• 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 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.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.503-506
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• 2022

The absolute decreasing in the school-age population due to the low fertility rate that has lasted for more than 20 years is a result of the lack of filling in universities. The lack of filling in general universities is more serious in universities of local area than universities in the metropolitan area, and in two-year junior colleges rather than general universities. The purpose of this study is to how the highschool grading system and university rankings have an effect on the lack of filling enterance quota for new students.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.293-299
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• 2009

In this article, we consider linear models such as regression, ARIMA (autoregressive integrated moving average), and regression+ARIMA (regression with ARIMA errors) for predicting hourly ozone concentration level in two areas of Daegu. Based on RASE(root average squared error), it is shown that the ARIMA is the best model in one area and that the regression+ARIMA model is the best in the other area. We further analyze the residuals from the optimal models, so that we might predict the ozone warning days where at least one of the hourly ozone concentration levels is over 120 ppb. Based on the training data in the years from 2000 to 2003, it is found that 35 ppb is a good cutoff value of residulas for predicting the ozone warning days. In on area of Daegu, our method predicts correctly one of two ozone warning days of 2004 as well as all of the remaining 364 non-warning days. In the other area, our methods predicts correctly all of one ozone warning days and 365 non-warning days of 2004.

• Progress in Medical Physics
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• v.20 no.3
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• pp.174-179
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• 2009

To track moving tumor in real time, CyberKnife system imports a technique of the synchrony respiratory tracking system. The fiducial marker which are detectable in X-ray images were demand in CyberKnife Robotic radiosurgery system. It issued as reference markers to locate and track tumor location during patient alignment and treatment delivery. Fiducial marker implantation is an invasive surgical operation that carries a relatively high risk of pneumothorax. Most recently, it was developed a direct lung tumor registration method that does not require the use of fiducials. The purpose of this study is to measure the accuracy of target applying X-sight lung tracking using the Gafchromic film in dynamic moving thorax phantom. The X-sight Lung Tracking quality assurance motion phantom simulates simple respiratory motion of a lung tumor and provides Gafchromic dosimetry film-based test capability at locations inside the phantom corresponding to a typical lung tumor. The total average error for the X-sight Lung Tracking System with a moving target was $0.85{\pm}0.22$ mm. The results were considered reliable and applicable for lung tumor treatment in CyberKnife radiosurgery system. Clinically, breathing patterns of patients may vary during radiation therapy. Therefore, additional studies with a set real patient data are necessary to evaluate the target accuracy for the X-sight Lung Tracking system.

• 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 the Institute of Electronics Engineers of Korea SD
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• v.47 no.10
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• pp.34-41
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• 2010

We proposed the new pixel compensation circuit with high aperture ratio and the driving method for the large-area, low-power AMOLED applications in this study. We designed with the low-temperature poly-silicon(LTPS) thin film transistors(TFTs) that has poor uniformity but good mobility and stability. To lower the error rate of the pixel circuit and to improve the aperture ratio for bottom emission method, we simplified the pixel compensation circuit. Because the proposed pixel compensation circuit with high aperture ratio has very low contrast ratio for conventional driving methods, we proposed the new driving method and circuit for high contrast ratio. Black data insertion was introduced to improve the characteristics for moving images. The pixel circuit was designed for 19.6" WXGA bottom-emission AMOLED panel, and the average aperture ratio of the pixel circuit is improved from 33.0% to 41.9%. For the TFT's $V_{TH}$ variation of ${\pm}0.2\;V$, the non-uniformity and contrast ratio of the designed panel was estimated under 6% and over 100000:1 respectively.