• Fashion & Textile Research Journal
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• v.12 no.4
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• pp.500-507
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• 2010

In this study, physical properties were studied by using latent stretching yarn in order to develop the texturing yarn technique for super bulky yarn, which is better in bulkiness and handle than natural wool and also adds property of synthetic fiber to natural wool. In order to obtain textured conditions by analysing basic properties for manufacturing DTY yarn with super bulky property, DTY 50d/12 after spinning latent yarn spined POY 80d/12 was obtained under the two conditions of (i) false twist(T/M) level 3 in DTY texturing and (ii) draw ratio level 4 in draw texturing. For DTY texturing yarn, Elongation rate increased as the heat treatment time and temperatures increased. In addition, shrinkage became higher as false twist was higher, so that elongation rate became lower. When annealing became longer in time and higher in temperature, initial modulus increased. In addition, as the count of false twist increased, the initial modulus showed higher values. For draw texturing yarn, under the conditions of heat temperature 180 and heating time 30 minutes, shrinkage rate in draw ratio 1.55 and 1.6 draw ratio was 7%, and that in 1.65 and 1.7 draw ratio was 8.5%. High draw ratio samples' tenacity was much influenced by heating time and temperature, but low draw ratio samples' tenacity was influenced not by treated time, but by treated temperature.

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.

• Textile Coloration and Finishing
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• v.1 no.1
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• pp.54-62
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• 1989

The woven fabric composed of nylon 6 filaments was treated with aqueous solutions (20, 30, 40, 50, 60%) of formic acid at 3$0^{\circ}C$ for 10 minutes under unrestrained condition, and the shrinkage behavior and some kinds of properties were examined. The shrinkages of the constituent yarns and fabric were increased with formic acid concentration, but they were lower than that of the original filaments because of fabric-structural factors. And the shrinkage of the warp was lower than that of the weft because of the residual stress from weaving process. By the restraint forces such as fabric-structural factors and residual stress, the constituent filaments were damaged partially at 60% of formic acid concentration and the degree of damage on the warp was greater than on the weft. And though the fabric count were increased overall, the spacing between the warps was decreased prior to the weft and eliminated nearly at 60% of formic acid concentration. The thickness, tensile strength, elongation, and handle value of fabric were increased overall with formic acid concentration excepting that the tensile strength for both the warp and weft directions and the elongation for the warp direction were decreased instead by the damage of yarns. But the crease recovery was decreased except the case of the weft direction at 60% of formic acid concentration.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.46-51
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• 2007

This research work aims at treating saline wastewater generated from a fish market using four Sequencing Batch Reactors (SBR) operated under different conditions. The effect of C/N ratio (3, 6) and salt concentration (0.5~2%) on organic and nitrogen removal was studied. The synthetic wastewater prepared with glucose ($C_6H_{12}O_6$) as the primary carbon source along with ammonium chloride ($NH_4Cl$) was used in the three reactors. The fill, anoxic, aeration, settle and draw conditions were 2 hr, 4 hr, 4 hr and 2 hr respectively. The fourth reactor was operated at different conditions to investigate the practical feasibility of SBR application to handle fish market wastewater generated in Ulsan city that had fluctuating loading characteristics. Though the unacclimated sludge was initially affected by the salt concentration, the acclimated sludge removed 95% of the organics irrespective of the NaCl concentration and C/N ratio. However, the removal of nitrogen was affected more by C/N ratio than the salt concentration. While handling fish market wastewater, though the organic and nitrogen loading rate were varying between $0.009{\sim}0.259gCOD_{OH}/gVSS/day$ and 0.005~0.034 gN/gVSS/day, the effluent concentrations were far less than the effluent standard of $120mgCOD_{OH}/L$ and 60 mgN/L respectively, except when loading rates were fluctuating and 4 times higher than the average.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.403-410
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• 2019

Image de-snowing aims at eliminating the negative influence by snow particles and improving scene understanding in images. In this paper, a perceptual generative adversarial network based a single image snow removal method is proposed. The residual U-Net is designed as a generator to generate the snow free image. In order to handle various sizes of snow particles, the inception module with different filter kernels is adopted to extract multiple resolution features of the input snow image. Except the adversarial loss, the perceptual loss and total variation loss are employed to improve the quality of the resulted image. Experimental results indicate that our method can obtain excellent performance both on synthetic and realistic snow images in terms of visual observation and commonly used visual quality indices.

• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.71-78
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• 1998

Inversion schemes of 2-D MT survey data generally take enormous computational time and computer memory. In addition, careful attention must be paid in handling MT data, especially in cases of TM mode, inversion results can be seriously distorted because of static effect caused by current channeling across inhomogeneous surface boundaries. There-fore inversion algorithm using the GRRI scheme for TM mode MT data was implemented. This scheme is based on a perturbation analysis with a locally 2-D analysis and local inversions were sequently performed over each divided section without additional forward modelings. The algorithm was applied to several synthetic data for the purpose of verification of its efficiency and applicability. With less computer resources than conventional schemes, it could handle static effect directly by including current channeling across inhomogeneous boundaries. Thus it is expected to be used for an useful tool such as a real-time inversion scheme in the field.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of Korean Society on Water Environment
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• v.26 no.4
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• pp.557-565
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• 2010

Recently, regulations on toxic compounds in aquatic environment have been strengthened in korea due to the increasing public awareness of the water quality. Typically, these regulations include introduction of emerging toxic compounds and stricter effluent limitations for the already regulated compounds. However, too strict regulations may cause excessive burden on the industry. Therefore it is also important to assess the economic impacts when the new effluent limitation guidelines are introduced. The estimation of the additional cost for the wastewater dischargers to meet the new guidelines are based on the selected treatment technology to handle the hazardous substances and the regulatory levels for effluent limitations. To explore the procedures for cost estimation in enforcing new effluent limitations, a case study was performed specially for 1,4-dichlorobenzene. The pollutants of concern are surveyed for different industrial categories and various treatment technologies. For a given pollutant, the general performances of the treatment technologies are surveyed and a representative technology is selected. For a given technology, the capital cost and annual Operation and Maintenance (O&M) cost was calculated. The calculation of baseline costs to operate ordinary treatment technologies is also important. The ratio between the cost for introducing new treatment process and the baseline cost required for conventional technology was used to evaluate the economic impact on the industry. For 1,4-dichlorobenzene, steam stripping and activated carbon processes were selected as the specific treatment technologies. The cost effects to the regulation of the compound were found to be 6.4% and 14.5% increase in capital cost and O&M cost, respectively, at the flow rate over $2,000m^3/d$ for the categories of synthetic resin and other plastics manufacturing industry. For the case of petrochemical basic compounds manufacturing industry, the cost increases were 5.8% and 12.4%, respectively. It was suggested that cost effect analysis to evaluate the economic impacts of new effluent limitations on the industry is crucial to establish more balanced and reasonable effluent limitations to manage the industrial wastewater containing emerging toxic compounds in the wastewater.

• Geophysics and Geophysical Exploration
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• v.19 no.4
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• pp.220-227
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• 2016

Marine seismic data have not only primary signals from subsurface but also ghost signals reflected from the sea surface. The ghost decreases temporal resolution of seismic data because it attenuates specific frequency components. For eliminating the ghost signals effectively, the exact ghost delaytimes and reflection coefficients are required. Because of undulation of the sea surface and vertical movements of airguns and streamers, the ghost delaytime varies spatially and randomly while acquiring seismic data. The reflection coefficient is a function of frequency, incidence angle of plane-wave and the sea state. In order to estimate the proper ghost delaytimes considering these characteristics, we compared the ghost delaytimes estimated with L-1 norm, L-2 norm and kurtosis of the deghosted trace and its autocorrelation on synthetic data. L-1 norm of autocorrelation showed a minimal error and the reflection coefficient was calculated using Kirchhoff approximation equation which can handle the effect of wave height. We applied the estimated ghost delaytimes and the calculated reflection coefficients to remove the source and receiver ghost effects. By removing ghost signals, we reconstructed the frequency components attenuated near the notch frequency and produced the migrated stack section with enhanced temporal resolution.