• Journal of Welding and Joining
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• 제8권2호
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• pp.53-57
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• 1990

Analytic solutions of heat conduction during welding which were first found by Resenthal have some restrictions. One of these is that models to which analytic solutions can be applied must have simple geometric shape, and another is that quasi-stationary state must be created. On the other hand, computational methods developed recently with the aid of the computer can overcome these shortcomings, but the methods raise problems from economic point of view when they are applied to 3 dimensional problems. Taking account of these problems, a new method combinig the analytic method with the computational one is proposed. This method can be ued in weldments with complicated geometric shape in non-stationary state, but with the aid of the analytic method can reduce the computing time.

• Wind and Structures
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• 제32권2호
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• pp.89-104
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• 2021

Non-synoptic winds generated by tornadoes, downbursts or gust fronts exhibit significant non-stationarity and can cause significant wind load effect on flexible structures such as long-span bridges. However, conventional assumptions on stationarity used to evaluate the structural wind-induced vibration are inadequate. In this paper, an efficient frequency domain scheme based on fast CQC method, which can predict non-stationary buffeting random responses of long-span bridges, is presented, and then this approach is applied to evaluate the buffeting response of a long-span suspension bridge located in a complex mountainous wind environment as an example. In this study, the data-driven method based on one available measured wind speed sample is firstly presented to establish non-stationary wind models, including time-varying mean wind speed, time-varying intensity envelope function and uniformly modulated fluctuating spectrum. Then, a linear time-variant (LTV) system based on the proposed scheme can be generally applied to calculate the non-stationary buffeting responses. The effectiveness and accuracy of the proposed scheme are verified through Monte Carlo time domain simulation implemented in ANSYS platform. Also, the transient effect nature of the bridge responses is further illustrated by comparison of the non-stationary, quasistationary and steady-state cases. Finally, buffeting response analysis with traditional stationary treatment (10 min constant mean plus stationary wind fluctuation) is performed to illustrate the importance of the non-stationary characteristics embedded in original wind speed samples.

• Journal of Communications and Networks
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• 제9권1호
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• pp.18-27
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• 2007

The conventional normalized least mean square (NLMS) algorithm is the most widely used for adaptive identification within a non-stationary input context. The convergence of the NLMS algorithm is independent of environmental changes. However, its steady state performance is impaired during input sequences with low dynamics. In this paper, we propose a new NLMS algorithm which is, in the steady state, insensitive to the time variations of the input dynamics. The square soot (SR)-NLMS algorithm is based on a normalization of the LMS adaptive filter input by the Euclidean norm of the tap-input. The tap-input power of the SR-NLMS adaptive filter is then equal to one even during sequences with low dynamics. Therefore, the amplification of the observation noise power by the tap-input power is cancelled in the misadjustment time evolution. The harmful effect of the low dynamics input sequences, on the steady state performance of the LMS adaptive filter are then reduced. In addition, the square root normalized input is more stationary than the base input. Therefore, the robustness of LMS adaptive filter with respect to the input non stationarity is enhanced. A performance analysis of the first- and the second-order statistic behavior of the proposed SR-NLMS adaptive filter is carried out. In particular, an analytical expression of the step size ensuring stability and mean convergence is derived. In addition, the results of an experimental study demonstrating the good performance of the SR-NLMS algorithm are given. A comparison of these results with those obtained from a standard NLMS algorithm, is performed. It is shown that, within a non-stationary input context, the SR-NLMS algorithm exhibits better performance than the NLMS algorithm.

• Smart Structures and Systems
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• 제14권1호
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• pp.17-37
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• 2014

In this study, four widely accepted and used variants of Evolution Strategies (ES) are adapted and applied to the output-error state-space identification problem. The selection of ES is justified by prior strong indication of superior performance to similar problems, over alternatives like Genetic Algorithms (GA) or Evolutionary Programming (EP). The ES variants that are being tested are (i) the (1+1)-ES, (ii) the $({\mu}/{\rho}+{\lambda})-{\sigma}$-SA-ES, (iii) the $({\mu}_I,{\lambda})-{\sigma}$-SA-ES, and (iv) the (${\mu}_w,{\lambda}$)-CMA-ES. The study is based on a six-degree-of-freedom (DOF) structural model of a shear building that is characterized by light damping (up to 5%). The envisaged analysis is taking place through Monte Carlo experiments under two different excitation types (stationary / non-stationary) and the applied ES are assessed in terms of (i) accurate modal parameters extraction, (ii) statistical consistency, (iii) performance under noise-corrupted data, and (iv) performance under non-stationary data. The results of this suggest that ES are indeed competitive alternatives in the non-linear state-space estimation problem and deserve further attention.

• 한국멀티미디어학회논문지
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• 제6권3호
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• pp.508-517
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• 2003

본 논문에서는 쌍직교 웨이브릿 영역에서 워터마크를 삽입할 수 있는 연속 부대역 양자화 및 스토케스틱 다해상도 특성을 갖는 지각 모델을 제안한다. 적응 워터마킹 알고리즘을 갖는 지각모델은 보다 강인한 워터마크 은닉을 위한 방법으로 연속 부대역 양자화(successive subband quantization: SSQ)에 의해서 텍스쳐 및 에지 영역에 삽입한다. 워터마크 삽입은 국부 영상 특성을 갖는 NVF(noise visibility function)함수에 의해 계산된다. 이 방법은 워터마크가 노이즈 특성을 갖기 때문에 영상의 통계적 특성에 기초한 비정상상태(non-stationary state) 가우스 모델과 정상상태(stationary state) 일반화 가우스(generalized Gaussian: GG)모델을 이용한다. 정상상태 GG모델의 삽입은 다해상도 내의 각 부대역별 분산과 형상계수(shape parameter)를 사용한다. 형상계수를 추정하기 위하여 모멘트 정합 방법을 사용한다. 비정상상태 가우스 모델은 각 부대역의 국부 평균 및 분산을 이용한다. 실험결과 우수한 비가시성과 강인성을 확인하였으며, 공격에 대한 실험으로 Stirmark 3.1 benchmark test를 수행하였다.

• Wind and Structures
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• 제9권5호
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• pp.369-386
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• 2006

A combination Aerodynamic/Atmospheric Boundary Layer (AABL) Wind and Gust Tunnel with a unique active gust generation capability has been developed for wind engineering and industrial aerodynamics applications. This facility is a cornerstone component of the Wind Simulation and Testing (WiST) Laboratory of the Department of Aerospace Engineering at Iowa State University (ISU). The AABL Wind and Gust tunnel is primarily a closed-circuit tunnel that can be also operated in open-return mode. It is designed to accommodate two test sections ($2.44m{\times}1.83m$ and $2.44m{\times}2.21m$) with a maximum wind speed capability of 53 m/s. The gust generator is capable of producing non-stationary gust magnitudes around 27% of the mean flow speed. This paper describes the motivation for developing this gust generator and the work related to its design and testing.

• 경영과학
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• 제10권2호
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• pp.27-42
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• 1993

Markov chain models can be used to predict the state of the system in the future. We extend the existing Markov chain models in two ways. For the stationary model, we propose a procedure that obtains the transition probabilities by appling the empirical Bayes method, in which the parameters of the prior distribution in the Bayes estimator are obtained on the collaternal micro data. For non-stationary model, we suggest a procedure that obtains a time-varying transition probabilities as a function of the exogenous variables. To illustrate the effectiveness of our extended models, the models are applied to the macro and micro time-series data generated from actual survey. Our stationary model yields reliable parameter values of the prior distribution. And our non-stationary model can predict the variable transition probabilities effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• 한국세라믹학회지
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• 제49권1호
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• pp.56-65
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• 2012

Functional materials are often exposed to high temperatures and inherent temperature gradients. These temperature gradients act as thermodynamic driving forces for the diffusion of mobile components. The detailed consequences of thermodiffusion depend on the boundary conditions of the non-isothermal sample: Once the boundaries of the sample are inert and closed for exchange of the mobile components, thermodiffusion leads to their pile-up in the stationary state (the so called Soret effect). Once the system is open for an exchange of the mobile component, chemical diffusion adds to the Soret effect, and stationary non-zero component fluxes are additionally observed in the stationary state. In this review, the essential aspects of thermodiffusion and Soret effect in inorganic functional materials are briefly summarized and our current practical knowledge is reviewed. Major examples include nonstoichiometric binary compounds (oxides and other chalcogenides) and ternary solid solutions. The potential influence of the Soret effect on the long term stability of high temperature thermoelectrics is briefly discussed. Typical Soret coefficients for nonstoichiometric compounds are found to be of the order of (d${\delta}$/dT) ${\approx}$ 1%/K.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.