• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.625-632
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• 2021

This paper studies the effect of the number of filters in the CNN (Convolutional Neural Network) layer on the performance of a noise attenuator. Speech is estimated from a noised speech signal using a 64-neuron, 16-kernel CNN filter and an error back-propagation algorithm. In this study, in order to verify the performance of the noise attenuator with respect to the number of filters, a program using Keras library was written and simulation was performed. As a result of simulation, it can be seen that this system has the smallest MSE (Mean Squared Error) and MAE (Mean Absolute Error) values when the number of filters is 16, and the performance is the lowest when there are 4 filters. And when there are more than 8 filters, it was shown that the MSE and MAE values do not differ significantly depending on the number of filters. From these results, it can be seen that about 8 or more filters must be used to express the characteristics of the speech signal.

• Journal of Power Electronics
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• v.9 no.5
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• pp.748-756
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• 2009

This paper presents a compensation algorithm for position error due to an amplitude imbalance between resolver output signals. Resolvers are typically used to obtain absolute position information for motor drive systems in severe environments. Position error is caused by an amplitude imbalance of the resolver output signals. As a result, the d- and q-axis currents of synchronous reference frame have periodic ripples in the stator fundamental frequency in permanent magnet synchronous motor (PMSM) drive systems. Therefore, this paper proposes a compensation algorithm to reduce the position error generated by the amplitude imbalance. The proposed method does not require any additional hardware, and reduces computation time with a simple integral operation according to rotor position. In addition, the position error can be directly compensated for by the estimated position error. The effectiveness of the proposed compensation algorithm is verified through several simulations and experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.10B
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• pp.1832-1840
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• 2000

The error diffusion algorithm is good for reproducing continuous images to binary images. However the reproduction of edge characteristics is weak in power spectrum an analysis of display error. In this paper an edge enhanced error diffusion method is proposed to improve the edge characteristic enhancement. Spatial gradient information in original image is adapted for edge enhance in threshold modulation of error diffusion. First the horizontal and vertical second order differential values are obtained from the gradient of peripheral pixels(3x3) in original image. second weighting function is composed by function including absolute value and sign of second order differential values. The proposed method presents a good visual results which edge characteristics is enhanced. The performance of the proposed method is compared with that of the conventional edge enhanced error diffusion by measuring the edge correlation and the local average accordance over a range of viewing distances and the RAPSD of display error.

• Journal of the Ergonomics Society of Korea
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• v.9 no.1
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• pp.35-42
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• 1990

This study is concerned with the accuracy, of error with which subjects can interpolate the location of a target between two graduation markers with 4 orientations and 6 sizes CRT display. Stimuli were graphic images on CRT with a linear, end-markec, ungraduated scales having a target. The location of a target is estimated in units over te range 1-99. Smallest error of estimates was at the near ends and middle of the base-line. The median error was less than 2 units, modal error was 1, and the most error (; 99.7%) was within 10. A proper size to make an minimum error in interpolation exists such that size 400 pixels. Interpolation estimation is shown to be affected by the size, location and interaction (orientation x location, size x location). The accuracy, interpolation performance are discussed in relation to absolute error associated with visual performance.

• Journal of The Korean Society of Integrative Medicine
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• v.5 no.1
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• pp.43-53
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• 2017

Purpose : To examine the relative absolute reliability and validity of step test (ST) scores in subjects with chronic stroke. Method : A total of 27 stroke patients, participated in the study. A relative reliability index (intraclass correlation coefficient, ICC) was used to examine the level of agreement of inter-rater test-retest reliability for ST score. Absolute reliability indices, including the standard error of measurement(SEM) and the minimal detectable change (MDC), and limits of agreement by Bland and Altman analysis. The validity was demonstrated by spearman correlation of ST score with 10 m Walk Test (10mWT), Fugl-Meyer Assessment-Lower/Extremity (FMA-L/E)-total score, Berg Balance Scale (BBS)-total score. Result : An excellent inter-rater reliability in ST scores was found (paretic, ICC=0.993~0.996; nonparetic, ICC=0.982~0.991). In addition, excellent test-retest reliability was found (paretic, ICC=0.992; nonparetic, ICC=0.967). It all showed acceptable SEM of the ST score as paretic and nonparetic were 0.22 and 0.46 respectively (average score <10 %), and the MDC of the paretic and nonparetic were 0.61 and 1.27 respectively (possible highest score <20 %). indicating that measures had a small and acceptable measurement error. The ST score of paretic and nonparetic were also found to be significantly associated with 10MWT (r=0.77~0.79), FMA-LE scores (r=0.73~0.81) and BBS scores (r=0.72~0.76). Conclusion : The ST showed highly sufficient Inter-rater test-retest agreement and validity and acceptable measurement errors caused by due to chance variation in measurement. It also can be used by clinicians and researchers to assess the balance and mobility performance and monitor functional change in chronic stroke patients.

• Fire Science and Engineering
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• v.19 no.2 s.58
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• pp.1-7
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• 2005

An accurate knowledge of the minimum oxygen concentration(MOC) is important in developing appropriate prevention and control measures in industrial fire protection. In this study, by using the literature data and RSM(response surface methodology), the new equations for predicting the MOC are proposed. The A.A.P.E.(average absolute percent error) and the A.A.D.(average absolute deviation) of the reported and the calculated MOC for hydrocarbons were $3.48\%\;and\;0.37\;vol\%$, respectively and the correlation coefficient was 0.919. The A.A.P.E and the A.A.D of the reported and the calculated MOC for halogenated hydrocarbons and hydrocarbons were $5.06\%$ and $0.59vo1\%$, and the correlation coefficient was 0.938. The values calculated by the proposed equations were in good agreement with the literature data. Therefore, it is expected that this proposed equations will support the use of the research for other flammable substances.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.723-734
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• 2021

Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.53-60
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• 2023

This study compared the absolute error of estimated fundamental frequency (AEF0) using voice - (VRP) and speech range profile (SRP) tasks across various etiological groups with voice disorders. Additionally, we explored the association between AEF0 and related voice parameters within each specific etiological group. The participants included 120 individuals, comprising 30 each from the functional (FUNC), organic (ORGAN), and eurological (NEUR) voice disorder groups, and a normal control group (NC). Each participant performed voice and SRP tasks, and the fundamental frequency of connected speech was measured using electroglottography (EGG). When comparing the AEF0 measures across the etiological groups, there were no differences in Grade and Severity among the patients. However, variations were observed in AEF0_VRP and AEF0_SUM. Specifically, AEF0_VRP was higher in the ORGAN group than in the FUNC and NC groups, whereas AEF0_SUM was higher in the ORGAN group than in the NC group. Furthermore, within FUNC and NEUR, AEF0 showed a positive correlation with Grade, while in ORGAN, it exhibited a positive correlation with the mean closed quotient (CQ). Attention should be paid to the application of AEF0 measures and related voice variables based on the etiological group. This study provides foundational information for the clinical application of AEF0 measures.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.