• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.129-138
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• 2010

For the fractional factorial designs, the resolution-IV designs can be used when we want to estimate the main effects and to investigate the structure of the non-negligible two-factor interaction effects, when the three-factor and higher order interaction effects are all negligible. However we need to add the additional treatment combination in order to identify the influential interactions for the resolution-IV fracrtional factorial designs. In this paper we investigate the statistical structure for 3-level resolution-IV designs constructed by fold-over scheme and introduce a method for analyzing the influential two-factor interactions.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.61-70
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• 2020

Convolutional Neural Networks (CNN) have been used extensively in recent times to solve image classification and segmentation problems. However, the use of CNNs in image super-resolution problems remains largely unexploited. Filter interpolation and prediction model methods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lot of the edge information are lost. In this paper, we analyze super resolution based on CNN and the wavelet transform super resolution method. We compare and analyze the performance according to the number of layers and the training data of the CNN.

• Korean Journal of Computational Design and Engineering
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• v.7 no.2
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• pp.121-130
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• 2002

The concept of Level Of Detail (LOD) was introduced and has been used to enhance display performance and to carry out certain engineering analysis effectively. We would like to use an adequate complexity level for each geometric model depending on specific engineering needs and purposes. Solid modeling systems are widely used in industry, and are applied to advanced applications such as virtual assembly. In addition, as the demand to share these engineering tasks through networks is emerging, the problem of building a solid model of an appropriate resolution to a given application becomes a matter of great necessity. However, current researches are mostly focused on triangular mesh models and various operators to reduce the number of triangles. So we are working on the multi-resolution of the solid model itself, rather than that of the triangular mesh model. In this paper, we propose multi-resolution representation of B-rep model by reordering and converting design features into an enclosing volume and subtractive features.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.488-498
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• 2008

Soil and Water Assessment Tool (SWAT) model have been widely used in simulating hydrology and water quality analysis at watershed scale. The SWAT model extracts topographic feature using the Digital Elevation Model (DEM) for hydrology and pollutant generation and transportation within watershed. Use of various DEM cell size in the SWAT leads to different results in extracting topographic feature for each subwatershed. So, it is recommended that model users use very detailed spatial resolution DEM for accurate hydrology analysis and water quality simulation. However, use of high resolution DEM is sometimes difficult to obtain and not efficient because of computer processing capacity and model execution time. Thus, the SWAT Topographic Feature Extraction Error (STOPFEE) Fix module, which can extract topographic feature of high resolution DEM from low resolution and updates SWAT topographic feature automatically, was developed and evaluated in this study. The analysis of average slope vs. DEM cell size revealed that average slope of watershed increases with decrease in DEM cell size, finer resolution of DEM. This falsification of topographic feature with low resolution DEM affects soil erosion and sediment behaviors in the watershed. The annual average sediment for Soyanggang-dam watershed with DEM cell size of 20 m was compared with DEM cell size of 100 m. There was 83.8% difference in simulated sediment without STOPFEE module and 4.4% difference with STOPFEE module applied although the same model input data were used in SWAT run. For Imha-dam watershed, there was 43.4% differences without STOPFEE module and 0.3% difference with STOPFEE module. Thus, the STOPFEE topographic database for Soyanggang-dam watershed was applied for Chungju-dam watershed because its topographic features are similar to Soyanggang-dam watershed. Without the STOPFEE module, there was 98.7% difference in simulated sediment for Chungju-dam watershed for DEM cell size of both 20 m and 100 m. However there was 20.7% difference in simulated sediment with STOPFEE topographic database for Soyanggang-dam watershed. The application results of STOPFEE for three watersheds showed that the STOPFEE module developed in this study is an effective tool to extract topographic feature of high resolution DEM from low resolution DEM. With the STOPFEE module, low-capacity computer can be also used for accurate hydrology and sediment modeling for bigger size watershed with the SWAT. It is deemed that the STOPFEE module database needs to be extended for various watersheds in Korea for wide application and accurate SWAT runs with lower resolution DEM.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.463-466
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• 2009

The numerical simulation optimized by Four Dimensional Data Assimilation (FDDA) with Quick Scatterometer (QuikSCAT) data is carried out to evaluate wind resource characteristics at various heights in the southeastern area of the Korean Peninsula, where wind farms are planned to be built on on- and off-shore as well as comparable diurnal wind variations are characterized at the surface. The temporal and spatial distributions of modeled wind speeds showed good agreement with the observations based on the temporal variation analysis. Model results indicate that the higher model is performed in resolution, the more precise results is at turbine hub height. Occasionally, wind speed variations for each numerical resolution has a different regional and seasonal variations. In the coast area, hub height wind speed of 9km-resolution is simillar to that of 3km-resolution. On the other hand, hub height wind speed of 3km-resolution is simillar to that of 1km-resolution in the Jiri mountainous area.

• Journal of Arbitration Studies
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• v.25 no.3
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• pp.155-184
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• 2015

This paper addresses the question of what kinds of dispute resolution choices can effectively handle complex intellectual property disputes, given the rising importance of IP, the increasing frequency and complexity of IP disputes, and the lack of research on dispute resolution strategies. For this analysis, the study adopted the analytic hierarchy process approach, which covers complex, multi-criteria decision problems, to quantify the expert's judgments on IP dispute resolution choice. Its results show that the effectiveness of resolution methods differs, depending on the type of IP dispute classified into seven issues, which are (i) requirement for validity of IP right, (ii) range and duration of IP right, (iii) transfer of IP right, (iv) licensing, (v) use of IP right, (vi) declaration of IP infringement, and (vii) estimation of damage. The disputes over IPR ownership and IP infringement remain challenging issues in due to strong requirement of the cross-border enforcement. Alternative dispute resolution (ADR), especially arbitration, is determined to be a more effective method to deal with international IP disputes, but various advanced types of ADR techniques should be further developed to deal with the increasing complexity of IP disputes.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.459-466
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• 2023

In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.71-84
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• 2001

The geographical phenomena in space are well observed in the specific scale. This scale is called the operational scale. For an analysis of the optimum scale, it is needed to measure and represent the characteristics of attribute information extracted from the satellite imagery. The development of remote sensing technique makes various images with different resolution available. Researchers can select the image with optimum resolution for their analysis among various resolutions. For an effective analysis of the scale characteristics of satellite image, we investigated the characteristics of attribute information extracted from satellite image with different resolution. The two stage-procedure for exploring the optimum resolution proposed in this study was tested by applying to the satellite imagery covering Sunchon bay. This procedure can be an effective tool utilizing the scale characteristics of attribute information extracted from satellite imagery.