• 제어로봇시스템학회논문지
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• 제13권2호
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• pp.179-186
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• 2007

This paper presents new ignition method and ignition recognition logic for a microturbine. New ignition method is designed by constant speed control of a microturbine with pre-determined time during a ignition period. It make more accurate air-fuel ratio as well as give enough time to ignition system to have full performance under cold temperature. And ignition recognition logic is designed by observing output current change of inverter by generating output torque of a microturbine in the instant of ignition. For filtering a output torque current of inverter with high frequency, we applied a moving average method. So far, ignition recognition is usually implemented by measuring of exhausted gas temperature(EGT) of microturbine. The proposed logic can give more accurate judgement of ignition as well as keep a good working of starting system under out of order a temperature measuring system and biased initial value of EGT sensor. Finally, the two proposed logics are proved by field operating a microturbine under various conditions.

• Journal of Communications and Networks
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• 제7권2호
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• pp.126-134
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• 2005

Reducing energy consumption in mobile hosts (MHs) is one of the most critical issues in wireles/mobile networks. IP paging protocol at network layer and power saving mechanism (PSM) at link layer are two core technologies to reduce the energy consumption of MHs. First, we investigate the energy efficiency of the current IEEE 802.11 power saving mechanism (PSM) when IP paging protocol is deployed over IEEE 802.11 networks. The result reveal that the current IEEE 802.11 PSM with a fixed wakeup interval (i.e., the static PSM) exhibits a degraded performance when it is integrated with IP paging protocol. Therefore, we propose an adaptive power saving mechanism in IEEE 802.11-based wireless IP networks. Unlike the static PSM, the adaptive PSM adjusts the wake-up interval adaptively depending on the session activity at IP layer. Specifically, the MH estimates the idle periods for incoming sessions based on the exponentially weighted moving average (EWMA) scheme and sets its wake-up interval dynamically by considering the estimated idle period and paging delay bound. For performance evaluation, we have conducted comprehensive simulations and compared the total cost and energy consumption, which are incurred in IP paging protocol in conjunction with various power saving mechanisms: The static PSM, the adaptive PSM, and the optimum PSM. Simulation results show that the adaptive PSM provides a closer performance to the optimum PSM than the static PSM.

• Journal of Power Electronics
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• 제8권4호
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of Biosystems Engineering
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• 제38권1호
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• pp.9-17
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• 2013

Purpose: The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• 한국재난정보학회 논문집
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• 제16권4호
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• pp.650-657
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• 2020

연구목적: 본 연구에서는 청주시에 시범운영한 PLSP(Priority Lane and Signal Preemption: 긴급차량 우선신호와 우선차로)제도 사업 효과를 분석하였다. 연구방법: 시범사업 구간(3.8km)을 대상으로 1.2km 구간에 VISSIM 프로그램을 활용하여 시범운영구간의 차량 데이터를 실측 분석하였다. 우선신호의 경우 경찰관이 CCTV로 교차로를 모니터링하여 긴급차량 접근 시 청색신호로 변경하는 방법이 사용되었고, 우선차로의 경우 노면에 긴급차량 우선차로를 표시하여 우선통행이 가능하도록 하였다. 연구결과: 시뮬레이션 분석 결과, PLSP 도입 시 긴급차량의 이동속도는 약 2배 증가한 42km/h로 PLSP 도입 전에 비해 약 3분 가량이 단축되었으며, 기존 대비 69%에 이르는 개선 효과가 있었다. 청주시 시범운영 결과, 평균 도달시간은 1차 기간 4분 14초 2차 기간 5분 40초로 약 2분의 시간 단축 효과가 나타났다. 결론: 본 PLSP 제도는 긴급차량의 현장 도착 시간 단축에 효과적인 것으로 분석되었다.

• 한국환경과학회지
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• 제15권8호
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• pp.701-718
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• 2006

The average ratio of the daily UV-B to total solar (75) irradiance at Busan (35.23$^{\circ}$N, 129.07$^{\circ}$E) in Korea is found as 0.11%. There is also a high exponential relationship between hourly UV-B and total solar irradiance: UV-B=exp (a$\times$(75-b))(R$^2$=0.93). The daily variation of total ozone is compared with the UV-B irradiance at Pohang (36.03$^{\circ}$N, 129.40$^{\circ}$E) in Korea using the Total Ozone Mapping Spectrometer (TOMS) data during the period of May to July in 2005. The total ozone (TO) has been maintained to a decreasing trend since 1979, which leading to a negative correlation with the ground-level UV-B irradiance doting the given period of cloudless day: UV-B=239.23-0.056 TO (R$^2$=0.52). The statistical predictions of daily total ozone are analyzed by using the data of the Brewer spectrophotometer and TOMS in East Asia including the Korean peninsula. The long-term monthly averages of total ozone using the multiplicative seasonal AutoRegressive Integrated Moving Average (ARIMA) model are used to predict the hourly mean UV-B irradiance by interpolating the daily mean total ozone far the predicting period. We also can predict the next day's total ozone by using regression models based on the present day's total ozone by TOMS and the next day's predicted maximum air temperature by the Meteorological Mesoscale Model 5 (MM5). These predicted and observed total ozone amounts are used to input data of the parameterization model (PM) of hourly UV-B irradiance. The PM of UV-B irradiance is based on the main parameters such as cloudiness, solar zenith angle, total ozone, opacity of aerosols, altitude, and surface albedo. The input data for the model requires daily total ozone, hourly amount and type of cloud, visibility and air pressure. To simplify cloud effects in the model, the constant cloud transmittance are used. For example, the correlation coefficient of the PM using these cloud transmissivities is shown high in more than 0.91 for cloudy days in Busan, and the relative mean bias error (RMBE) and the relative root mean square error (RRMSE) are less than 21% and 27%, respectively. In this study, the daily variations of calculated and predicted UV-B irradiance are presented in high correlation coefficients of more than 0.86 at each monitoring site of the Korean peninsula as well as East Asia. The RMBE is within 10% of the mean measured hourly irradiance, and the RRMSE is within 15% for hourly irradiance, respectively. Although errors are present in cloud amounts and total ozone, the results are still acceptable.

• 한국응용곤충학회지
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• 제23권4호
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• pp.237-241
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• 1984

이 시험은 우리나라 남부지방에 있어서 끝동매미충의 알에 기생율이 높은 Paracentrobia andoi의 포장생태에 대해서 조사한 결과는 다음과 같다. 1. 월동후 활동은 3월상순부터 활발하였으며 4월상순 이후 부터는 휴한답로 옮겨가는 경향이였다. 2 .휴한답의 둑새풀에서 끝동매미충의 알에 대한 기생율은 끝동매미충의 산란최성기인 4월하순에서 5월상순 사이에 $21\%$ 높은 경향이였다. 3. 휴한답에서 지역별 평균 기생율은 거창과 남지는 $0\%$, 남해, 김해 및 진주는 $9.6\~29.2\%$였다. 4. 본답에 있어서 이 알기생봉은 3회 발생을 하였으며 발생최성기는 8월중순과 10월초순경이였다. 5. 본답기에 있어서 끝동매미충의 2세기충(6월하순경 발생)과 3세대충(8월하순경 발생)에 대한 이 알기생봉의 평균 기생율은 각각 $16.5\%$와 $36.7\%$였다.

• 한국멀티미디어학회논문지
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• 제11권2호
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• pp.182-196
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• 2008

센서 네트워크를 이용한 위치 추정은 많은 연구가 되어왔다. 실내 혹은 실외에서 위치추정 방식의 차이를 고려한 방법이 연구되고 있다. 실외의 위치 추정에서 RSSI(Received Signal Strength Indication) 값을 통하여 단일 시간 동안 일정하게 한 분포를 가지기 때문에 추론이 가능하지만, 실내는 다중경로와 간섭이 실외보다 높고, 그 밖에 다른 변수로 인해 추론하기가 어렵다. 논문에서는 이동평균과 K-means 알고리즘을 통해 다중경로와 간섭으로 변화된 RSSI 정보를 보정하고, 단일 시간 동안 수신된 수신신호의 집단에서 신뢰성을 가진 RSSI의 값에 대한 추론을 제안한다. 또한 위치추정에서 보정노드를 이용하여 네트워크에 속한 고정 노드에 가중치를 두는 방법을 제안하고, 네트워크 재설정을 통해 기존의 방식을 시스템 상에서 새롭게 구현하여 위치인지에 대한 효율성을 비교 평가한다.