Earticle

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare algorithms for data analysis of industrial accidents and this paper provides an optimal predicting model of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. Also, this paper provides an approximation model for an optimal predicting model based on NN. The approximation model provided in this study can be utilized for easy interpretation of data analysis using NN. This study uses selected ten independent variables to group injured people according to a dependent variable in a way that reduces variation. In order to find an optimal predicting model among 5 algorithms, a retrospective analysis was performed in 67,278 subjects. The sample for this work chosen from data related to industrial accidents during three years () in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

The design variables and material properties as well as the external loads concerned with structural engineering are used to be deterministic in optimization process. These values, however, have variability from expected performance. Therefore, deterministic optimum designs that are obtained without taking these uncertainty into account could lead to unreliable designs, which necessitates the Reliability-Based Design Optimization(RBDO). RBDO involves an evaluation of probabilistic constraints which constitutes another optimization procedure. So, an expensive computational cost is required. Therefore, how to decrease the computational cost has been an important challenge in the RBDO research field. Approximation models, response surface model and Kriging model, are employed to improve an efficiency of the RBDO.

In this paper, we used computerized glow curve deconvolution (CGCD) software with several models for the simulation of a TL glow curve which was used for analysis. By using the general approximation plus model, parameters values of the glow curve were analyzed and compared with the other models parameters (general approximation, mixed order kinetics, general order kinetics). The LiF:Mg,Cu,Si and the LiF:Mg,Cu,P material were used for the glow curve analysis. And we based on figure of merits (FOM) which was the goodness of the fitting that was monitored through the value between analysis model and TLD materials. The ideal value of FOM is 0 which represents a perfect fit. The main glow peak makes the most effect of radiation dose assessment of TLD materials. The main peak of the LiF:Mg,Cu,Si materials has a intensity rate 80.76% of the whole TL glow intensity, and that of LiF:Mg,Cu,P materials has a intensity rate 68.07% of the whole TL glow intensity. The activation energy of LiF:Mg,Cu,Si was analyzed as 2.39 eV by result of the general approximation plus(GAP) model. In the case of mixed order kinetics (MOK), the activation energy was analyzed as 2.29 eV. The activation energy was analyzed as 2.38 eV by the general order kinetics (GOK) model. In the case of LiF:Mg,Cu,P TLD, the activation energy was analyzed as 2.39 eV by result of the GAP model. In the case of MOK, the activation energy was analyzed as 2.55 eV. The activation energy was analyzed as 2.51 eV by the GOK model. The R value means different ratio of retrapping‐recombination. The R value of LiF:Mg,Cu,Si TLD main peak analyzed as 1.12 x 10‐6 and α value analyzed as 1.0 x 10‐3. The R of LiF:Mg,Cu,P TLD analyzed as 7.91 x 10‐4, the α value means different ratio of initial thermally trapped electron density‐initial trapped electron density (include thermally disconnected trap electrons density). The α value was analyzed as 9.17 x 10‐1 which was the difference from LiF:Mg,Cu,Si TLD. The deep trap electron density of LiF:Mg,Cu,Si was higher than the deep trap electron density of LiF:Mg,Cu,P.

특정 구획으로 유입된 오염물질이 해당 구획 내부에 순간적으로 균일하게 분포한다는 구획모델에 대한 기본가정의 한계로 인해, 전통적인 단일구획모델로는 불포화대에서 오염물질의 이동현상을 적절하게 예측할 수 없다. 한편 물리적으로 동일한 불포화대를 여러 개의 구획으로 구분한 다중구획모델링 기법은 실제 불포화대에서의 오염물질 이동 지연효과를 적절하게 설명할 수 있으나, 지금까지 일반적인 해석해가 보고된 바 없으며 고려하는 구획의 개수가 증가할수록 모델링에 많은 시간이 소요되는 등의 한계가 있다. 이러한 문제점을 해결하기 위하여, 이류가 지배적인 조건 하에서 불포화대의 오염물질 이동현상에 대한 다중구획모델을 수립하고 이를 해석적인 방법으로 계산할 수 있는 일반해를 유도한 후, 다중구획모델을 단일구획모델로 근사할 수 있는 수학적 제약조건을 도출하였다. 단순화된 근사방법론의 유효성은 가상적인 조건 하에서 간단한 수치해석적 방법을 통해 검증하였다. 물리적으로 동일한 특성을 갖는 불포화대를 단일 구획으로 가정할 경우, 불포화대로부터 포화대로 유입되는 오염물질의 전이율은 상수가 아닌 시간 종속적인 명목전이율로 표현할 수 있음을 증명하였다. 또한 명목전이율은 불포화대 구획간 전이율에 민감하며 오염층으로부터의 전이율에 대한 민감도는 미미한 것으로 나타났다. 이 연구에서 개발된 단순화된 근사방법론은 많은 시간이 요구되는 다중구획 모델링을 통하지 않고 불포화대 오염물질 이동현상을 신속하고 합리적으로 예측하기 위한 목적으로 활용될 수 있을 것으로 기대된다.

A conventional single compartment model cannot simulate reasonably the migration phenomenon of contaminants through unsaturated zone, due to the intrinsic unrealistic assumption of the compartment model that contaminants entering a compartment are immediately and uniformly mixed. Although, a multi-compartments model, in which even physically identical layer is divided into multiple compartments, may be used for explaining the retardation of contaminant mass flux along with increasing number of compartments, its numerical modeling is usually time-consuming and appropriate analytical solutions have not been reported yet. In order to improve the conventional compartment models on contaminant migration through unsaturated zone, a series of analytical solutions for multi-compartments model were derived and a generalized constraint under which the results from multi-compartments model can be simply approximated by single compartment model was proposed. The simplified approximation method was verified by a simple numerical analysis on the constraint under hypothetical conditions. It was also proved that the influent contaminant transfer rate from the bulk unsaturated zone can be generally represented into a time-dependent nominal transfer rate rather than a constant. In addition, the nominal transfer rate turned out to be very sensitive to the contaminant transfer rate between compartments in unsaturated zone, but to be almost insensitive to the transfer rate from contaminated zone. It is expected that the simplified approximation method developed in this study can be used for rapid and reasonable estimation of the migration phenomenon of contaminant through unsaturated zone, instead of time-consuming multi-compartments modeling.

In this paper a new non-ideal model of 8-bit Current Mode Successive Approximation Analog-Digital Converter (CM-SAR-ADC) has been proposed, the main blocks of CM SAR ADC are a current sample and hold, a current comparator, SAR logic register and current steering digital to analogue converter (DAC). The model is implemented in Matlab Simulink environment with non ideals factors such as switching noise, clock feed through, charge injection, flicker noise, clock jitter, settling error, and the effect of MOS transistor mismatch in the current sample and hold(S/H) and in the current steering DAC models, the delay time and the current offset are introduced as non-ideal sources to the current comparator model. The comparisons of the simulation results using 8-bits CM SAR ADC with ideal and non-ideal models confirmed the effects of the non ideality sources on the parameters of the ADC.

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

Compared with model approximation for general SISO transfer function models with or without a single constant delay, the approximation problem for SISO transfer function models with multiple delays has received much less attention. In this paper, we attack this problem and thus present a multiple-point step response fitting based approximation method to derive the reduced models. A simple frequency-domain weighted recursive least squares (RLS) algorithm is proposed to determine a set of parameters so that the reduced models can approximate the original models by minimizing the defined frequency-domain squared-error between the step responses of the original and the reduced model. Numerical examples have demonstrated that the proposed approximation approach can not only introduce less dynamic approximation error, but also yield zero steady-state error.

In the proportional hazard model with the beta process prior, the posterior computation with the discrete approximation is considered. The time period of interest is partitioned by small intervals. On each partitioning interval, the likelihood is approximated by that of a binomial experiment and the beta process prior is by a beta distribution. Consequently, the posterior is approximated by that of many independent binomial model with beta priors. The analysis of the leukemia remission data is given as an example. It is illustrated that the length of the partitioning interval affects the posterior and one needs to be careful in choosing it.

In this paper we propose a parametric and a nonparametric small sample estimators for the mean residual life (MRL) under the random censorship model using the partial moment approximation. We also compare the proposed nonparametric estimator with the well-known nonparametric MRL estimator based on Kaplan-Meier estimator of the survival function, and present the efficiency of the nonparametric method relative to the Weibull model for small samples.

This paper presents a control design for networked control systems (NCS) with uncertain time delay using model matching. The dynamics of the time delay are approximated through the Pade linearization and the uncertain delay term is recursively estimated by the recursive least square (LS) algorithm. Computer simulation illustrates that the proposed control compares favorably with a recently published control approach.

In this paper we consider several nonparametric estimators for the mean residual life by using the partial moment approximation under the proportional hazard model. Also we compare the magnitude of mean square error of the proposed nonparametric estimators for mean residual life under the proportional hazard model.

This paper applies approximation of the renewal function for Hjorth model and Dhillon model which show the trend change in its aging properties. We obtain the renewal function for Hjorth model and Dhillon model by a numerical solution of an approximate integral. We observe the influence of each parameter in these models. The results of the computation are described and their corresponding graphs are provided.

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.

This paper introduces a new approach for evaluating reliability of a complex system in terms of distributional parameters where analytical determination of reliability is intractable. The concept of discrete approximation, reported in the literature so far, fails to meet the latter requirement in terms of distributional parameters. The current work aims at offering a bound based approach where reliability planners not only get a clear idea about the extent of error but also can manipulate in terms of distributional parameters. This reliability approximation has been under taken under the Weibull frame work which is the most widely used model for reliability analysis. Numerical study has been carried out to examine the strength of our proposed reliability approximation via closeness between the two reliability bounds. This approach will be very useful during the early stages of product design as the distributional parameters can be adjusted.

Stochastic approximation Monte Carlo algorithm has recently been proposed by Liang, Liu and Carroll(2007) as a general simulation and optimization algorithm. In this paper, we discuss the application of this algorithm to Bayesian model selection problems and propose the new sampling method for the case of the multiple change-points identification among those problems. We estimate multiple change-points in the Bayesian multiple change-points model when the data are from normal distributions. The algorithm is tested on simulated and the real dataset, and compared with evolutionary Monte Carlo algorithm. The numerical results indicate that the new sampling method can generate the efficient samplers and thus Stochastic approximation Monte Carlo outperforms evolutionary Monte Carlo significantly for the model selection problems.

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

In this paper, we consider a class of inhomogeneous random intersection graphs by assigning random weight to each vertex and two vertices are adjacent if they choose some common elements. In the inhomogeneous random intersection graph model, vertices with larger weights are more likely to acquire many elements. We show the Poisson convergence of the number of induced copies of a fixed subgraph as the number of vertices n and the number of elements m, scaling as $m={\lfloor}{\beta}n^{\alpha}{\rfloor}$ (${\alpha},{\beta}>0$), tend to infinity.

Based on an introduced optimal trajectory planning method, this paper mainly deals with the accuracy analysis during the function approximation process of the optimal trajectory planning method. The basis functions are composed of Hermit polynomials and Fourier series to improve the approximation accuracy. Since the approximation accuracy is affected by the given orders of each basis function, the accuracy of the optimal solution is examined by changing the combinations of the orders of Hermit polynomials and Fourier series as the approximation basis functions. As a result, it is found that the proper approximation basis functions are the $5^{th}$ order Hermit polynomials and the $7^{th}-10^{th}$ order of Fourier series.