• The Journal of Engineering Geology
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• v.14 no.1
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• pp.21-28
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• 2004

Three-dimensional (3-D) resistivity inversion including a topographic effect can be considered theoretically to be the technique of acquiring the most accurate image in the interpretation of resistivity data, because it includes characteristic image that the actual subsurface structure is 3-D. In this study, a finite-element method was used as the numerical method in modeling, and the efficiency of Jacobian calculation has been maximized with sensitivity analysis for the destination block in inversion process. Also, during the iterative inversion, the resolution of inversion can be improved with the method of selecting the optimal value of Lagrange multiplier yielding minimum RMS(root mean square) error in the parabolic equation. In this paper, we present synthetic examples to compare the difference between the case which has the toprographic effect and the other case which has not the effect in the inversion process.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2711-2720
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• 1997

Multitasking and microtasking on the CRAY machine provides still another way to improve computational power. Since CRAY-2 has 4 processors we can achieve speedup up to 4 properly designed algorithms. In this paper we present two parallelizations of linear system solution in the CRAY-2 with multitasking and microtasking library. One is the LU decomposition on the dense matrices and the other is the iterative solution of large sparse linear systems with the preconditioner proposed by Radicati di Brozolo. In the first case we realized a speedup of 1.3 with 2 processors for a matrix of dimension 600 with the multitasking and in the second case a speedup of around 3 with 4 processors for a matrix of dimension 600 with the multitasking and in the second case a speedup of around 3 with 4 processors for a matrix of dimension 8192 with the microtasking. In the first case the speedup is limited because of the nonuniform vector lenghts. In the second case the ILU(0) preconditioner with Radicati's technique seem to realize a reasonable high speedup with 4 processors.

• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.989-995
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• 2018

Argonne National Laboratory of the United States and Kharkov Institute of Physics and Technology (KIPT) of Ukraine have cooperated on the development, design, and construction of a neutron source facility. The facility was constructed at Kharkov, Ukraine, and its commissioning process is underway. The facility will be used for researches, producing medical isotopes, and training young nuclear specialists. The neutron source facility is designed with a provision to include a cryogenically cooled moderator system-a cold neutron source (CNS). This CNS provides low-energy neutrons, which will be used in the scattering experiment and material structures analysis. Cold neutron guides, coated with reflective material for the low-energy neutrons, will be used to transport the cold neutrons to the experimental site. The cold neutron guides would keep the cold neutrons within certain energy and angular space concentrated inside, while most of the gamma rays and high-energy neutrons are not affected by the cold neutron guides. For the KIPT design, the cold neutron guides need to extend several meters outside the main shield of the facility, and curved guides will also be used to remove the gamma and high-energy neutron. The neutron guides should be installed inside a shield structure to ensure an acceptable biological dose in the facility hall. Heavy concrete is the selected shielding material because of its acceptable performance and cost. Shield design analysis was carried out for the CNS guide hall. MCNPX was used as the major computation tool for the design analysis, with neutron and gamma dose calculated separately. Weight windows variance reduction technique was also used in the shield design. The goal of the shield design is to keep the total radiation dose below the $5.0{\mu}Sv/hr$ guideline outside the shield boundary. After a series of iterative MCNPX calculations, the shield configuration and parameters of CNS guide hall were determined and presented in this article.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.19 no.1
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• pp.51-56
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• 2013

From the time the product is manufactured until it is carried and ultimately used, the product is subjected to some form of handling and transportations. During this process, the product can be subjected to many potential hazards. One of them is the damage caused by shocks. In order to design a product-package system to protect the product, the peak acceleration or G force to the product that causes damage needs to be determined. When a corrugated fiberboard box loaded with products is dropped onto the ground, part of the energy acquired due to the action of the gravitational acceleration during the free fall is dissipated in the product and the package in various ways. The shock absorbing characteristics of the packaging cushion materials are presented as a family of cushion curves in which curves showing peak accelerations during impacts for a range of static loads are shown for several drop heights. The new method for determining the shock absorbing characteristics of cushioning materials for protective packaging has been described and demonstrated. It has been shown that cushion curves can be produced by combining the static compression and impact characteristics of the material. The dynamic factor was determined by the iterative least mean squares (ILMS) optimization technique in which the discrepancies between peak acceleration data predicted from the theoretical model and obtained from the impact tests are minimized. The approach enabled an efficient determination of cushion curves from a small number of experimental impact data.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• Journal of the Korean Institute of Gas
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• v.2 no.1
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• pp.40-46
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• 1998

Mathematical modeling on the corrosion of the steel casing and main pipe due to the protection current resulting from a cathodic protection system was carried out using boundary element method. The model is consisted of Laplace's equation with non-linear boundary conditions(Tafel equations) and the iterative technique to determine the miexed potential of the steel casing. The model is applied to the normal steel casing section as well as abnormal one with defects such as metal touch and insulation defects. From the modeling procedure, we can calculate the potential distributions and current density distributions of the system. The theoretical results of the qualitatiive corrosion aspect along the steel casing and main pipe agree well with the experimental results within the experimental conditions studied.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.152-152
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• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.631-636
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• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.97-103
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• 2005

Recent development of marine magnetic gradient systems, using arrays of sensors, has made it possible to survey large contaminated areas very quickly. However, underwater Unexploded Ordnances (UXO) can be moved by water currents. Because of this mobility, the cleanup process in such situations becomes dynamic rather than static. This implies that detection should occur in near real-time for successful remediation. Therefore, there is a need for a fast interpretation method to rapidly detect signatures of underwater objects in marine magnetic data. In this paper, we present a fast method for location and characterization of underwater UXOs. The approach utilises gradient interpretation techniques (analytic signal and Euler methods) to locate the objects precisely. Then, using an iterative linear least-squares technique, we obtain the magnetization characteristics of the sources. The approach was applied to a theoretical marine magnetic anomaly, with random errors, over a known source. We demonstrate the practical utility of the method using marine magnetic gradient data from Japan.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.257-264
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• 2019

This paper presents a single image haze removal method via a pixel-based joint BDCP (bright and dark channel prior) and a hierarchical bilateral filter in order to reduce computational complexity and memory requirement while improving the dehazing performance. Pixel-based joint BDCP reduces the computational complexity compared to the patch-based DCP, while making it possible to estimate the atmospheric light in pixel unit and the transmission more accurately. Moreover the bilateral filter, which can smooth an image effectively while preserving edges, refines the transmission to reduce the halo effects, and its hierarchical structure applied to edges only prevents the increase of complexity from the iterative application. Experimental results on various hazy images show that the proposed method exhibits excellent haze removal performance with low computational complexity compared to the conventional methods, and thus it can be applied in various fields.