• Language and Information
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• v.9 no.1
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• pp.69-92
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• 2005

Pattern recognition in cognitive agents is based on (i) the uninterpreted input data (e.g. parameter values) provided by the agent's hardware devices and (ii) and interpreted patterns (e.g. templates) provided by the agent's memory. Computationally, the task consists in finding the memory data corresponding best to the input data, for any given input. Once the best fitting memory data have been found, the input is recognized by applying to it the interpretation which happens to be stored with the memorized pattern. This paper presents a fast converging procedure which starts from a few initially recognized items and then analyzes the remainder of the input by systematically checking for items shown by memory to have been related to the initial items in previous encounters. In this way, known patterns are tried first, and only when they have been exhausted, an elementary exploration of the input is commenced. Efficiency is improved further by choosing the candidate to be tested next according to frequency.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.681-691
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• 2010

LiDAR waveform decomposition plays an important role in LiDAR data processing since the resulting decomposed components are assumed to represent reflection surfaces within waveform footprints and the decomposition results ultimately affect the interpretation of LiDAR waveform data. Decomposing the waveform into a mixture of Gaussians involves two related problems; 1) determining the number of Gaussian components in the waveform, and 2) estimating the parameters of each Gaussian component of the mixture. Previous studies estimated the number of components in the mixture before the parameter optimization step, and it tended to suggest a larger number of components than is required due to the inherent noise embedded in the waveform data. In order to tackle these issues, a new LiDAR waveform decomposition algorithm based on the sequential approach has been proposed in this study and applied to the ICESat waveform data. Experimental results indicated that the proposed algorithm utilized a smaller number of components to decompose waveforms, while resulting IMP value is higher than the GLA14 products.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.29-39
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• 2017

The building skins are important architectural elements in both functional and aesthetical aspects. This study focuses on developing a responsive facade with autonomous opening and closing behaviors in accordance with environmental conditions and user requirements for natural ventilation for the office building. The pneumatic ETFE panels are applied as the skin materials taking advantage of the efficiency of the inflatable skin of lightness, architectural performance and sustainable material properties. The biomimetic design methodology is taken for its innovative and visionary concept for the facade design. The interpretation of the building facade in analogy to natural organisms delivers functional and aesthetic characters. By exploring the structural movements of the plant pores, the facade control is developed to be autonomous by the parameter values. The facade opening and closing configurations are derived through parametric modeling and visualization programming. Through the application of this study, expected results are to improve user comfort and energy efficiency.

• Journal of the Korean Geotechnical Society
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• v.19 no.1
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• pp.151-161
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• 2003

In this study, an interpretation method is proposed to evaluate the stress conditions, including individual excess pore pressure components ($\Delta{u}_{oct}$/ and $\Delta{u}_{shear}$), of normally consolidated clay elements adjacent to the cone face. It is assumed that the stress path of triaxial compression is representative f3r that of the soil element and the soil is elastic-perfectly plastic material. The proposed method is applied to the results of miniature piezocone tests conducted at Louisiana State University calibration chamber system. Based on the results of interpretation, it was found that the ratio of $\Delta{u}_{oct}$/ $\Delta{u}$and $\Delta{u}_{shear}$/$\Delta{u}$ estimated by the proposed method is affected only by the pore pressure parameter. The proposed method gives consistent and reliable values of $\Delta{u}_{oct}$/ $\Delta{u}$and $\Delta{u}_{shear}$/$\Delta{u}$ compared with early works, whereas those obtained by other solutions are significantly dependent on the accuracy in estimating soil properties such as undrained shear strength and rigidity index.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.299-303
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• 2006

Geologic noise, especially located at shallow depth, can be a great obstacle in the interpretation of geophysical data. Thus, it is important to suppress geologic noise in order to accurately detect major anomalous bodies in the survey area. In the inversion of geophysical data, model parameters at shallow depth, which have small size and low contrast of physical property, can be regarded as one of geologic noise. The least-squares method with smoothness constraint has been widely used in the inversion of geophysical data. The method imposes a big penalty on the large model parameter, while a small penalty on the small model parameter. Therefore, it is not easy to suppress small anomalous boies. In this study, we developed a new inversion scheme which can effectively suppress geologic noise by imposing a big penalty on the slowly varying model parameter and a small penalty on the largely varying model parameter. We call the method MTE (main-target emphasizing) inversion. Applying the method to the inversion of 2.5D small loop EM data, we can ensure that it is effective in suppressing small anomalous boies and emphasizing major anomalous bodies in the survey area.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.326-334
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• 2008

The resistivity method has been used to image the electrical properties of the subsurface. Especially, this method has become suitable for monitoring since data could be rapidly and automatically acquired. In this study, we developed a time-lapse inversion algorithm for the interpretation of resistivity monitoring data. The developed inversion algorithm imposes a big penalty on the model parameter with small change, while a minimal penalty on the model parameter with large change compared to the reference model. Through the numerical experiments, we can ensure that the time-lapse inversion result shows more accurate and focused image where model parameters have changed. Also, applying the timelapse inversion method to the leakage detection of an embankment dam, we can confirm that there are three major leakage zones, but they have not changed over time.

• Journal of the Korea Concrete Institute
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• v.21 no.6
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• pp.781-788
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• 2009

Mechanical properties of steel-concrete interface were evaluated on the basis of experimental observations. The properties included bond strength, unbounded and bonded friction angles, residual level of friction angle, mode I fracture energy, mode II bonded fracture energy and unbonded slip-friction energy under different levels of normal stress, and shape parameters to define geometrical shape of failure envelope. For this purpose, a typical type of constitutive model of describing steel-concrete interface behavior was presented based on a hyperbolic three-parameter Mohr-Coulomb type failure criterion. The constitutive model depicts the strong dependency of interface behavior on bonding condition of interface, bonded or unbounded. Values of the interface parameters were determined through interpretation of experimental results, geometry of failure envelope and sensitivity analysis. Nonlinear finite element analysis that incorporates steel-concrete interface as well as material nonlinearities of concrete and steel were performed to predict the experimental results.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Coupled systems mechanics
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• v.11 no.1
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• pp.55-78
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• 2022

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

• Wind and Structures
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• v.6 no.6
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• pp.485-498
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• 2003

This paper presents a mechanical model of Rain-Wind Induced Vibration (RWIV) of stay cables. It is based on the physical interpretation of the phenomenon as given in Cosentino, et al. (2003, referred as Part I). The model takes into account all the main forces acting on cable, on the upper water rivulet (responsible of the excitation) and the cable-rivulet interaction. It is a simplified (cable cross-sectional and deterministic) representation of the actual (stochastic and three-dimensional) phenomenon. The cable is represented by its cross section and it is subjected to mechanical and aerodynamic (considering the rivulet influence) forces. The rivulet is supposed to oscillate along the cable circumference and it is subjected to inertial and gravity forces, pressure gradients and air-water-cable frictions. The model parameters are calibrated by fitting with experimental results. In order to validate the proposed model and its physical basis, different conditions (wind speed and direction, cable frequency, etc.) have been numerically investigated. The results, which are in very good agreement with the RWIV field observations, confirm the validity of the method and its engineering applicability (to evaluate the RWIV sensitivity of new stays or to retrofit the existing ones). Nevertheless, the practical use of the model probably requires a more accurate calibration of some parameters through new and specifically oriented wind tunnel tests.