• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.35-40
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• 2015

This paper presents the optimal network configuration for electric stations using HOMER software. For the given data such as annual average wind speed and grid costs, this software calculates the NPC(Net Present Cost), operating cost and COE(Cost of Energy). Based on these simulation results, it is possible to find the optimal network configuration for electric stations depending on the grid cost and average wind speed. When the rising grid cost is considered, it is essential to use grid and renewable energy together. Depending on the increase of the grid cost, NPC of the configuration using renewable energy and grid can be gradually getting smaller than NPC of the configuration using only grid.

• Korean Journal Plant Pathology
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• v.14 no.3
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• pp.229-239
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• 1998

Antibodies were raised against fusion proteins of the N-terminus and a region containing the GDNQ (Gly-Asp-Asn-Gln) polymerase motif of the L (polymerase) protein of sonchus yellow net virus (SYNV). Immunoblot analyses using these antibodies revealed the presence of the L protein in purified SYNV preparations and in nuclear extracts from infected tobacco. The serological analyses and detection in a polyacrylamide gels suggested that the L protein is present in at least a 20 fold lower abundance than the G, N, M1 and M2 proteins, and has size corresponding to a molecular weight of over 200 kDa as predicted from nucleotide sequence data. Electron microscopy with gold-labelled antibodies was used to localize the N, M2, and G proteins of SYNV in thin sections of infected tissue. When sections of SYNV-infected tissue were treated with antisera against total SYNV proteins and N protein, gold label could be detected in both the viroplasms and in virus particles. With the anti-M2 protein antiserum, the gold label was strongly localized in the viroplasms but only limited labelling of the virus particle sonly. Limited labelling of the L protein was observed in the viroplasms and the virus particles, presumably because of the low abundance of L protein in the tissues.

• Journal of fish pathology
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• v.22 no.2
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• pp.163-166
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• 2009

The ectoparasite ciliate Trichodina has been recorded from above 100 species of freshwater and marine fishes. In April 2009, we investigated the trichodia infection in 13 species of marine fishes captured by emplacement net and gill net in order to understand trichodina infection status among the natural fish populations along the Namhae-Gun coast area, the southen coast of Korea. Trichodina infection was observed in 10 out of 10 sea basses Lateolabrax japonicus, 2 out of 5 finespotted flounder Pleuronichthys cornutus and 1 out of a stone flounder Kareius bicoloratus. The sea bass infected with the parasite showed hypertrophy and exfoliation in gill epithelium. In addition, hyperplasia and lamellar fusion of gill filament were noticed among naturally affected sea bass.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.175-182
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• 2021

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world. Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment. It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances. In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images. In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently. We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3. These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET. The experiments were performed on our own dataset collected from hospitals. We also performed experiments of BreakHis, and ICML datasets to evaluate our model. The results produced by our model are very good as compared to previously reported results.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.737-745
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• 2023

This paper discusses a study on developing an artificial intelligence model to detect incidents of fires in distribution switchboard using thermal images. The objective of the research is to preprocess collected thermal images into suitable data for object detection models and design a model capable of determining the occurrence of fires within distribution panels. The study utilizes thermal image data from AI-HUB's industrial complex for training. Two CNN-based deep learning object detection algorithms, namely Faster R-CNN and RetinaNet, are employed to construct models. The paper compares and analyzes these two models, ultimately proposing the optimal model for the task.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.4
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• pp.29-42
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• 2017

As the "Guidelines for GHG Environmental Assessment" was revised, it pointed out that the developers should evaluate GHG sequestration and storage of the developing site. However, the current guidelines only taking into account the quantitative reduction lost within the development site, and did not consider the qualitative decrease in the carbon sequestration capacity of forest edge produced by developments. In order to assess the quantitative and qualitative effects of vegetation carbon uptake, the CASA-NPP model and satellite image spatial-temporal fusion were used to estimate the annual net primary production in 2005 and 2015. The development projects between 2006 and 2014 were examined for evaluate quantitative changes in development site and qualitative changes in surroundings by development types. The RMSE value of the satellite image fusion results is less than 0.1 and approaches 0, and the correlation coefficient is more than 0.6, which shows relatively high prediction accuracy. The NPP estimation results range from 0 to $1335.53g\;C/m^2$ year before development and from 0 to $1333.77g\;C/m^2$ year after development. As a result of analyzing NPP reduction amount within the development area by type of forest development, the difference is not significant by type of development but it shows the lowest change in the sports facilities development. It was also found that the vegetation was most affected by the edge vegetation of industrial development. This suggests that the industrial development causes additional development in the surrounding area and indirectly influences the carbon sequestration function of edge vegetaion due to the increase of the edge and influx of disturbed species. The NPP calculation method and results presented in this study can be applied to quantitative and qualitative impact assessment of before and after development, and it can be applied to policies related to greenhouse gas in environmental impact assessment.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• Asian Spine Journal
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• v.12 no.6
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• pp.987-991
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• 2018

Study Design: Cross sectional study. Purpose: To assess the quality of anterior cervical discectomy and fusion (ACDF) videos available on YouTube and identify factors associated with video quality. Overview of Literature: Patients commonly use the internet as a source of information regarding their surgeries. However, there is currently limited information regarding the quality of online videos about ACDF. Methods: A search was performed on YouTube using the phrase 'anterior cervical discectomy and fusion.' The Journal of the American Medical Association (JAMA), DISCERN, and Health on the Net (HON) systems were used to rate the first 50 videos obtained. Information about each video was collected, including number of views, duration since the video was posted, percentage positivity (defined as number of likes the video received, divided by the total number of likes or dislikes of that video), number of comments, and the author of the video. Relationships between video quality and these factors were investigated. Results: The average number of views for each video was 96,239. The most common videos were those published by surgeons and those containing patient testimonies. Overall, the video quality was poor, with mean scores of 1.78/5 using the DISCERN criteria, 1.63/4 using the JAMA criteria, and 1.96/8 using the HON criteria. Surgeon authors' videos scored higher than patient testimony videos when reviewed using the HON or JAMA systems. However, no other factors were found to be associated with video quality. Conclusions: The quality of ACDF videos on YouTube is low, with the majority of videos produced by unreliable sources. Therefore, these YouTube videos should not be recommended as patient education tools for ACDF.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.959-964
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• 2022

Lead is an important material, both for fusion or fission reactors. The cross sections of natural lead should be validated because lead is a main component of lithium-lead modules suggested for fusion power plants and it directly affects the crucial variable, tritium breeding ratio. The presented study discusses a validation of the lead transport libraries by dint of the activation of carefully selected activation samples. The high emission standard ²⁵²Cf neutron source was used as a neutron source for the presented validation experiment. In the irradiation setup, the samples were placed behind 5 and 10 cm of the lead material. Samples were measured using a gamma spectrometry to infer the reaction rate and compared with MCNP6 calculations using ENDF/B-VIII.0 lead cross sections. The experiment used validated IRDFF-II dosimetric reactions to validate lead cross sections, namely ¹⁹⁷Au(n, 2n)¹⁹⁶Au, ⁵⁸Ni(n,p)⁵⁸Co, ⁹³Nb(n, 2n)^92mNb, ¹¹⁵In(n,n')^115mIn, ¹¹⁵In(n,γ)^116mIn, ¹⁹⁷Au(n,γ)¹⁹⁸Au and ⁶³Cu(n,γ)⁶⁴Cu reactions. The threshold reactions agree reasonably with calculations; however, the experimental data suggests a higher thermal neutron flux behind lead bricks. The paper also suggests ²⁵²Cf isotropic source as a valuable tool for validation of some cross-sections important for fusion applications, i.e. reactions on structural materials, e.g. Cu, Pb, etc.