• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.294-299
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• 2007

For efficient and accurate diagnosis of ultrasound images, appropriate time gain compensation(TGC) and dynamic range(DR) control of ultrasound echo signals are important. TGC is used for compensating the attenuation of ultrasound echo signals along the depth, and DR controls the image contrast. In recent ultrasound systems, these two factors are automatically set by a system and/or manually adjusted by an operator to obtain the desired image quality on the screen. In this paper, we propose an algorithm to find the optimized parameter values far TGC and DR automatically. In TGC optimization, we determine the degree of attenuation compensation along the depth by dividing an image into vertical strips and reliably estimating the attenuation characteristic of ultrasound signals. For DR optimization, we define a novel cost function by properly using the characteristics of ultrasound images. We obtain experimental results by applying the proposed algorithm to a real ultrasound(US) imaging system. The results verify that the proposed algorithm automatically sets values of TGC and DR in real-time such that the subjective quality of the enhanced ultrasound images may be sufficiently high for efficient and accurate diagnosis.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.195.2-195.2
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• 2011

The borehole heat exchanger of Geothermal Heat Pump (GHP) system should be sustainable and cost effective for long term operation. To guaranty the performance of the system thermal Response Tests (TRTs) with simple recommended procedures have been applied in many countries. Korea government developed a standard TRT procedure in order to control the quality on GHP projects. In the TRT procedure interpretation method has a rule that data set has to be interpreted by the line source model(LSM). The LSM employes some assumptions that surrounding medium is homogeneous and the line source is infinite and constant heat flux, however real ground condition is unisotropic and heterogeneous, and showing regional or local ground water flows in many cases. We need to develope improved evaluation models to estimate accurate ground thermal conductivity with respect to geological and influence of ground water because current TRT standard test procedure has limitations to be applied for every locations and system. This study surveyed the uncertainty of the thermal parameters from the interpretation method considering different evaluation period. The interpretation of 208 TRT data sets represents limitations of LSM application that some obtained ground thermal conductivities are statistically unstable and convergence time of ground thermal conductivity over test period shows trends responding the length of test period. This evaluation study will be helpful to provide some effective procedure for the thermal parameter estimation and to complement current TRT standard procedure.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.85-87
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• 1994

In this paper, we present an object-oriented stereotactic radiosurgery planning system, which accepts medical images such as CT and angiography, transforms the coordinates to a reference frame coordinate, calculates dose distributions, and finally displays isodose curves over the images. The user finds an adequate one for radiosurgeries after performing computer simulations on different treatment parameter sets. The object-oriented design concept was fully applied to the system composed of seven manager objects of different classes: a patient information manager, a user-interface manager, a coordinate transformation manager, a blackboard manager, a dose calculation manager, an isodose curve display manager, and a report manager. All the user interactions are carried out through the use of mouse buttons. The performance of the system was verified by four physicians and two medical physicists, and now is being used in two clinical sites.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.219-226
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• 2012

Recently, shape analysis of human organs has achieved much attention, owing to its potential to localize structural abnormalities. For a group-wise shape analysis, it is important to accurately restore the shape of a target structure in each subject and to build the inter-subject shape correspondences. To accomplish this, we propose a shape modeling method based on the Laplacian deformation framework. We deform a template model of a target structure in the segmented images while restoring subject-specific shape features by using Laplacian surface representation. In order to build the inter-subject shape correspondences, we implemented the progressive weighting scheme for adaptively controlling the rigidity parameter of the deformable model. This weighting scheme helps to preserve the relative distance between each point in the template model as much as possible during model deformation. This area-preserving deformation allows each point of the template model to be located at an anatomically consistent position in the target structure. Another advantage of our method is its application to human organs of non-spherical topology. We present the experiments for evaluating the robustness of shape modeling against large variations in shape and size with the synthetic sets of the second cervical vertebrae (C2), which has a complex shape with holes.

• Smart Structures and Systems
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• v.7 no.2
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• Wind and Structures
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• v.24 no.5
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• pp.465-480
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• 2017

Modelling an equilibrium atmospheric boundary layer (ABL) in computational wind engineering (CWE) and relevant areas requires the boundary conditions, the turbulence model and associated constants to be consistent with each other. Among them, the inflow boundary conditions play an important role and determine whether the equations of the turbulence model are satisfied in the whole domain. In this paper, the idea of modeling an equilibrium ABL through specifying proper inflow boundary conditions is extended to the SST $k-{\omega}$ model, which is regarded as a better RANS model for simulating the blunt body flow than the standard $k-{\varepsilon}$ model. Two new sets of inflow boundary conditions corresponding to different descriptions of the inflow velocity profiles, the logarithmic law and the power law respectively, are then theoretically proposed and numerically verified. A method of determining the undetermined constants and a set of parameter system are then given, which are suitable for the standard wind terrains defined in the wind load code. Finally, the full inflow boundary condition equations considering the scale effect are presented for the purpose of general use.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1451-1458
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• 2004

Current software reliability growth models based on Gompertz growth curve are all logarithmic type. Software reliability growth models based on logarithmic type Gompertz growth curve has difficulties in parameter estimation. Therefore this paper proposes a software reliability growth model based on the logistic type Gompertz growth curie. Its usefulness is empirically verified by analyzing the failure data sets obtained from 13 different software projects. The parameters of model are estimated by linear regression through variable transformation or Virene's method. The proposed model is compared with respect to the average relative prediction error criterion. Experimental results show that the pro-posed model performs better the models based on the logarithmic type Gompertz growth curve.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.21-27
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• 2015

An improved method based on direct extraction and simultaneous optimization is developed to determine model parameters of symmetric inductors fabricated by the high resistivity(HR) silicon-on-insulator(SOI) RF CMOS process. In order to improve modeling accuracy, several model parameters are directly extracted by Y and Z-parameter equations derived from two equivalent circuits of symmetric inductor and grounded center-tap one, and the number of unknown parameters is reduced using parallel resistance and total inductance equations. In order to improve optimization accuracy, two sets of measured S-parameters are simultaneously optimized while same model parameters in two equivalent circuits are set to common variables.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Computers and Concrete
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• v.20 no.1
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.