• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.192-200
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• 2021

In the mid of the December 2019, the virus has been started to spread from China namely Corona virus. It causes fatalities globally and WHO has been declared as pandemic in the whole world. There are different methods which can fit such types of values which obtain peak and get flattened by the time. The main aim of the paper is to find the best or nearly appropriate modeling of such data. The three different models has been deployed for the fitting of the data of Coronavirus confirmed patients in Pakistan till the date of 20^th November 2020. In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Logistic model, Gompertz model and Auto-Regressive Integrated Moving Average Model (ARIMA) model. The fitted models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan.

• 한국항공우주학회지
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• 제49권9호
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• pp.729-738
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• 2021

본 연구에서는 실속 받음각 근처에 발생하는 익형 위의 유동박리를 억제하기 위하여 인공신경망 기반의 피드백 유동제어를 NACA0015 익형에 수치적으로 적용하였다. 익형 위 박리영역 크기의 축소화라는 제어 목표를 달성하기 위해 익형의 박리 지점 근처에 인위적 외란(Blowing & Suction) 제어 신호를 적용하였다. 유동의 운동을 나타내는 시스템 모델링 단계에서 압력데이터에 적합직교분해(Proper Orthogonal Decomposition)를 적용하여 유동제어에 필요한 운동 모드를 추출하고 유동의 특성을 분석하였다. 분해된 모드를 기반으로 NARX(Nonlinear AutoRegressive Exogenous) 구조의 인공 신경망을 학습하여 유동의 운동을 나타내도록 하였으며, 최종적으로 피드백 제어루프에 작동시켰다. 예측된 제어신호를 CFD 해석에 적용하였으며 제어 유/무에 따른 공력특성을 분석하고 익형 주변의 고유 공간모드의 변화를 비교하여 제어 효과를 분석하였다. 본 연구에서 진행된 피드백 제어는 약 29%의 압력항력 감소효과를 보여주었으며, 이는 익형 뒷전의 큰 압력회복으로 인해 나타나는 것을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.21-23
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup rates when control rod and boron were adjusted in load following operations. Data of UCN 3 were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and seems to be utilized as a handy tool for the use of a plant simulation.

• 한국기계가공학회지
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• 제2권2호
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• pp.60-66
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• 2003

For the estimation of chatter lobe boundary it is very important to calculate the natural mode of cutting process. There are many time series algorithms for getting the natural mode of structural endmilling dynamics considering the cutting process. In this study, we have compared several time series methods such as AR algorithm, ARX, ARMAX, ARMA, Box Jenkins, Output Error, Recursive ARX, Recursive ARMAX considering the sampling frequency. As a results, the ARX, ARMAX and IV 4 are more desirable algorithms for the calculation of modal parameters such as natural frequency and damping ratio In endmilling operation. Also these algorithms may be adopted for the natural mode estimation of endmilling operation for chatter lobe prediction.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1437-1440
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• 2007

Many hydroclimatic time series are marked by interannual and longer quasi-period features that are associated with narrow band oscillatory climate modes. A time series modeling approach that directly considers such structures is developed and presented. The essence of the approach is to first develop a wavelet decomposition of the time series that retains only the statistically significant wavelet components, and to then model each such component and the residual time series as univariate autoregressive processes. The efficacy of this approach is demonstrated through the simulation of observed and paleo reconstructions of climate indices related to ENSO and AMO, tree ring and rainfall time series. Long ensemble simulations that preserve the spectral attributes of the time series in each ensemble member can be generated. The usual low order statistics are preserved by the proposed model, and its long memory performance is superior to the direction application of an autoregressive model.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• 한국항공우주학회지
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• 제32권3호
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• pp.45-50
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• 2004

위성의 성공적인 임무 수행을 위한 자세 안정화와 성능요구조건을 만족하기 위해서 반작용휠 제어로직의 설계가 중요하다. 실제 위성궤도 상에서 발생하는 여러 가지 불확실성으로 인해 지상실험을 통해 획득한 모델 파라미터 값들만으로 제어로직을 설계하는데 한계가 있다. 그러므로 위성이 궤도상에 있을 때의 반작용휠 입력 및 출력 데이터를 이용하여 모델 파라미터를 보정하고 자세제어기에 반영하는 것이 요구된다. 본 논문에서는 다목적실용위성의 Telemetry 데이터를 활용한 시스템인식 (System Identification)을 수행하였고, 이를 통한 반작용휠의 모델 파라미터를 추출한다. 또한, 반작용휠을 모델링 하고 또한 제어기설계에 사용된 모델 파라미터를 추출하여 지상실험 데이터와 비교분석한다.

• 대한전자공학회논문지TC
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• 제43권2호
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• pp.39-43
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• 2006

본 논문은 잡음이 존재하는 미지 시스템 출력 신호의 3차 통계치인 cumulant를 이용한 AR 식별에 관한 것이다. 미지 시스템 식별을 위한 알고리즘에서는 Parametric Modeling 기법중에서 Global Convergence 보장 및 시스템의 진폭과 위상 정보를 모두 표현할 수 있는 Cumulant를 이용한 AR (Auto Regressive) 식별 방법을 적용하였다. 식별 과정에서 미지 시스템을 하나의 AR 시스템으로 간주하였고 입력 신호를 발생하여 이를 통과시킨 후 잡음이 부가된 출력 신호를 얻어 이를 이용하였다. 신호대 잡음비의 변화에따른 AR 시스템의 식별을 수행한 결과 원래의 시스템 출력치와 유사한 양호한 식별 결과를 얻을 수 있었고 극점이 z 변환의 단위원내에 존재함을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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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.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.