• 한국정보통신설비학회:학술대회논문집
• /
• 2009.08a
• /
• pp.382-386
• /
• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.183-200
• /
• 1994

Data-adaptive sequential suboptimal designs for very complex computer simulation codes are considered based on a spatial prediction model. These designs are constructed for two simulators of the computational nuclear fusion devices model. The difficulty of constructing the optimal designs due to the irregular design region, and its alternatives are also discussed with some computational algorithms for obtaining the designs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1963-1978
• /
• 2015

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.

• Journal of Internet Computing and Services
• /
• v.10 no.2
• /
• pp.1-13
• /
• 2009

Intrusion detection is a process that identifies the attacks and responds to the malicious intrusion actions for the protection of the computer and the network resources. Due to the fast development of the Internet, the types of intrusions become more complex recently and need immediate and correct responses because the frequent occurrences of a new intrusion type rise rapidly. Therefore, to solve these problems of the intrusion detection systems, we propose a sequential pattern miner for analysis of the alert data in order to support intelligent and automatic detection of the intrusion. Sequential pattern mining is one of the methods to find the patterns among the extracted items that are frequent in the fixed sequences. We apply the prefixSpan algorithm to find out the alert sequences. This method can be used to predict the actions of the sequential patterns and to create the rules of the intrusions. In this paper, we propose an extended prefixSpan algorithm which is designed to consider the specific characteristics of the alert data. The extended sequential pattern miner will be used as a part of alert data analyzer of intrusion detection systems. By using the created rules from the sequential pattern miner, the HA(high-level alert analyzer) of PEP(policy enforcement point), usually called IDS, performs the prediction of the sequence behaviors and changing patterns that were not visibly checked.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.36 no.8
• /
• pp.873-879
• /
• 2012

Metamodels have been developed with a variety of design optimization techniques in the field of structural engineering over the last decade because they are efficient, show excellent prediction performance, and provide easy interconnections into design frameworks. To construct a metamodel, a sequential procedure involving steps such as the design of experiments, metamodeling techniques, and validation techniques is performed. Because validation techniques can measure the accuracy of the metamodel, the number of presampled points for an accurate kriging metamodel is decided by the validation technique in the sequential kriging metamodel. Because the interpolation model such as the kriging metamodel based on computer experiments passes through responses at presampled points, additional analyses or reconstructions of the metamodels are required to measure the accuracy of the metamodel if existing validation techniques are applied. In this study, we suggest a sensitivity validation that does not require additional analyses or reconstructions of the metamodels. Fourteen two-dimensional mathematical problems and an engineering problem are illustrated to show the feasibility of the suggested method.

• Journal of the Korean Geotechnical Society
• /
• v.15 no.3
• /
• pp.113-129
• /
• 1999

In this study, a total of 12 types of sequential model tests were conducted at the laboratory for small scale anchored walls. The sequential behavior for flexible wall embedded in sand was investigated by varying degrees of relative density of Joomoonjin sand and flexibility number of model wall. The model tests were carried out in a 1000mm width, 1500mm length, and 1000mm high steel box. Load cells, pressure cells, displacement transducer and dial gauges were used to measure the anchor forces, lateral wall deflections, lateral earth pressures and vertical displacements of ground surface, respectively. Limited model tests were performed to examine the parameters for soil-wall interaction model and the formulation of analytical method was revised in order to predict the behavior of anchored wall in sand. Based on the model tests and proposed analytical method, model simulations were performed and the predictions by the present approach were compared with measurements by the model tests and predictions by other commercial programs. It is shown that the prediction by the present approach simulates qualitatively well the general trend observed for model test.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.39-39
• /
• 2012

Data assimilation techniques have received growing attention due to their capability to improve prediction in various areas. Despite of their potentials, applicable software frameworks to probabilistic approaches and data assimilation are still limited because the most of hydrologic modelling software are based on a deterministic approach. In this study, we developed a hydrological modelling framework for sequential data assimilation, namely MPI-OHyMoS. MPI-OHyMoS allows user to develop his/her own element models and to easily build a total simulation system model for hydrological simulations. Unlike process-based modelling framework, this software framework benefits from its object-oriented feature to flexibly represent hydrological processes without any change of the main library. In this software framework, sequential data assimilation based on the particle filters is available for any hydrologic models considering various sources of uncertainty originated from input forcing, parameters and observations. The particle filters are a Bayesian learning process in which the propagation of all uncertainties is carried out by a suitable selection of randomly generated particles without any assumptions about the nature of the distributions. In MPI-OHyMoS, ensemble simulations are parallelized, which can take advantage of high performance computing (HPC) system. We applied this software framework for several catchments in Japan using a distributed hydrologic model. Uncertainty of model parameters and radar rainfall estimates is assessed simultaneously in sequential data assimilation.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1994.10a
• /
• pp.175-182
• /
• 1994

As for the safety evaluation of existing large-scale structures, methods for estimation of the structural and dynamic properties are studied. Sequential prediction error method in time domain and improved FRF estimator in frequency domain are comparatively studied. For this purpose, impact tests of 2 bay 3 floor steel frame structure are performed. Results from both methods are found to be consistent to each others, however those from the finite-element analysis are slightly different from experimental results.

• Proceedings of the KSME Conference
• /
• 2005.11a
• /
• pp.137.2-137.2
• /
• 2005

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10b
• /
• pp.311-313
• /
• 2000

본 논문에서는 시간이 흐름에 따라 관측되는 시계열 데이터에 대한 예측을 위한 순차적 베이지안 진화 연산기법을 제안한다. 이 방법에서는 이전 세대의 모델을 바탕으로 예측을 수행하고 새로운 데이터가 주어지면 현재의 예측 모델을 평가하여 더 좋은 모델을 생성하도록 한다. 제안된 방법을 시계열 데이터에 적용한 결과 기조의 방법보다 데이터에 적합한 모델을 학습하고 성공적인 예측을 수행함을 확인하였다.