• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.614-618
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• 2005

수문시계열 분석과 예측은 대부분 ARMA(AutoRegressive Moving Average) 형태의 선형적인 추계학적인 모형을 이용하였으나 자현현상이 복잡해지고 비선형적인 특성을 가짐에 따라 선형적인 해석은 수문시계열의 분석과 예측에 있어서 많은 오류를 내포하고 있다. 이와 같은 문제를 해결하기 위한 시도로 Chaos이론이란 개념이 사용되기 시작하였으며, 수자원분야에서는 1980년대 후반부터 물수지 방정식 및 강우유출에 대한 카오스적 특성분석 등 많은 연구가 진행되었다. 본 연구에서는 영산강유역의 본류를 대표하는 나주지점을 대상으로 2003년 1월 1일 00시부터 2004년 12월 31일 23시까지 17,544개의 시수위 자료에 대하여 해당 년도의 Rating-Curve식을 적용 환산한 유출량자료에 데한 카오스적 특성을 분석하였다. 카오스적 특성을 분석하기에 앞서 원자료에 대하여 이동평균법과 Savitzky-Golay Filter를 적용하여 잡음을 제거하였으며, 1차원의 단일변량의 자료에 대한 상태공간(Phase Space)의 재건을 통하여 비교검토 하였다. 이러한 일련의 과정을 거친 자료에 대하여 상관차원법을 이용하여 영산강 유역의 나주지점의 시유출량 자료에 대한 카오스적 특성을 분석한 결과 저차원의 수렴으로 카오스 특성을 가졌다.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.373-382
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• 1998

Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) models presented by Box and Jeckins(1976). If the data exhibits no ap-parent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARIMA(p,q) model is fit to the given data. Selection of the orders of p and q is one of the crucial steps in Time Series Analysis. Most of the methods to determine p and q are based on the autocorrelation function and partial autocor-relation function as suggested by Box and Jenkins (1976). many new techniques have emerged in the literature and it is found that most of them are over very little use in determining the orders of p and q when both of them are non-zero. The Durbin-Levinson algorithm and Innovation algorithm (Brockwell and Davis 1987) are used as recur-sive methods for computing best linear predictors in an ARMA(p,q)model. These algorithms are modified to yield an effective method for ARMA model identification so that the values of order p and q can be determined from them. The new method is developed and its validity and usefulness is illustrated by many theoretical examples. This method can also be applied to an real world data.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.831-838
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• 2013

This paper focused on the design of an efficient temperature controller for a plant with a wide range of operating temperatures. The greater the temperature difference a plant has, the larger the nonlinearity it is exposed to in terms of heat transfer. For this reason, we divided the temperature range into five sections, and each was modeled using ARMAX(auto regressive moving average exogenous). The movement of the dominant poles of the sliced system was analyzed and, based on the variation in the system parameters with temperature, optimal control parameters were obtained through simulation and experiments. From the configurations for each section of the temperature range, a temperature-based gain-scheduled controller (TBGSC) was designed for parameter variation of the plant. Experiments showed that the TBGSC resulted in improved performance compared with an existing proportional integral derivative (PID) controller.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.37-43
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• 2005

This paper presents a hybrid control algorithm for the active noise control in the rectangular enclosure using an active/passive foam actuator. The hybrid control composes of the adaptive feedforward with feedback loop in which the adaptive feedforward control uses the well-known filtered-x LMS(least mean square) algorithm and the feedback loop consists of the sliding mode controller and observer. The hybrid control has its robustness for both transient and persistent external disturbances and increases the convergence speed due to the reduced variance of the jiltered-x signal by adding the feedback loop. The sliding mode control (SMC) is used to incorporate insensitivity to parameter variations and rejection of disturbances and the observer is used to get the state information in the controller deign. An active/passive smart foam actuator is used to minimize noise actively using an embedded PVDF film driven by an electrical input and passively using an absorption-foam. The error path dynamics is experimentally identified in the form of the auto-regressive and moving-average using the frequency domain identification technique. Experimental results demonstrate the effectiveness of the hybrid control and the feasibility of the smart foam actuator.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.21-34
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• 1998

[n this paper, we propose a hierarchical self-organizing fuzzy logic controller to improve the performance of the FARMA(Fuzzy auto-regressive moving average) SOC(Self-organizing fuzzy logic controller) when the system parameters change. The proposed controller contains the FARMA SC)C in the lower level and has a coordinator in the higher level, which evaluates convergence. and when it senses the degradation of system performance it compensates the control input by a look-up table. The proposed controller shows good perforniance over the FARMA SOC when the system parameters change. We executed some computer simulations on the regulation problem of an inlrerted pendulum system and compared the results with those of the FARMA SOC. As a result, it ha:; been shown that the proposed controller outperformed the FARMA SOC when the changes of the system parameters occurred.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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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.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of the Korean Institute of Gas
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• v.23 no.5
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• pp.52-58
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• 2019

In this study, we have developed a forecasting model for city- gas acceptance. City-gas corporations have to report about city-gas sale volume next year to KOGAS. So it is a important thing to them. Factors influenced city-gas have differences corresponding to usage classification, however, in city-gas acceptence, it is hard to classificate. So we have considered tha outside temperature as factor that influence regardless of usage classification and the model development was carried out. ARIMA, one of the traditional time series analysis, and LSTM, a deep running technique, were used to construct forecasting models, and various Ensemble techniques were used to minimize the disadvantages of these two methods.Experiments and validation were conducted using data from JB Corp. from 2008 to 2018 for 11 years.The average of the error rate of the daily forecast was 0.48% for Ensemble LSTM, the average of the error rate of the monthly forecast was 2.46% for Ensemble LSTM, And the absolute value of the error rate is 5.24% for Ensemble LSTM.

• Journal of Distribution Science
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• v.12 no.6
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.