• Structural Engineering and Mechanics
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• v.85 no.1
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• pp.119-133
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• 2023

Impact event is the key factor influencing the operational state of the mechanical equipment. Additionally, nonlinear factors existing in the complex mechanical equipment which are currently attracting more and more attention. Therefore, this paper proposes a novel hybrid-separate identification strategy to solve the force identification problem of the nonlinear structure under impact excitation. The 'hybrid' means that the identification strategy contains both l1-norm (sparse) and l2-norm regularization methods. The 'separate' means that the nonlinear response part only generated by nonlinear force needs to be separated from measured response. First, the state-of-the-art two-step iterative shrinkage/thresholding (TwIST) algorithm and sparse representation with the cubic B-spline function are developed to solve established normalized sparse regularization model to identify the accurate impact force and accurate peak value of the nonlinear force. Then, the identified impact force is substituted into the nonlinear response separation equation to obtain the nonlinear response part. Finally, a reduced transfer equation is established and solved by the classical Tikhonove regularization method to obtain the wave profile (variation trend) of the nonlinear force. Numerical and experimental identification results demonstrate that the novel hybrid-separate strategy can accurately and efficiently obtain the nonlinear force and impact force for the nonlinear structure.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.952-954
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• 2003

In this paper, we present the different characteristics of NDVI fluctuation pattern between irrigated and non-irrigated area in Suphanburi province, in Central Thailand. For non-irrigated rice cultivation area, there is a strong correlation between NDVI fluctuation and peak rainfall, while there is a lower correlation with irrigated area. In this study, the 'peak detector' classifier was developed to identify the area of non-irrigated and irrigated cropping and its cropping intensity (number of crops per year). This classifier was created based on cropping characteristics such as number of crops, time or planting period of each crop and its relationship with the peak of rainfall. The classified result showed good accuracy in identification irrigated and nonirrigated rice cultivation areas.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.485-485
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• 2000

A design method of model following control system using neural networks is proposed. An unknown nonlinear single-input single-output plant is identified using a multilayer neural networks. A linear controller is designed fer the linear approximation model obtained by linearinzing the identification model. The identification model is also used as a plant emulator to obtain the prediction error. Deficient servo performance due to controlling nonlinear plant with only linear controller is mended by adjusting the linear controller output using the prediction output and the parameters of the identification model. An optimal preview controller is adopted as the linear controller by reason of having good servo performance lowering the peak of control input. Validity of proposed method is illustrated through a numerical simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.9
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• pp.710-723
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• 1989

This paper describes the efforts to develope a PC based parameter identification system for D.C servomotors. A new identification algorithm for the D.C. servomotor parameters is developed. The algorithm is implemented on 16 bit IBM-PC/XT using the C language. The whole identification process of signal generation, measuring and parameter determination is fully automated. To minimize the errors due to the ripple component in the measured armature currents, digital averaging filter is employed. The proposed parameter correction method using the deadzone current and the time to reach the peak current resulted in excellent agreement between the measured current and the current estimated using the model.

• Journal of the Korean Ceramic Society
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• v.49 no.6
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• pp.493-497
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• 2012

We propose a new reliable, fast, and low cost identification method for similarly looking 0.3ct vivid yellow color of natural, HPHT treated, and synthesized diamonds. Conventional optical microscopy as well as low temperature PL(photoluminescence), FT-IR, UV-VIS-NIR, micro-Raman spectroscopy, and vibrating sample magnetometry(VSM) characterization were executed. We could not distinguish the natural diamonds from the treated or the synthesized stones with an optical microscopy, PL, FT-IR, and UV-VIS-NIR spectroscopy. However, we could identify the treated diamond with micro-Raman spectroscopy due to unique $1440cm^{-1}$ peak appearance. VSM revealed easily the synthesized diamond because of its ferromagnetic behavior. Our preliminary propose on employing the Micro-Raman spectroscopy and VSM might be suitable for identification of the similar looking vivid yellow colored diamonds.

• Journal of Power System Engineering
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• v.2 no.1
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• pp.52-58
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• 1998

An immune system has powerful abilities such as memory, recognition and learning to respond to invading antigens, and is applied to many engineering algorithm recently. In this paper, the combined optimization algorithm is proposed for multi-objective problem by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed algorithm is identified by using multi-peak function which have many local optimums and identification of the flexural rigidity for wire rope model.

• Earthquakes and Structures
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• v.2 no.4
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• pp.323-336
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• 2011

The problem of reconstruction of complete building response from a limited number of response measurements is considered. The response at the intermediate degrees of freedom is reconstructed by using piecewise cubic Hermite polynomial interpolation in time domain. The piecewise cubic Hermite polynomial interpolation is preferred over the spline interpolation due to its trend preserving character. It has been shown that factorization of response data in variable separable form via singular value decomposition can be used to derive the complete set of normal modes of the structural system. The time domain principal components can be used to derive empirical transfer functions from which the natural frequencies of the structural system can be identified by peak-picking technique. A reduced-rank approximation for the system flexibility matrix can be readily constructed from the identified mass-orthonormal mode shapes and natural frequencies.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• The Journal of the Petrological Society of Korea
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• v.9 no.4
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• pp.205-210
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• 2000

Measurements of phase identification and degree of orientation for fine-grained (about 0.3 mm in diameter) minerals in rock samples performed by micro-area X-ray diffractometer.$Al_{2}SiO_{5}$ polymorphs (andalusite, kyanite and sillimanite) were chosen for the measurements and target minerals were existed on thin sections. Micro-area X-ray diffractometer is composed of 3(${\omega}\;{\chi}\;{\phi}$)-circle oscillating goniometer and position sensitive proportional counter (PSPC). $CuK_{\alpha}$ radiation was used as X-ray source and a pin hole ($50\;\mu\textrm{m}$$ in diameter) collimator was selected to focus radiation X-ray onto the target minerals. Phase identification and diffracted X-ray peak indexing were carried out by 3(${\omega}\;{\chi}\;{\phi}$)-circle oscillation measurement. Then, 2(${\omega}\;{\phi}$)-circle oscillation measurement was made for the purpose of searching the prevailing lattice plane of the minerals on thin section surface. Finally, for a selected peak by 2-circle oscillation measurement, X-ray pole figure measurement was executed for the purpose of check the degree of orientation of the single lattice direction and examine its pole distribution. As a result of 3-circle oscillation measurement, it was possible that phase identification among $Al_{2}SiO_{5}$ polymorphs. And from the results of 2-circle oscillation measurement and X-ray pole figure measurement, we recognized that poles of andalusite (122), kyanite (200) and sillimanite (310) lattice plances were well developed with direction normal to each mineral surface plane respectively. Therfore, the measurements used with micro-area X-ray diffractometer in this study will be a useful tool of phase identification and degree of orientation measurement for fine-grained rock forming minerals.