• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.457-468
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• 2007

This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

• Journal of Preventive Medicine and Public Health
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• v.33 no.3
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• pp.343-348
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• 2000

Objectives : This study was conducted to integrate the results of studies assessing the association between chronic noise exposure and blood pressure. Methods : Using a MEDLINE search with noise exposure, blood pressure and hypertension as key words, we retrieved articles from the literature that were published from 1980 to December 1999. The criteria for quality evaluation were as follows: 1) the study subjects must have been workers employed at a high noise level area 2) The paper should use average and cumulative noise exposure as method for exposure evaluation. 3) Blood pressure in each article should be reported in a continuous scale Among the 77 retrieved articles, six studies were selected for quantitative meta-analysis. Before the integration of the regression coefficients for the association between blood pressure and noise level, homogeneity tests were conducted. Results : All studies were a cross-sectional design and the study subjects were industrial workers. Five papers used a time-weighted average for noise exposure and only one paper calculated the cumulative noise exposure level. The measurement of blood pressure in the majority of studios were accomplished in a resting stale, and used an average of two or more readings. The homogeneity of studies was rejected in a fixed effect model, so we used the results in a random effect model. The results of the quantitative meta-analysis, the weighted regression coefficient of noise associated with systolic blood pressure and diastolic blood pressure were 0.05 (95% confidence interval [CI]: -0.03, 0.13) and 0.06 (95% CI: -0.01, 0.13), respectively. Conclusions : Our results suggested that chronic exposure to industrial noise does not cause elevated blood pressure.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.8
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• pp.759-766
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• 2016

This paper is on a system design of recognizing sound sources and tracing locations from detecting a section of sound sources which is strong in surrounding environmental sounds about sound sources occurring in an abnormal situation by using signals within the section. In detection of the section with strong sound sources, weighted average delta energy of a short section is calculated from audio signals received. After inputting it into a low-pass filter, through comparison of values of the output result, a section strong in background sound is defined. In recognition of sound sources, from data of the detected section, using an HMM(: Hidden Markov Model) as a traditional recognition method, learning and recognition are realized from creating information to recognize sound sources. About signals of sound sources that surrounding background sounds are included, by using energy of existing signals, after detecting the section, compared with the recognition through the HMM, a recognition rate of 3.94% increase is shown. Also, based on the recognition result, location grasping by using TDOA(: Time Delay of Arrival) between signals in the section accords with 97.44% of angles of a real occurrence location.

• Journal of Welding and Joining
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• v.14 no.6
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• pp.131-139
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• 1996

The response of the arc sensor using the welding current and/or welding voltage as its outputs has been obtained by the analysis and/or experiments of the static characteristics of arc sensor. But in order to improve the reliability of arc sensor, it is necessary to know its dynamic characteristics. So in this paper, it is presented the dynamic model of arc sensor including the power source, arc voltage, electrode burnoff rate, and wire feed rate. A numerical simulation of the dynamic model of arc sensor was implemented, computing the welding current with input of CTWD. The results of computer simulations and experiments of $CO_2$arc welding showed that a linear relationship between weaving center - weld line distance and current area difference was established. Additionally, a real-time weld seam tracking system interfaced with industrial welding robot was constructed, the result of the weld seam tracking experiment for weld line with an initial offset error of 5$^{\circ}$was good.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.7-15
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• 2010

Research of this paper focused on developing and demonstrating of algorithm with the figures of difference between historical traffic pattern data and real-time traffic data to decide on what the incident is. The aim of this dissertation is to develop incident detection algorithm which can be understood and modified easier to operate. To establish traffic pattern of this algorithm, weighted moving average method was applied. The basis of this method was traffic volume and speed of the same day and time at the same location based on 30-second raw data. The model was completed by a serious of steps of process-screening process of error data, decision of the traffic condition, comparison with pattern data, decision of incident circumstances, continuity test. A variety of parameter value was applied to select reasonable parameter. Results of application of the algorithm came out with figures of average detection rate 94.7 percent, 0.8 percent rate of misinformation and the average detection time 1.6 minutes. With these following results, the detection rate turned out to be superior compared with result of existing model. Applying the concept of traffic patterns was useful to gain excellent results of this study. Also, this study is significant in terms of making algorithm which theorized the decision process of actual operators.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.135-142
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• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.

• Atmosphere
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• v.24 no.2
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• pp.245-251
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• 2014

Statistical trajectory analysis has been widely used to identify potential source regions for chemically and radiatively important chemical species in the atmosphere. The most widely used method is a statistical source-receptor model developed by Stohl (1996), of which the underlying principle is that elevated concentrations at an observation site are proportionally related to both the average concentrations on a specific grid cell where the observed air mass has been passing over and the residence time staying over that grid cell. Thus, the method can compute a residence-time-weighted mean concentration for each grid cell by superimposing the back trajectory domain on the grid matrix. The concentration on a grid cell could be used as a proxy for potential source strength of corresponding species. This technical note describes the statistical trajectory approach and introduces its application to estimate potential source regions of $CO_2$ enhancements observed at Korean Global Atmosphere Watch Observatory in Anmyeon-do. Back trajectories are calculated using HYSPLIT 4 model based on wind fields provided by NCEP GDAS. The identified $CO_2$ potential source regions responsible for the pollution events observed at Anmyeon-do in 2010 were mainly Beijing area and the Northern China where Haerbin, Shenyang and Changchun mega cities are located. This is consistent with bottom-up emission information. In spite of inherent uncertainties of this method in estimating sharp spatial gradients within the vicinity of the emission hot spots, this study suggests that the statistical trajectory analysis can be a useful tool for identifying anthropogenic potential source regions for major GHGs.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.247-255
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• 2002

Indoor and outdoor nitrogen dioxide (NO$_2$) concentrations were measured and compared with measurements of personal exposures of 95 persons in Seoul, Korea and 57 persons in Brisbane, Australia, respectively. Time activity diary was used to determine the impact on NO$_2$ exposure assessment and microenvironmental model to estimate the personal NO$_2$ exposure. Most people both Seoul and Brisbane spent their times more than 90% of indoor and more than 50% in home, respectively. Personal NO$_2$ exposures were significantly associated with indoor NO$_2$ levels with Pearson coefficient of 0.70 (p＜0.01) and outdoor NO$_2$ levels with Pearson coefficient of 0.66 (p＜0.01) in Seoul and of 0.51 (p＜0.01) and of 0.33 (p＜0.05) in Brisbane, respectively. Using microenvironmental model by time weighted average model, personal NO$_2$ exposures were estimated with NO$_2$ measurements in indoor home, indoor office and outdoor home. Estimated NO$_2$ measurements were significantly correlated with measured personal exposures (r = 0.69, p＜0.001) in Seoul and in Brisbane (r = 0.66, p＜0.001), respectively. Difference between measured and estimated NO$_2$ exposures by multiple regression analysis was explained that NO$_2$ levels in near workplace and other outdoors in Seoul (p = 0.023), and in transportation in Brisbane (p = 0.019) affected the personal NO$_2$ exposures.

• Journal of Environmental Science International
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• v.10 no.3
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• pp.195-200
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• 2001

The personal exposures of nitrogen dioxide(NO$_2$), microenvironmental levels and daily time activity patterns on Seoul subway station workers were measured from February 10 to March 12, 1999. Personal NO$_2$exposure for 24 hours were 29.40$\pm$9.75 ppb. NO$_2$level of occupational environment were 27.87$\pm$7.15 ppb in office, 33.60$\pm$8.64 ppb in platform and 50.13$\pm$13.04 ppb in outdoor. Personal exposure time of subway station workers was constituted as survey results with $7.94\pm$3.00 hours in office, $2.82\pm$1.63 hours in platform and 1 hours in outdoor. With above results, personal $NO_2$exposure distributions on subway station workers in Seoul were estimated with Monte Carlo simulation which uses statistical probabilistic theory on various exposure scenario testing. Some of distributions which did not have any formal patterns were assumed as custom distribution type. Estimated personal occupational $NO_2$exposure using time weighted average (TWA) model was 31.$29\pm$5.57 ppb, which were under Annual Ambient Standard (50ppb) of Korea. Though arithmetic means of measured personal $NO_2$exposure was lower than that of occupational $NO_2$exposure estimated by TWA model, considering probability distribution type simulated, probability distribution of measured personal $NO_2$exposures for 24 hours was over ambient standard with 3.23%, which was higher than those of occupational exposure(0.02%). Further research is needed for reducing these 24 hour $NO_2$personal excess exposures besides occupational exposure on subway station workers in Seoul.