results of the noise reduction by the spectral subtraction method demonstrate using the speech and noise sections detecting by the speech recognition algorithm at each frame. Based on measuring signal-to-noise ratio, experiments confirm that the proposed algorithm is effective for the speech by corrupted the noise using signal-to-noise ratio.

• Earthquakes and Structures
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• v.9 no.5
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• pp.983-997
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• 2015

This research evaluates the soil conditions for seismic stations situated in Romania using the horizontal-to-vertical spectral ratio (HVSR). The strong ground motion database assembled for this study consists of 179 analogue and digital strong ground motion recordings from four intermediate-depth Vrancea seismic events with $M_w{\geq}6.0$. In the first step of the analysis, the influence of the earthquake magnitude and source-to-site distance on the H/V curves is evaluated. Significant influences from both the earthquake magnitude and hypocentral distance are found especially for soil class A sites. Next, a site classification method proposed in the literature is applied for each seismic station and the soil classes are compared with those obtained from borehole data and from the topographic slope method. In addition, the success and error rates of this method are computed and compared with other studies from the literature. A more in-depth analysis of the H/V results is performed using data from seismic stations in Bucharest and a comparison of the free-field and borehole H/V curves is done for three seismic stations. The results show large differences between the free-field and the borehole curves. As a conclusion, the results from this study represent an intermediary step in the evaluation of the soil conditions for seismic stations in Romania and the need to perform more detailed soil classification analysis is highly emphasized.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.171-174
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• 2000

In this paper, we improve statistical-model-based speech detector using the probability that a speech occupies a frequency bin. While the previous method assumes speech energy occupies all the frequency components and use them with equal weights in the likelihood ratio test for speech detection, the proposed method assumes speech energy occupies just some frequency componets and use them with different weights in accordance with the probabilities of spectral occupancy in the test. The probability is iteratively up-dated for speech frames to contribute to the likelihood ratio test. The proposed method well reflects the characteristic distribution of speech spectrum, and yields better detection performance.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.4
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• pp.350-358
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• 2012

This paper deals with accuracy of accumulated fatigue damage estimation using stochastic fatigue analysis method based on Rayleigh PDF. From full scale measurement data on an 8100TEU container vessel, zero-order spectral moments for wave- and vibration-induced energy spectral densities are determined on the probability level of 99%. 80 simulation cases in total are prepared according to the variation of ratio of zero-order spectral moments and center frequency of vibration ESD. By using inverse Fourier transformation and rainflow cycle counting for the combined ESD of wave and vibration, exact fatigue damages are derived. Fatigue damages in frequency domain based on Rayleigh PDF show large conservativeness compared to exact fatigue damages in times domain. The main cause of the excessive conservativeness is analyzed by two aspects: ratio of zero crossing and peak frequencies and ratio of initial zero order spectral moments and zero order spectral moments from rainflow stress range distributions. Finally, a guideline of applicability of Rayleigh PDF is proposed for wide band processes.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.162-167
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• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.