• Proceedings of the Korean Geotechical Society Conference
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• 2010.09c
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• pp.72-77
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• 2010

The Incheon Bridge, which was opened to the traffic in October 2009, is an 18.4 km long sea-crossing bridge connecting the Incheon International Airport with the expressway networks around the Seoul metropolitan area by way of Songdo District of Incheon City. This bridge is an integration of several special featured bridges and the major part of the bridge consists of cable-stayed spans. This marine cable-stayed bridge has a main span of 800 m wide to cross the vessel navigation channel in and out of the Incheon Port. In waterways where ship collision is anticipated, bridges shall be designed to resist ship impact forces, and/or, adequately protected by ship impact protection (SIP) systems. For the Incheon Bridge, large diameter circular dolphins as SIP were made at 44 locations of the both side of the main span around the piers of the cable-stayed bridge span. This world's largest dolphin-type SIP system protects the bridge against the collision with 100,000 DWT tanker navigating the channel with speed of 10 knots. Diameter of the dolphin is up to 25 m. Vessel collision risk was assessed by probability based analysis with AASHTO Method-II. The annual frequency of bridge collapse through the risk analysis for 71,370 cases of the impact scenario was less than $0.5{\times}10^{-4}$ and satisfies design requirements. The dolphin is the circular sheet pile structure filled with crushed rock and closed at the top with a robust concrete cap. The structural design was performed with numerical analyses of which constitutional model was verified by the physical model experiment using the geo-centrifugal testing equipment. 3D non-linear finite element models were used to analyze the structural response and energy-dissipating capability of dolphins which were deeply embedded in the seabed. The dolphin structure secures external stability and internal stability for ordinary loads such as wave and current pressure. Considering failure mechanism, stability assessment was performed for the strength limit state and service limit state of the dolphins. The friction angle of the crushed stone as a filling material was reduced to $38^{\circ}$ considering the possibility of contracting behavior as the impact.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.1-12
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• 2021

In this paper, we study empirically examined the adventurous investments in corporate venture capital (CVC) firms' investment in the U.S. based corporate venture capital industry. Unlike existing studies focusing CVC firm's characteristics related to parent corporates and regarding CVC firm as a vehicle of corporate venturing, we identified CVC firm as an independent learning agent to adapt to dynamic environment and investigate their exploration and exploitation in investments based on organizational learning theory. Specifically, we investigate the market-environmental factors affecting CVC's adventurous investment in different sector rather than previously done. First, we examined competition intensity in CVC industry might be related to CVC firm's explorative investments. Second, CVC firm's investment experiences might affect as an inertia to invest on unexperienced sector. Finally, we investigated risk preference effect on CVC firm's venturing investments. The empirical data analyzed in the study contained a total of 85 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a GEE regression analysis and a Logit regression analysis, we found the significance and direction of our independent and moderating variables strongly supported all of our four hypotheses in a highly robust manner.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1263-1263
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• 2001

The amounts of organic matter present in soil and the rate of soil organic matter (SOM) turnover are influenced by agricultural management practice, such as rotation, tillage, forage plow down direct seeding and manure application. The amount of nutrients released from SOM is highly dependent upon the state of the organic matter. If it contains a large proportion of light fractions (low-density) more nutrients will be available to the glowing crops. However, if it contains mostly heavy fractions (high-density) that are difficult to breakdown, then lesser amounts of nutrients will be available. The state of the SOM and subsequent release of nutrients into the soil can be predicted by NIRS as long as a robust regression equation is developed. The NIRS method is known for its rapidity, convenience, simplicity, accuracy and ability to analyze many constituents at the same time. Our hypothesis is that the NIRS technique allows researchers to investigate fully and in more detail each field for the status of SOM, available moisture and other soil properties in Alberta soils for precision farming in the near future. One hundred thirty one (131) Alberta soils with various levels (low 2-6%, medium 6-10%, and high >10%) of organic matter content and most of dry land soils, including some irrigated soils from Southern Alberta, under various management practices were collected throughout Northern, Central and Southern Alberta. Two depths (0- 15 cm and 15-30 cm) of soils from Northern Alberta were also collected. These air-dried soil samples were ground through 2 mm sieve and scanned using Foss NIR System 6500 with transport module and natural product cell. With particle size above 150 microns only, the “Ludox” method (Meijboom, Hassink and van Noorwijk, Soil Biol. Biochem.27: 1109-1111, 1995) which uses stable silica, was used to fractionate SOM into light, medium and heavy fractions with densities of <1.13, 1.13-1.37 and >1.37 respectively, The SOM fraction with the particle size below 150 microns was discarded because practically, this fraction with very fine particles can't be further separated by wet sieving based on density. Total organic matter content, mechanical texture, ash after 375$^{\circ}C$, and dry matter (DM) were also determined by “standard” soil analysis methods. The NIRS regression equations were developed using Infra-Soft-International (ISI) software, version 3.11.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.74-82
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• 2024

In this study, feature point detection and displacement measurement performance depending on feature extraction algorithms were compared and analyzed according to environmental changes and target types in the feature point-based displacement measurement algorithm. A three-story frame structure was designed for performance evaluation, and the displacement response of the structure was digitized into FHD (1920×1080) resolution. For performance analysis, the initial measurement distance was set to 10m, and increased up to 40m with an increment of 10m. During the experiments, illuminance was fixed to 450lux or 120lux. The artificial and natural targets mounted on the structure were set as regions of interest and used for feature point detection. Various feature detection algorithms were implemented for performance comparisons. As a result of the feature point detection performance analysis, the Shi-Tomasi corner and KAZE algorithm were found that they were robust to the target type, illuminance change, and increase in measurement distance. The displacement measurement accuracy using those two algorithms was also the highest. However, when using natural targets, the displacement measurement accuracy is lower than that of artificial targets. This indicated the limitation in extracting feature points as the resolution of the natural target decreased as the measurement distance increased.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.555-562
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• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.25-50
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• 2001

Using Fourier transformation and wavelet analysis, we compared the auditory event-related potentials of the patients with attention deficit-hyperactivity disorders(abbr. ADHD, 13 boys) and normal control children(8 boys). Amplitudes of the event-related potentials which were calculated via Fourier transformation were compared between the groups and between conditions(non-target versus target) in each group. To the non-target stimuli, the patients with ADHD showed significantly greater amplitudes across almost all of the electrode sites and frequencies. To the target stimuli, the incidents which ADHD patients showed much higher amplitudes than normal controls significantly decreased, while those of the reverse results increased significantly. These results were consistent with the comparison results about negative difference wave(abbr. Nd wave) using Fourier transformation. In summary, it was proved that non-target stimulus which should be ignored elicited more robust electrical response from the patients with ADHD than normal children, but the target stimulus which reguired active processing did much less electrical activity in the patients. For the patients, they showed much inhibited electrical response to the target stimuli in some electrodes and frequency ranges. Normal children were more strongly stimulated by the target stimuli in almost all electrodes and frequency ranges than the patients, but less in prefrontal leads and frontal leads. Wavelet analysis results proved that early responses(0-300msec) to the nontarget stimuli of the patients were significantly greater than the normal controls in prefrontal, anterior frontal, some parts of temporal, and occipital lobes and that late response(300-370msec) were significantly lesser than normal children in parietal and central electrodes. Target stimuli elicited significantly higher electrical activity in both group than non-target stimuli did. Prefrontal and frontal lobes showed stronger responses in the patients than normal children irrespective of stimulus condition, but parietal and temporal lobes did higher activities in normal children than the patients only to the target stimuli. In conclusion, the patients with ADHD showed much greater responses to the stimuli which should be ignored, but failed to activated the necessary processes to the target stimuli. Also, we found that the frequency-dimension analysis and wavelet analysis were useful for the signal processing such as event related potentials.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.317-325
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• 2007

Purpose: Biological parameters can be quantified using dynamic PET data with compartment modeling and Nonlinear Least Square (NLS) estimation. However, the generation of parametric images using the NLS is not appropriate because of the initial value problem and excessive computation time. In irreversible model, Patlak graphical analysis (PGA) has been commonly used as an alternative to the NLS method. In PGA, however, the start time ($t^*$, time where linear phase starts) has to be determined. In this study, we suggest a new Multiple Linear Analysis for irreversible radiotracer (MLAIR) to estimate fluoride bone influx rate (Ki). Methods: $[^{18}F]Fluoride$ dynamic PET scans was acquired for 60 min in three normal mini-pigs. The plasma input curve was derived using blood sampling from the femoral artery. Tissue time-activity curves were measured by drawing region of interests (ROls) on the femur head, vertebra, and muscle. Parametric images of Ki were generated using MLAIR and PGA methods. Result: In ROI analysis, estimated Ki values using MLAIR and PGA method was slightly higher than those of NLS, but the results of MLAIR and PGA were equivalent. Patlak slopes (Ki) were changed with different $t^*$ in low uptake region. Compared with PGA, the quality of parametric image was considerably improved using new method. Conclusion: The results showed that the MLAIR was efficient and robust method for the generation of Ki parametric image from $[^{18}F]Fluoride$ PET. It will be also a good alternative to PGA for the radiotracers with irreversible three compartment model.