• Journal of Digital Convergence
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• v.14 no.11
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• pp.249-255
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• 2016

Since the elderly voices include a lot of noise caused by physiological changes in respiration, phonation, and resonance, the performance of the convergence health-care equipments such as speech recognition, synthesis, analysis program done by elderly voice is deteriorated. Therefore it is necessary to develop researches to operate health-care instruments with elderly voices. In this study, a voice activity detection using a symmetric higher-order differential energy function (SHODEO) was developed and was compared with auto-correlation function(ACF) and the average magnitude difference function(AMDF). It was confirmed to have a better performance than other methods in the voice interval detection. The voice activity detection will be applied to a voice interface for the elderly to improve the accessibility of the smart devices.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.330-335
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• 2000

This paper deals with sensing ability of smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the two type of smart sensors for experiment. The first type of smart sensor is H2 type. The second type of smart sensor is HH type. The smart sensor was developed for recognition of material. And then we developed estimation method of sensing ability of smart sensors. The first method(Sensing Ability Index) is developed for H2 smart sensor. The second method($R_{SAI}$ Index) is developed for HH smart sensor. We estimated sensing ability of smart sensor with new SAI and $R_{SAI}$ method. This paper describes our primary study for a new method of estimate sensing ability of smart sensor, which is need for precision work system. This is a study of dynamic characteristics of smart sensor according to frequency and displacement changing with new SAI and $R_{SAI}$ method. Experiment and analysis are executed for proper dynamic sensing condition. First, we developed advanced smart sensors. Second, we develop new SAI and $R_{SAI}$ methods that have a sensing ability of distinguish materials. Dynamic characteristics of smart sensor are evaluated through new SAI and $R_{SAI}$ method relatively. We can use the new SAI and $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object(auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.687-697
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• 2023

Botnets, whose attack patterns are becoming more sophisticated and diverse, are recognized as one of the most serious cybersecurity threats today. This paper revisits the experimental results of botnet detection using autoencoder, a semi-supervised deep learning model, for UGR and CTU-13 data sets. To prepare the input vectors of autoencoder, we create data points by grouping the NetFlow records into sliding windows based on source IP address and aggregating them to form features. In particular, we discover a simple power-law; that is the number of data points that have some flow-degree is proportional to the number of NetFlow records aggregated in them. Moreover, we show that our power-law fits the real data very well resulting in correlation coefficients of 97% or higher. We also show that this power-law has an impact on the learning of autoencoder and, as a result, influences the performance of botnet detection. Furthermore, we evaluate the performance of autoencoder using the area under the Receiver Operating Characteristic (ROC) curve.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.26-31
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• 2022

Changwon City has the second highest accident rate with 79.6 according to the city bus accident rate. In fact, 250,000 people use the city bus a day in Changwon, The number of accidents is increasing gradually. In addition, a recent fire accident occurred in the engine room of a city bus (CNG) in Changwon, which has gradually expanded the public's anxiety. In the case of business vehicles, the government conducts inspections with a short inspection cycle for the purpose of periodic safety inspections, etc., but it is not in the monitoring stage. In the case of city buses, the operation records are monitored using Digital Tacho Graph (DTG). As such, driving records, methods, etc. are continuously monitored, but inspections are conducted every six months to ascertain the safety and performance of automobiles. It is difficult to identify real-time information on automobile safety. Therefore, in this study, individual automobile management solutions are presented through machine learning techniques of inspection results based on driving records or habits by linking DTG data and Vehicle Inspection Management System (VIMS) data for city buses in Changwon from 2019 to 2020.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.5
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• pp.779-803
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• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.155-164
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• 2016

Algal blooms not only destroy fish habitats but also diminish biological diversity of ecosystem which results into water quality deterioration of 4 major rivers in South Korea. The relationship between algal bloom and environmental factors had been analyzed through the cross-correlation function between concentration of chlorophyll a and other environmental factors. However, time series of cross-correlations can be affected by the stochastic structure such auto-correlated feature of other controllers. In order to remove external effect in the correlation analysis, the pre-whitening procedure was implemented into the cross correlation analysis. The modeling process is consisted of a series of procedure (e.g., model identification, parameter estimation, and diagnostic checking of selected models). This study provides the exclusive correlation relationship between algae concentration and other environmental factors. The difference between the conventional correlation using raw data and that of pre-whitened series was discussed. The process implemented in this paper is useful not only to identify exclusive environmental variables to model Chl-a concentration but also in further extensive application to configure causality in the environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7C
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• pp.712-722
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• 2002

We have investigated a novel optical decoder for a fiber-optic code division multiple access(CDMA) communication systems. The conventional optical encoder and decoder have the advantage of simple structure. However the number of users in the system is limited by the auto- and cross-correlation properties generated in decoding process. In previous studies, to improve the system performance, although they used an optical code that minimize the sidelobe and cross-correlation, could not yet find a novel methods for performance improvement in fiber-optic CDMA system. Thus, it is necessary to investigate the novel optical decode in order to improve the performance of system. In this paper, we schematize the AND gate logic element(AGLE) composed with 1$\times$2 or 1$\times$3 coupler and the optical thyristor and propose the novel optical decoder using K(weight) AGLE. The optical thyristor only passes the overlapped signal and clips other signals. Such a novel concept means that the optical thyristor can operate as a hard-limiter. We analyze the fiber-optic CDMA system using the novel optical decoder with simulation and is found that the novel optical decoder using the AGLE and optical thyristor excludes the sidelobe and cross-correlation intensity between any two sequences.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.153-160
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• 2009

In relation to digital elevation model(DEM) production using high resolution satellite imagery, existing studies present that DEM accuracy differently show according to land cover property. This study therefore proposes auto-selection method of window size for correlation matching according to land cover property of IKONOS Geo-level stereo image. For this, land cover classified image is obtained by IKONOS color image with four bands. In addition, correlation-coefficients are computed at regular intervals in pixels of the window-search area to shorten of matching time. As the results, DEM by the proposed method showed more accurate than DEM using the fixed window-size matching. We estimate that accuracy of the proposed DEM improved more than DEM by digital map and ERDAS in agricultural land.

• Journal of radiological science and technology
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• v.46 no.5
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• pp.401-408
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• 2023

Thyroid uptake measurements can be subject to measurement errors due to the scoping and positioning of the thyroid gland. To compensate for these limitations, the clinical utility of the thyroid simultaneous counting method as an alternative to thyroid uptake measurement was analyzed and evaluated experimentally through quantitative analysis of images acquired after thyroid scanning. Experimental data were obtained using a Gamma camera (GE infinia), a thyroid uptake system (KOROID 1), and a thyroid neck phantom. Based on the thyroid uptake rate of 1-5% according to the protocol of thyroid scan test (^99mTcO₄ - , 370 MBq) in normal results, ^99mTcO₄ - was set in the range of 3.7-18.5 MBq (Matrix: 256×256, Scan time: 1 min, collimator: pin hole, phantom-collimator distances: 7 cm). The acquired images were corrected for the attenuation of isotopes due to the set-up time and half-life by applying the Auto Region of interest (ROI) drawing system, and the significance of the experimental results was evaluated by Multiple linear regression analysis (SPSS, ver. 22, IBM). The thyroid uptake rate showed a significant correlation between the dose and the measured counts when using the thyroid uptake system equipment. Meanwhile, the quantitative analysis counts of phantom images using Gamma camera also showed a significant correlation. Thus confirmed that the correlation between these two experiments was statistically significant (P<0.05). The simultaneous counting protocol, which indirectly measures thyroid uptake from thyroid scans, is likely to be clinically relevant if complemented by additional studies with different variables in patients with thyroid disease.