• Structural Engineering and Mechanics
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• 제41권5호
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• pp.675-689
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• 2012

Most connections of steel structures exhibit flexible behaviour under cyclic loading. The flexible connections can be assumed as nonlinear rotational springs attached to the ends of each beam. The nonlinear behaviour of the connections can be considered by suitable moment-rotation relationship. Time-history analysis by direct integration method can be used as a powerful technique to determine the nonlinear dynamic response of the structure. In conventional numerical integration, the response is evaluated for a series of short time increments. The limitations on the size of time intervals can be removed by using Chen and Robinson improved time history analysis method, in which the integrated displacements are used as the new variables in integrated equation of motion. The proposed method permits longer time intervals and reduces the computational works. In this paper the nonlinearity behaviour of the structure is summarized on the connections, and the step by step improved time-history analysis is used to calculate the dynamic response of the structure. Several numerical calculations which indicate the applicability and advantages of the proposed methodology are presented. These calculations illustrate the importance of the effect of the nonlinear behaviour of the flexible connections in the calculation of the dynamic response of steel frames.

• 전자공학회논문지SC
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• 제49권2호
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• pp.17-25
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• 2012

본 논문에서는 입력에 시간지연이 있는 연속 비선형 시스템의 일반적인 이산화를 위해 높은 차수의 샘플링 보관법을 제안한다. 제안한 방법은 테일러 시리즈 확장, 샘플링 이론과 보관법의 조합을 기초로 한다. 새로운 이산화 방법의 수학적인 구조에 대해 세부적으로 유도하였으며, 제안한 이산화 방법에 대한 성능을 2차 시스템에 대한 시뮬레이션을 통해 검증하였다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 춘계학술대회
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• pp.18-21
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• 1996

Even though there are several deterministic methods for the modeling of linear systems, there is no standard method for the modeling of nonlinear systems. For the modeling of nonlinear systems we have applied the genetic programming method to estimate nonlinear time sereis. We get the time series from the simple known nonlinear dynamics, and fed those to genetic programming. For the tested nonlinear systems, suggested method estimated the nonlinear dynamics correctly.

• 전자공학회논문지SC
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• 제46권2호
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• pp.52-60
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• 2009

본 논문에서는 입력에 시간 지연이 존재하는 비선형 다중입출력(multi-input/multi-output (MIMO)) 시스템을 위한 디지털 제어기를 제안하였다. 다중 입력 시간 지연 비선형 시스템을 테일러의 추산 방법으로 이산화하고, 이 이산형 시스템을 일반적인 비선형 시스템으로 변환할 수 있다. 결과적으로, 일반 비선형 제어기를 이산형 시간 지연 시스템에 적용할 수 있다. 본 논문에서는 MAC 제어기를 설계하고 컴퓨터 시뮬레이션을 실시하여 제안된 방법의 성능을 검증하였다. 시뮬레이션의 결과 제안된 제어기는 비선형 다중입력 다중 시스템에 있어서 입력에 시간지연이 발생하더라도 시스템의 성능을 유지함을 확인할 수 있었다. 따라서 제안된 방법은 비선형 시간 지연 시스템의 제어에 유용함을 알 수 있다.

• 항공우주시스템공학회지
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• 제13권5호
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• pp.9-15
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• 2019

In this paper, dynamic analysis of a nonlinear active vibration absorber is conducted with a time delay acceleration feedback to suppress the vibration of a nonlinear single degree of freedom primary system. The primary system consisting of linear and nonlinear cubic springs, mass, and damper is subjected to the multi-harmonic hard excitation with a parametric excitation. It is proposed to reduce the vibration of the primary system and the absorber by using a lead zirconate titanate (PZT) stack actuator in series with a spring in the absorber which configures as an active vibration absorber. The method of multiple scales (MMS) is used to obtain the approximate solution of the system under the internal resonance, subharmonic, superharmonic, and principal parametric resonance conditions simultaneously. Frequency and time responses of the system are investigated considering a delay in the feedback for the various parameters of the absorber configuration and controlling force.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.

• 대한의용생체공학회:의공학회지
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• 제15권3호
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• pp.347-354
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• 1994

This paper describes a diagnostic protocol of nonlinear dynamic characteristics of biological system using chaos theory. An integrated chaos analysis system for the diagnosis of biological system was designed. We suggest a procedure of attractor reconstruction for reliable qualitative and quantitative analysis. The effect of autonomic nervous system activity on heart rate variability with power spectral analysis and its characteristics of chaotic attractors are investigated. The results show the applicability to evaluate the mental and physical conditions using nonlinear characteristics of biological signal.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2007년도 춘계 학술발표회 발표논문집
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• pp.199-202
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• 2007

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2007년도 춘계 학술발표회 발표논문집
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• pp.218-222
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• 2007

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1450-1454
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• 2006

Empirical mode decomposition (EMD) is applied to analyze time series characterized with nonlinearity and nonstationarity. This decomposition could be utilized to construct finite and small number intrinsic mode functions (IMF) that describe complicated time series, while admitting the Hilbert transformation properties. EMD has the capability of being adaptive, capture local characteristics, and applicable to nonlinear and nonstationary processes. Unlike discrete wavelet transform (DWT), IMF eliminates spurious harmonics and retains meaningful instantaneous frequencies. Examples based on data representing natural phenomena are given to demonstrate highlight the power of this method in contrast and comparison of other ones. A presentation of the energy-frequency-time distribution of these signals found to be more informative and intuitive when based on Hilbert transformation.