Earticle

Human activity recognition is a main research area of context-aware computing, and is widely used in many applications, such as smart home and elderly care. Smart phone-based human activity recognition is very popular by making use of the embedded inertial sensors. However, there exists the problems of misclassification activities, and how to effectively apply the model trained by known users to new users. To solve these two problems, in this paper, we proposed a novel approach, Uncertainty Sampling based posterior Probability Extreme Learning Machine (USP-ELM), by introducing two strategies: first, we transfer the actual outputs of ELM to posterior probabilities for each instances, and then use uncertainty sampling strategy for confidence level assignment to adapt the training model and improve the classification accuracy. Experimental results show that the proposed approach is more efficient, compared with the existing ELMs.

Sampling based uncertainty analysis was carried out to quantify uncertainty in predictions of best estimate code RELAP5/MOD3.2 for a thermal hydraulic test (10% hot leg break LOCA) performed in the Large Scale Test Facility (LSTF) as a part of an IAEA coordinated research project. The nodalisation of the test facility was qualified for both steady state and transient level by systematically applying the procedures led by uncertainty methodology based on accuracy extrapolation (UMAE); uncertainty analysis was carried out using the Latin hypercube sampling (LHS) method to evaluate uncertainty for ten input parameters. Sixteen output parameters were selected for uncertainty evaluation and uncertainty band between $5^{th}$ and $95^{th}$ percentile of the output parameters were evaluated. It was observed that the uncertainty band for the primary pressure during two phase blowdown is larger than that of the remaining period. Similarly, a larger uncertainty band is observed relating to accumulator injection flow during reflood phase. Importance analysis was also carried out and standard rank regression coefficients were computed to quantify the effect of each individual input parameter on output parameters. It was observed that the break discharge coefficient is the most important uncertain parameter relating to the prediction of all the primary side parameters and that the steam generator (SG) relief pressure setting is the most important parameter in predicting the SG secondary pressure.

This paper proposes an efficient reliability-based optimization method for designing a superconducting magnetic energy system in presence of uncertainty. To evaluate the probability of failure of constraints, samplingbased reliability analysis method is employed, where Monte Carlo simulation is incorporated into dynamic Kriging models. Its main feature is to drastically reduce the numbers of iterative designs and computer simulations during the optimization process without sacrificing the accuracy of reliability analysis. Through comparison with existing methods, the validity of the proposed method is examined with the TEAM Workshop Problem 22.

Free radicals including reactive oxygen species (ROS) are important chemicals in the research area of biology, pharmaceutical, medical, and environmental science as well as human health risk assessment as they are highly involved in diverse metabolism and toxicity mechanisms through chemical reactions with various components of living bodies. Electron spin resonance (ESR) spectroscopy is a powerful tool for detecting and quantifying those radicals in biological environments. In this work we observed the ESR signal of 2,2,6,6-Tetra-methyl piperidine 1-oxyl (TEMPO) in aqueous solution at various concentrations to estimate the uncertainty factors arising from the experimental conditions and signal treatment methods. As the sample position highly influences the signal intensity, dual ESR tube geometry (consists of a detachable sample tube and a position fixed external tube) was adopted. This type of measurement geometry allowed to get the relative uncertainty of signal intensity lower than 1% when triple measurements are averaged. Linear dependence of signal intensity on the TEMPO concentration, which is required for the quantification of unknown sample, could be obtained over a concentration range of ~10³ by optimizing the signal treatment method depending on the concentration range.

In this study, a cross section stochastic sampling (S.S.) capability is implemented into both the McCARD continuous energy Monte Carlo code and MIG multiple-correlated data sampling code. The ENDF/B-VII.1 covariance data based 30 group cross section sets and the SCALE6 covariance data based 44 group cross section sets are sampled by the MIG code. Through various uncertainty quantification (UQ) benchmark calculations, the McCARD/MIG results are verified to be consistent with the McCARD stand-alone sensitivity/uncertainty (S/U) results and the XSUSA S.S. results. UQ analyses for Three Mile Island Unit 1, Peach Bottom Unit 2, and Kozloduy-6 fuel pin problems are conducted to provide the uncertainties of k_eff and microscopic and macroscopic cross sections by the McCARD/MIG code system. Moreover, the SNU S/U formulations for uncertainty propagation in a MC depletion analysis are validated through a comparison with the McCARD/MIG S.S. results for the UAM Exercise I-1b burnup benchmark. It is therefore concluded that the SNU formulation based on the S/U method has the capability to accurately estimate the uncertainty propagation in a MC depletion analysis.

To study the influence of parameter uncertainty in small pressurized water reactor (SPWR) once-through steam generator (OTSG), the nonlinear mathematical model of the SPWR is firstly established. Including the reactor core model, the OTSG model and the pressurizer model. Secondly, a control strategy that both the reactor core coolant average temperature and the secondary-side outlet pressure of the OTSG are constant is adopted. Then, the uncertainty quantification method is established based on Latin hypercube sampling and statistical method. On this basis, the quantitative platform for parameter uncertainty of the OTSG is developed. Finally, taking the uncertainty in primary-side flowrate of the OTSG as an example, the platform application work is carried out under the variable load in SPWR and step disturbance of secondary-side flowrate of the OTSG. The results show that the maximum uncertainty in the critical output parameters is acceptable for SPWR.

다양한 불확실성을 체계적으로 반영하는 신뢰성 기반 해석기법을 사면안정 해석의 한 형식으로 제시한다. 통계오차, 공간 변동성, 그리고 공간 평균의 효과를 고려할 수 있는 지반특성 표현식이 사용되었다. 여러 가지 형식의 지반특성 표현식을 이용하여 사면안정 신뢰성 해석을 수행한 결과 통계오차, 공간적 상관성, 그리고 조건부 해석기법을 사용할 경우가 기존의 단순 확률변수 기법에 비해 상당히 작은 파괴확률을 제시한다는 사실이 밝혀졌다. 이 결과는 사면안정 해석에서 공간적 변동성과 통계오차가 합리적으로 고려되어야 한다는 점을 제시한다.

A reliability-based approach that can systematically model various sources of uncertainty is presented in the context of slope stability. Expressions for characterization of soil properties are developed in order to incorporate sampling errors, spatial variability and its effect of spatial averaging. Reliability analyses of slope stability with different statistical representations of soil properties show that the incorporation of sampling error, spatial correlation, and conditional simulation leads to significantly lower probability of failure than that obtained by using simple random variable approach. The results strongly suggest that the spatial variability and sampling error have to be properly incorporated in slope stability analysis.