• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.

• Journal of Veterinary Science
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• v.23 no.5
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• pp.78.1-78.13
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• 2022

Background: Florfenicol might be ineffective for treating Staphylococcus aureus small colony variants (SCVs) mastitis. Objectives: In this study, florfenicol-loaded chitosan (CS)-sodium tripolyphosphate (TPP) composite nanogels were prepared to allow targeted delivery to SCV infected sites. Methods: The formulation screening, the characteristics, in vitro release, antibacterial activity, therapeutic efficacy, and biosafety of the florfenicol composite nanogels were studied. Results: The optimized formulation was obtained when the CS and TPP were 10 and 5 mg/mL, respectively. The encapsulation efficiency, loading capacity, size, polydispersity index, and zeta potential of the optimized florfenicol composite nanogels were 87.3% ± 2.7%, 5.8% ± 1.4%, 280.3 ± 1.5 nm, 0.15 ± 0.03, and 36.3 ± 1.4 mv, respectively. Optical and scanning electron microscopy showed that spherical particles with a relatively uniform distribution and drugs might be incorporated in cross-linked polymeric networks. The in vitro release study showed that the florfenicol composite nanogels exhibited a biphasic pattern with the sustained release of 72.2% ± 1.8% at 48 h in pH 5.5 phosphate-buffered saline. The minimal inhibitory concentrations of commercial florfenicol solution and florfenicol composite nanogels against SCVs were 1 and 0.25 ㎍/mL, respectively. The time-killing curves and live-dead bacterial staining showed that the florfenicol composite nanogels were concentration-dependent. Furthermore, the florfenicol composite nanogels displayed good therapeutic efficacy against SCVs mastitis. Biological safety studies showed that the florfenicol composite nanogels might be a biocompatible preparation because of their non-toxic effects on the renal tissue and liver. Conclusions: Florfenicol composite nanogels might improve the treatment of SCV infections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.8
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• pp.52-59
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• 2007

In this paper, we analyzed and measured implant isolation characteristics for a 1.25 Gbps monolithic integrated hi-directional (M-BiDi) optoelectronic system-on-a-chip, which is a key component to constitute gigabit passive optical networks (PONs) for a fiber-to-the-home (FTTH). Also, we derived an equivalent circuit of the implant structure under various DC bias conditions. The 1.25 Gbps M-BiDi transmit-receive SoC consists of a laser diode with a monitor photodiode as a transmitter and a digital photodiode as a digital data receiver on the same InP wafer According to IEEE 802.3ah and ITU-T G.983.3 standards, a receiver sensitivity of the digital receiver has to satisfy under -24 dBm @ BER=10-12. Therefore, the electrical crosstalk levels have to maintain less than -86 dB from DC to 3 GHz. From analysed and measured results of the implant structure, the M-BiDi SoC with the implant area of 20 mm width and more than 200 mm distance between the laser diode and monitor photodiode, and between the monitor photodiode and digital photodiode, satisfies the electrical crosstalk level. These implant characteristics can be used for the design and fabrication of an optoelectronic SoC design, and expended to a mixed-mode SoC field.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.77-90
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• 2002

Network expansion has been an inevitable method for most traffic equilibrium assignments to consider intersection movements such as intersection delays. The drawback of network expansion is that because it dramatically increases network sizes to emulate possible directional movements as corresponding links, not only is complexities for building network amplified, but computational performance is shrunk. This paper Proposes a new variational inequality formulation for a user-optimal traffic equilibrium assignment model to explicitly consider directional delays without building expanded network structures. In the formulation, directional delay functions are directly embedded into the objective function, thus any modification of networks is not required. By applying a vine-based shortest Path algorithm into the diagonalization algorithm to solve the problem, it is additionally demonstrated that various loop-related movements such as U-Turn, P-Turn, etc., which are frequently witnessed near urban intersections, can also be imitated by blocking some turning movements of intersections. The proposed formulation expects to augment computational performance through reduction of network-building complexities.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.9-15
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• 2017

With the development of semiconductor integration technology, central processing units and storage devices have been miniaturized and performance has been rapidly developed, interconnection network technology is becoming a more important factor in terms of the performance of high performance computing system. In this paper, we analyze the trend of interconnection network technology used in high performance computing. Interconnect technology, which is the most widely used in the Supercomputer Top 500(2017. 06.), is an Infiniband. Recently, Ethernet is the second highest share after InfiniBand due to the emergence of 40/100Gbps Gigabit Ethernet technology. Gigabit Ethernet, where latency performance is lower than InfiniBand, is preferred in cost-effective medium-sized data centers. In addition, top-end HPC systems that demand high performance are devoting themselves from Ethernet and InfiniBand technologies and are attempting to maximize system performance by introducing their own interconnect networks. In the future, high-performance interconnects are expected to utilize silicon-based optical communication technology to exchange data with light.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.56-56
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• 2010

Single-walled carbon nanotubes (SWCNTs) have attracted much attention as a promising material for transparent conducting films (TCFs), due to their superior electrical conductivity, high mechanical strength, and complete flexibility as well as their one-dimensional morphological features of extremely high length-to-diameter ratios. This study investigated three kinds of SWCNTs with different purities: as-produced SWCNTs (AP-SWCNTs), thermally purified SWCNTs (TH-SWCNTs), thermally and acid purified SWCNTs (TA-SWCNTs). The purity of each SWCNT sample was assessed by considering absorption peaks in the semiconducting ($S_{22}$) and metallic ($M_{11}$) tubes with UV-Vis NIR spectroscopy and a metal content with thermogravimetric analysis (TGA). The purity increased as proceeding the purification stages from the AP-SWCNTs through the thermal purification to the acid purification. The samples containing different contents of SWCNTs were dispersed in water using sodium dodecyl benzensulfate (SDBS). Aqueous suspensions of different purities of SWCNTs were prepared to have similar absorbances in UV-Vis absorption measurements so that one can make the TCFs possess similar optical transmittances irrespective of the SWCNT purity. Transparent conductive SWCNT networks were formed by spraying an SWCNT suspension onto a poly(ethyleneterephthalate) (PET) substrate. As expected, the TCFs fabricated with AP-SWCNTs showed very high sheet resistances. Interestingly, the TH-SWCNTs gave lower sheet resistances to the TFCs than the TA-SWCNTs although the latter was of higher purity in the SWCNT content than the former. The TA-SWCNTs would be shortened in length and be more bundled by the acid purification, relative to the TH-SWCNTs. For both purified (TH, TA) samples, the subsequent nitric acid ($HNO_3$) treatment greatly lowered the sheet resistances of the TCFs, but almost eliminated the difference of sheet resistances between them. This seems to be because the electrical conductivity increased not only due to further removal of surfactants but also due to p-type doping upon the acid treatment. The doping effect was likely to overwhelm the effect of surfactant removal. Although the nitric acid treatment resulted in the similar. electrical properties to the two samples, the TCFs of TH-SWCNTs showed much lower sheet resistances than those of the TA-SWCNTs prior to the acid treatment.

• Composites Research
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• v.36 no.1
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• pp.48-52
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• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.