• Title/Summary/Keyword: linear operator.

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결함검출을 위한 실험적 연구

  • 목종수
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1996.03a
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    • pp.24-29
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    • 1996
  • The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

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Finite Element Analysis for Vibration of Laminated Plate Using a Consistent Discrete Theory Part I : Variational Principles (복합재료적층판의 진동해석을 위한 유한요소모델 I. 변분원리의 유도)

  • 홍순조
    • Computational Structural Engineering
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    • v.7 no.4
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    • pp.85-101
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    • 1994
  • A family of variational principles governing the dynamics of laminated plate has been derived using a variationally consistent shear deformable discrete laminated plate theory with particular reference to finite element procedures. The theoretical basis for the derivation is Sandhu's generalized procedure for the variational formulation of linear coupled boundary value problem. As the bilinear mapping to write the operator matrix of the field equations in self-adjoint form, convolution product was employed. Boundary conditions, initial conditions and probable internal discontinuity were explicitly included in the governing functionals. Some interesting extensions and specializations of the general variational principle were presented, which can provide many different finite element formulations for the problem.

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QoS- and Revenue Aware Adaptive Scheduling Algorithm

  • Joutsensalo, Jyrki;Hamalainen, Timo;Sayenko, Alexander;Paakkonen, Mikko
    • Journal of Communications and Networks
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    • v.6 no.1
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    • pp.68-77
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    • 2004
  • In the near future packet networks should support applications which can not predict their traffic requirements in advance, but still have tight quality of service requirements, e.g., guaranteed bandwidth, jitter, and packet loss. These dynamic characteristics mean that the sources can be made to modify their data transfer rates according to network conditions. Depending on the customer&; needs, network operator can differentiate incoming connections and handle those in the buffers and the interfaces in different ways. In this paper, dynamic QoS-aware scheduling algorithm is presented and investigated in the single node case. The purpose of the algorithm is in addition to fair resource sharing to different types of traffic classes with different priorities ?to maximize revenue of the service provider. It is derived from the linear type of revenue target function, and closed form globally optimal formula is presented. The method is computationally inexpensive, while still producing maximal revenue. Due to the simplicity of the algorithm, it can operate in the highly nonstationary environments. In addition, it is nonparametric and deterministic in the sense that it uses only the information about the number of users and their traffic classes, not about call density functions or duration distributions. Also, Call Admission Control (CAC) mechanism is used by hypothesis testing.

Seismic behavior enhancement of frame structure considering parameter sensitivity of self-centering braces

  • Xu, Longhe;Xie, Xingsi;Yan, Xintong;Li, Zhongxian
    • Structural Engineering and Mechanics
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    • v.71 no.1
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    • pp.45-56
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    • 2019
  • A modified mechanical model of pre-pressed spring self-centering energy dissipation (PS-SCED) brace is proposed, and the hysteresis band is distinguished by the indication of relevant state variables. The MDOF frame system equipped with the braces is formulated in an incremental form of linear acceleration method. A multi-objective genetic algorithm (GA) based brace parameter optimization method is developed to obtain an optimal solution from the primary design scheme. Parameter sensitivities derived by the direct differentiation method are used to modify the change rate of parameters in the GA operator. A case study is conducted on a steel braced frame to illustrate the effect of brace parameters on node displacements, and validate the feasibility of the modified mechanical model. The optimization results and computational process information are compared among three cases of different strategies of parameter change as well. The accuracy is also verified by the calculation results of finite element model. This work can help the applications of PS-SCED brace optimization related to parameter sensitivity, and fulfill the systematic design procedure of PS-SCED brace-structure system with completed and prospective consequences.

Improvement of inspection system for common crossings by track side monitoring and prognostics

  • Sysyn, Mykola;Nabochenko, Olga;Kovalchuk, Vitalii;Gruen, Dimitri;Pentsak, Andriy
    • Structural Monitoring and Maintenance
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    • v.6 no.3
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    • pp.219-235
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    • 2019
  • Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

Floating Gas Power Plants

  • Kim, Hyun-Soo
    • Journal of the Korean Society of Industry Convergence
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    • v.23 no.6_1
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    • pp.907-915
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    • 2020
  • Specification selection, Layout, specifications and combinations of Power Drives, and Ship motions were studied for FGPP(Floating Gas-fired Power Plants), which are still needed in areas such as the Caribbean, Latin America, and Southeast Asia where electricity is not sufficiently supplied. From this study, the optimal equipment layout in ships was derived. In addition, the difference between engine and turbine was verified through LCOE(Levelized Cost of Energy) comparison according to the type and combination of Power Drives. Analysis of Hs(Significant Height of wave) and Tp(spectrum Peak Period of wave) for places where this FGPP will be tested or applied enables design according to wave characteristics in Brazil and Indonesia. Normalized Sloshing Pressures of FGPP and LNG Carrier are verified using a sloshing analysis program, which is CFD(Computational Fluid Dynamics) software developed by ABS(American Bureau of Shipping). Power Transmission System is studied with Double bus with one Circuit Breaker Topology. A nd the CFD analysis allowed us to calculate linear roll damping coefficients for more accurate full load conditions and ballast conditions. Through RAO(Response Amplitude Operator) analysis, we secured data that could minimize the movement of ships according to the direction of waves and ship placement by identifying the characteristics of large movements in the beam sea conditions. The FGPP has been granted an AIP(Approval in Principle) from a classification society, the ABS.

Drought forecasting over South Korea based on the teleconnected global climate variables

  • Taesam Lee;Yejin Kong;Sejeong Lee;Taegyun Kim
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.47-47
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    • 2023
  • Drought occurs due to lack of water resources over an extended period and its intensity has been magnified globally by climate change. In recent years, drought over South Korea has also been intensed, and the prediction was inevitable for the water resource management and water industry. Therefore, drought forecasting over South Korea was performed in the current study with the following procedure. First, accumulated spring precipitation(ASP) driven by the 93 weather stations in South Korea was taken with their median. Then, correlation analysis was followed between ASP and Df4m, the differences of two pair of the global winter MSLP. The 37 Df4m variables with high correlations over 0.55 was chosen and sorted into three regions. The selected Df4m variables in the same region showed high similarity, leading the multicollinearity problem. To avoid this problem, a model that performs variable selection and model fitting at once, least absolute shrinkage and selection operator(LASSO) was applied. The LASSO model selected 5 variables which showed a good agreement of the predicted with the observed value, R2=0.72. Other models such as multiple linear regression model and ElasticNet were also performed, but did not present a performance as good as LASSO. Therefore, LASSO model can be an appropriate model to forecast spring drought over South Korea and can be used to mange water resources efficiently.

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Sequential prediction of TBM penetration rate using a gradient boosted regression tree during tunneling

  • Lee, Hang-Lo;Song, Ki-Il;Qi, Chongchong;Kim, Kyoung-Yul
    • Geomechanics and Engineering
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    • v.29 no.5
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    • pp.523-533
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    • 2022
  • Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R2≥0.79; whereas, a one-step prediction is R2≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R2 and UPD is proposed. When UPD increases R2 decreases exponentially; In particular, UPD at R2=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R2 value targeted by an operator. Therefore, a calculation process for the equation between the R2 and UPD is addressed.

Experimental Study for the Resonance Effect of the Power Buoy Amplitude (공진형 전력부이의 상하변위증폭 효과에 관한 실험적 연구)

  • Kweon, Hyuck-Min;Koh, Hyeok-Jun;Kim, Jung-Rok;Choi, Young-Hwan
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.33 no.2
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    • pp.585-594
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    • 2013
  • In this study, laboratory experiments and numerical simulations were conducted to test the performance of resonance power buoy system proposed by Kweon et al.(2010). The system is composed of a linear generator and a mooring buoy. The mover of the linear generator mainly has heave motion driven by vertical oscillation of the buoy. In this system, the velocity discrepancy between the mover and the buoy makes electricity. However, ocean wave energy as a natural resource around Korean peninsula is comparatively small and the driving force for producing electricity is not enough for commercialization. Therefore, it is necessary that the buoy motion be amplified by using resonance characteristics. In order to verify the resonance effects on the test power buoy, the experimental investigations were conducted in the large wave flume (length of 110 m, width of 8 m, maximum depth of 6 m) equipped with regular and random plunger wave generator. The resonance draft of test power buoy is designed for the corresponding period of incident wave, 1.96 sec. Regular wave test results show that the heave response amplitude operator(RAO) by a test buoy has the amplification of 5.66 times higher compared to the wave amplitude at the resonance period. Test results of random waves show that the buoy has the largest spectrum area of 20.73 times higher at the point of not the resonance period but the shorter one of 1.85 sec. Therefore this study suggests the resonance power buoy for wave power generation for commercial application in the case of the coastal and oceanic area with smaller wave energy.

Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models

  • Oh Beom Kwon;Solji Han;Hwa Young Lee;Hye Seon Kang;Sung Kyoung Kim;Ju Sang Kim;Chan Kwon Park;Sang Haak Lee;Seung Joon Kim;Jin Woo Kim;Chang Dong Yeo
    • Tuberculosis and Respiratory Diseases
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    • v.86 no.3
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    • pp.203-215
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    • 2023
  • Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.