• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.329-340
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• 1999

We consider a Bayesian forcasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under multiprocess dynamic generalized linear models. The multiprocess dynamic model offers a powerful framework for the modelling and analysis of time series which are subject to a abrupt changes in pattern. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Journal of Communications and Networks
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• 제8권2호
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• pp.234-240
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• 2006

We study the dynamic burst assembly based on traffic prediction and offset and delay differentiation in optical burst switching network. To improve existing burst assembly mechanism and build an adaptive flexible optical burst switching network, an approach called quality of service (QoS) based adaptive dynamic assembly (QADA) is proposed in this paper. QADA method takes into account current arrival traffic in prediction time adequately and performs adaptive dynamic assembly in limited burst assembly time (BAT) range. By the simulation of burst length error, the QADA method is proved better than the existing method and can achieve the small enough predictive error for real scenarios. Then the different dynamic ranges of BAT for four traffic classes are introduced to make delay differentiation. According to the limitation of BAT range, the burst assembly is classified into one-dimension limit and two-dimension limit. We draw a comparison between one-dimension and two-dimension limit with different prediction time under QoS based offset time and find that the one-dimensional approach offers better network performance, while the two-dimensional approach provides strict inter-class differentiation. Furthermore, the final simulation results in our network condition show that QADA can execute adaptive flexible burst assembly with dynamic BAT and achieve a latency reduction, delay fairness, and offset time QoS guarantee for different traffic classes.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• 한국안전학회지
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• 제7권2호
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• pp.71-79
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• 1992

This paper presents a least-square algorithms of lattice structures and their use for adaptive prediction of time series generated from the dynamic system. As the view point of adaptive prediction, a new method of Identification of dynamic characteristics by means of estimating the parameters of linear auto regressive model is proposed. The fast convergence of adaptive lattice algorithms is seen to be due to the orthogonalization and decoupling properties of the lattice. The superiority of the least-square lattice is verified by computer simulation, then predictor coefficients are computed from the linear sequential time data. For the application to the dynamic characteristic analysis of unknown system, the transfer function of ideal system represented in frquency domain and the estimated one obtained by predicted coefficients are compared. Using the proposed method, the damping ratio and the natural frequency of a dynamic structure subjected to random excitations can be estimated. It is expected that this method will be widely applicable to other technical dynamic problem in which estimation of damping ratio and fundamental vibration modes are required.

• Smart Structures and Systems
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• 제26권1호
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.869-874
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• 2001

In order that precision equipment using high precision industrial operate normally. vibration criteria of expected area that equipment be set up is micrometer level. that method is a trust design for apply to in field, when there attend to quantifiable method. Hence, semi -empirical method that using on the basis of experimental data about undefined information (properities of vibration source, dynamic properities of structure, etc.,) for prediction of vibration response make the use of dynamic structure design of semiconductor ＆ TFT-LCD in the inside and outside country. Like this, for doing an optimal design of dynamic about structure, it is best important to get trust data that apply to semi-empirical method that is method of prediction vibration level. In this paper, on the basis of experimental data which was offered by a manufacturing company Of precisin equipment that plan to set up in semiconductor factory, we predicted vibration response on expected area that equipment be set up.

• 한국항공우주학회지
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• 제31권8호
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• pp.19-26
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• 2003

비정상 Euler 방정식 틀에서 동안정 미계수의 정상 예측 방법을 제안하였다. 새로운 접근방법은 비정상 지배방정식을 수정하지 않고 정상 예측방법을 적용하도록 해 준다. 제안된 방법을 통해 lunar 코닝운동 및 나선운동을 사용하여 피치감쇠 계수 합과 개별 값을 계산하였다. ANSR 형상과 Basic Finner 형상에 대한 계산결과는 PNS 계산결과, 실험치, 그리고 비정상적 예측방법을 사용한 결과와 잘 일치하고, 직교좌표계에서 정상적 예측 방법이 피치감쇠 계수의 예측에 성공적으로 적용될 수 있음을 보여준다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1945-1950
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• 2000

When one build a building that posses Precison production process to be sensitive to vibration and SMD to procuce a large dynamic force, how do one predict & answer vibration control problem at building structure design at first stage, That is a question. It has tried to predict dynamic response and establish answering about global or local dynamic problem in building using experimental and analysis method. One of such a try, it be proposed Semi-Empirial Method that reduce error element of input information about dynamic analysis using dynamic experimental study and measurement data in the basis of real-structure. In this paper, the dynamic response problem about RC-structure building that will be set-up SMD produce large transient dynamic force using Semi-Empirical Method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.772-775
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• 2005

Most engineers, related to soil and civil dynamic field, have been interested in the direct dynamic design of building transmitted from soil and rock to structure due to blasting. However it is not easy to estimate the dynamic response of structures due to blasting by using analytical method because of difficulties of soil modeling, prediction of excitation force and so on. In this paper, dynamic analysis have been performed to predict vibration level and evaluate dynamic safety of structure adjacent to tunnel blast and the semi empirical method, which is based on vibration measurement data, has been employed to consider blast vibration characteristics.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2004년도 춘계학술발표논문집
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• pp.86-96
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• 2004

Identification of dynamics of short circulation during grade change operations in paper mills is very important for the effective plant operation. In the present study a prediction method of One Pass Retention(OPR) is proposed based on the neural network. The present method is used to analyze the dynamics of short circulation during grade change. Properties of the product paper largely depend upon the change in the OPR. In the present study the OPR is predicted from the training of the network by using grade change operation data. The results of the prediction are applied to the modeling equation to give flow rates and consistencies of short circulation.