• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.119-126
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• 2024

Disease caused by the coronavirus (COVID-19) is sweeping the globe. There are numerous methods for identifying this disease using a chest imaging. Computerized Tomography (CT) chest scans are used in this study to detect COVID-19 disease using a pretrain Convolutional Neural Network (CNN) ResNet50. This model is based on image dataset taken from two hospitals and used to identify Covid-19 illnesses. The pre-train CNN (ResNet50) architecture was used for feature extraction, and then fully connected layers were used for classification, yielding 97%, 96%, 96%, 96% for accuracy, precision, recall, and F1-score, respectively. When combining the feature extraction techniques with the Back Propagation Neural Network (BPNN), it produced accuracy, precision, recall, and F1-scores of 92.5%, 83%, 92%, and 87.3%. In our suggested approach, we use a preprocessing phase to improve accuracy. The image was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, which was followed by cropping the image before feature extraction with ResNet50. Finally, a fully connected layer was added for classification, with results of 99.1%, 98.7%, 99%, 98.8% in terms of accuracy, precision, recall, and F1-score.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1993.07a
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• pp.20-25
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• 1993

In coastal waters, more often than not, waves propagate on currents driven by tidal forces, earth’s gravity, or wind. There have been a number of studies for dealing with the change of wave spectrum due to tile presence of current. Based on the conservation of wave action, Hedges et al. (1985) have proposed an equation which describes the influence of current on the change of wave spectrum in water of finite depth. (omitted)

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.4
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• pp.367-371
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• 2005

About 3,000 bacterial colonies with esterase activities were isolated from soil samples by enrichment culture and halo-size on Luria broth-tributyrin (LT) plates. The colonies were assayed for esterase activity in microtiter plates using enantiomerically pure (R)- and (S)-2-phenylbutyric acid resorufin ester (2PB-O-res) as substrates. Two enantioselective strains (JH2 and JH13) were selected by the ratio of initial rate of hydrolysis of enantiomerically pure (R)- and (S)-2-PB-O-res. When cell pellets were used, both strains showed high apparent enantioselectivity ($E_{app}>100$) for (R)-2PB-O-res and were identified as Exiguobacterium acetylicum. The JH13 strain showed high esterase activity on p-nitrophenyl acetate (pNPA), but showed low lipase activity on p-nitrophenyl palmitate (pNPP). The esterase was located in the soluble fraction of the cell extract. The crude intracellular enzyme preparation was stable at a pH range from 6.0 to 11.0.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.19-27
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• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.141-148
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• 2021

This paper presents a method to detect zebra-crossing using deep learning which combines SegNet and ResNet. For the blind, a safe crossing system is important to know exactly where the zebra-crossings are. Zebra-crossing detection by deep learning can be a good solution to this problem and robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors. These traditional methods have achieved significant results with relatively long processing times, and enhanced the zebra-crossing perception to a large extent. However, running all detectors jointly incurs a long latency and becomes computationally prohibitive on wearable embedded systems. In this paper, we propose a model for fast and stable segmentation of zebra-crossing from captured images. The model is improved based on a combination of SegNet and ResNet and consists of three steps. First, the input image is subsampled to extract image features and the convolutional neural network of ResNet is modified to make it the new encoder. Second, through the SegNet original up-sampling network, the abstract features are restored to the original image size. Finally, the method classifies all pixels and calculates the accuracy of each pixel. The experimental results prove the efficiency of the modified semantic segmentation algorithm with a relatively high computing speed.

• Proceedings of the Korean Ceranic Society Conference
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• 1986.12a
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• pp.185-201
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• 1986

• Korean Journal of Breeding Science
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• v.41 no.4
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• pp.579-582
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• 2009

A new red leaf lettuce (Lactuca sativa L.) cultivar, "Mihong" which has late bolting and deep red leaf color was developed from a cross of "Danhongongchukmyeon" (deep red but early bolting and low yield) and "Hajicheongchukmyeon" (late bolting and high yield). The selection and breeding of advanced lines were done by pedigree method during 2000-2006. The advanced lines were evaluated for yield and adaptability in Korea (Gangwon, Kyunggi, Chungbuk, Kyungnam, Jeonnam, Jeonbuk, and Jeju) from 2007 to 2008. The "Mihong" has gray seed color, traverse elliptic leaves and deep red color. Compared to "Dukseomjeokchukmyeon", marketable yield of "Mihong" was higher by 2% (at 291 g per plant) as a 17.3 ton/ha, but with particularly improved good expression of deep red leaf color in high temperature cultivation in the field. The shelf-life of "Mihong" was three weeks longer than "Dukseomjeokchukmyeon" at $4^{\circ}C$. The amount of anthocyanin content of "Mihong" were higher than with "Dukseomjeokchukmyeon" at 28.9 mg/100 g. Especially the degree of leaf hardness in "Mihong" showed $26.9kg/cm^2$ compared to "Dukseomjeokchukmyeon". therefore, its taste is better, more crispy, and sweeter than "Dukseomjeokchukmyeon" This new cultivar "Mihong" can be cultivated in all year around even if summer time cultivation.

• Natural Product Sciences
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• v.19 no.4
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• pp.303-310
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• 2013

Velvet deer antler (VDA) is well known oriental medicine claimed to have tonic activities as improving bone mineral density (BMD), immune-enhancing, rejuvenating and many other medicinal activities. Ossified deer antler (ODA) is bony product produced by over-calcification of deer antler due to late harvesting. The extraction efficiency of ODA by conventional boiling in water must be very poor due to bony nature, hence the reputations for the medicinal efficacies of ODA has been highly under-evaluated compared to that of VDA without any experimental evidences. Employing our new efficient water extraction process ($135^{\circ}C$), the extracts of ODA and VDA were analysed to compare the contents of bioactive components and the potencies of pharmacological activities. The results showed that; 1) The $135^{\circ}C$ extraction (autoclaving) of ODA gave highly increased amount of biomass, 120% more than the conventional extraction by 100-boiling, whereas the same treatment for VDA showed only 15% increased amount of biomass. 2) Feeding the ODA- or VDA-extracts to oophorectomized rats showed very potent BMD-recovering activity. 3) During the ossification of deer antler, the total collagen content was found to be increased by addition of type-1 to pre-existing type-2 collagen, but not replacement of type-2 to type-1 collagen. High titer of peptide hormones like growth hormone and IGF-1 were detected in the ODA- and VDA-extracts and also in the serum of ODA- or VDA-treated oophorectomized animals dose-dependently. Present experimental data will give a conclusion that folkloric poor reputations on ODA must be concerned only with poor extraction efficiency of conventional $100^{\circ}C$ water extraction and not based on the composition of bioactive substances of ODA.

• Molecules and Cells
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• v.44 no.2
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• pp.88-100
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• 2021

Anoctamin 6/TMEM16F (ANO6) is a dual-function protein with Ca2+-activated ion channel and Ca2+-activated phospholipid scramblase activities, requiring a high intracellular Ca2+ concentration (e.g., half-maximal effective Ca2+ concentration [EC50] of [Ca2+]i > 10 μM), and strong and sustained depolarization above 0 mV. Structural comparison with Anoctamin 1/TMEM16A (ANO1), a canonical Ca2+-activated chloride channel exhibiting higher Ca2+ sensitivity (EC50 of 1 μM) than ANO6, suggested that a homologous Ca2+-transferring site in the N-terminal domain (Nt) might be responsible for the differential Ca2+ sensitivity and kinetics of activation between ANO6 and ANO1. To elucidate the role of the putative Ca2+-transferring reservoir in the Nt (Nt-CaRes), we constructed an ANO6-1-6 chimera in which Nt-CaRes was replaced with the corresponding domain of ANO1. ANO6-1-6 showed higher sensitivity to Ca2+ than ANO6. However, neither the speed of activation nor the voltage-dependence differed between ANO6 and ANO6-1-6. Molecular dynamics simulation revealed a reduced Ca2+ interaction with Nt-CaRes in ANO6 than ANO6-1-6. Moreover, mutations on potentially Ca2+-interacting acidic amino acids in ANO6 Nt-CaRes resulted in reduced Ca2+ sensitivity, implying direct interactions of Ca2+ with these residues. Based on these results, we cautiously suggest that the net charge of Nt-CaRes is responsible for the difference in Ca2+ sensitivity between ANO1 and ANO6.