• Journal of Information Processing Systems
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• 제15권5호
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Earthquakes and Structures
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• 제7권2호
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• pp.119-139
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• 2014

A new method is proposed for random vibration anaylsis of hysteretic systems subjected to non-stationary random excitations. With the Bouc-Wen model, motion equations of hysteretic systems are first transformed into quasi-linear equations by applying the concept of equivalent excitations and decoupling of the real and hysteretic displacements, and the derived equation system can be solved by either the precise time integration or the Newmark-${\beta}$ integration method. Combining the numerical solution of the auxiliary differential equation for hysteretic displacements, an explicit iteration algorithm is then developed for the dynamic response analysis of hysteretic systems. Because the computational cost for a large number of deterministic analyses of hysteretic systems can be significantly reduced, Monte-Carlo simulation using the explicit iteration algorithm is now viable, and statistical characteristics of the non-stationary random responses of a hysteretic system can be obtained. Numerical examples are presented to show the accuracy and efficiency of the present approach.

• 노동경제논집
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• 제34권3호
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• pp.59-80
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• 2011

본 연구는 비안정적 시계열 자료 분석 방법을 이용하여 장단기 고용탄력성을 추정하고 그 변화를 고찰하였다. 그 결과, 지난 25년간 고용탄력성이 유의하게 감소했다는 증거를 발견할 수 없었다. 이는 외환위기 이후의 취업자 수 증가율 감소가 고용탄력성 감소보다는 성장률 감소에 크게 기인함을 의미한다. 그러므로 일자리 창출력을 높이려는 정책적 노력은 성장잠재력 강화에 경주되어야 한다. 통계적 유의성을 떠나 고용탄력성 감소경향을 사실로 받아들인다고 하더라고 그것은 장기적으로 영향력을 발휘하는 구조적 요인에 기인하는 것이다. 그러므로 주어진 성장률 아래에서 일자리 창출력을 높이기 위한 노력도 장기적인 체질 강화에 역점을 두어야 한다.

• 방송공학회논문지
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• 제17권6호
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• pp.1106-1109
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• 2012

팬/틸트 카메라와 같은 비정적인 카메라를 사용한 지능적 영상 감시 시스템들의 경우 정적인 카메라를 사용한 시스템에서보다 객체 추적의 안정성이 많이 떨어진다. 비정적인 환경에서 배경에 대한 모델링을 수행할 수 없기 때문이다. 본 레터에서는 팬/틸트 카메라를 사용하지만, 안정적인 객체추적을 위해 차영상을 얻을 수 있도록 동적 윤곽선모델을 stop/go방식과 연동하여 사용하는 방식의 팬/틸트 시스템을 제안한다. 제안한 시스템은 추적대상의 움직임으로 차영상을 몇 프레임밖에 얻지 못하는 상황에서도 추적대상에 대한 영역정보를 추출할 수 있고, 안정적인 추적이 가능하다.

• 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.

• 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.

• Structural Engineering and Mechanics
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• 제77권3호
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• pp.355-368
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• 2021

The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.

• Wind and Structures
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• 제36권1호
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• pp.61-73
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• 2023

In this article, a novel structural modal parameters identification methodology is developed to determine the natural frequencies and damping ratios of civil structures based on the symplectic geometry mode decomposition (SGMD) approach. The SGMD approach is a new decomposition algorithm that can decompose the complex response signals with better decomposition performance and robustness. The novel method firstly decomposes the measured structural vibration response signals into individual mode components using the SGMD approach. The natural excitation technique (NExT) method is then used to obtain the free vibration response of each individual mode component. Finally, modal natural frequencies and damping ratios are identified using the direct interpolating (DI) method and a curve fitting function. The effectiveness of the proposed method is demonstrated based on numerical simulation and field measurement. The structural modal parameters are identified utilizing the simulated non-stationary responses of a frame structure and the field measured non-stationary responses of a supertall building during a typhoon. The results demonstrate that the developed method can identify the natural frequencies and damping ratios of civil structures efficiently and accurately.

• 한국수자원학회논문집
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• 제47권9호
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• pp.825-838
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• 2014

정상성 마코프 연쇄 모형은 일강우모의 모형으로 광범위하게 이용되고 있다. 하지만 정상성 마코프 연쇄 모형의 기본가정은 통계학적 특성이 시간에 따라 변화하지 않는 것으로, 일강우모의 시에 평균 또는 분산의 경향적 변화를 효과적으로 반영할 수 없다. 이러한 문제점을 인지하여 본 연구에서는 연주기 및 계절변화에 대하여 우수한 모의 능력을 나타내는 GCM의 모의결과를 입력자료로 이용하여 일강우량을 모의하기 위한 통계학적 상세화(downscaling) 기법인 비정상성 은닉 마코프 모형을 개발하였다. 개발된 모형을 낙동강 유역에 존재하는 영주지점, 문경지점 및 구미지점의 관측강우량에 적용한 결과, 일단위 및 계절단위의 강우량의 통계적 특성을 기존 모형에 비하여 개선된 결과를 도출할 수 있었으며, 또한 개발된 모형은 극치강수량 복원에 있어서도 관측값과 보다 유사한 결과를 보여 주었다. 이러한 점에서 정확성이 확보된 GCM 계절예측자료가 입력자료로 NHMM 모형에 활용된다면 예측기반의 일강수 상세화 모형으로 활용될 수 있을 것으로 판단된다. 이와 더불어, 기후변화 시나리오 입력자료가 사용된다면 기후변화 상세화 모형으로서도 적용될 수 있을 것으로 사료된다.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권4호
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• pp.317-323
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• 2005

To increase the operating speed of the solenoid actuator, this paper proposed a modified model using a non-magnetic ring, which is welded on the magnetic guide tube, and also presents the characteristic equations, results of Finite Element Method (FEM) analysis for magnetic flux distribution and density in magnetic flux paths, and computer simulation results for the dynamic characteristics of plunger motion according to the stroke and time variation. As well, we proved the non-magnetic ring effect by experiments using prototypes.