• Journal of Korea Foresty Energy
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• v.21 no.3
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• pp.28-33
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• 2002

This study was carried out to develop the technique for the production of high quality container seedlings of Comus controversa Hemsley. The seedlings were grown on the medium of peatmoss : perlite : vermiculite (1:1:1, v/v) in plastic net container and no-net plastic container as control for five months in the PE house. The seedlings grown in the plastic net container showed better root collar diameter growth, better development of long and fine roots, more increment of dry mass of roots and shoots than those grown in the no-net container. But the height growth of the seedlings in both container was similar. In particular the seedlings grown in plastic net container had no spiraling roots which were always observed in the control container seedlings. This result was induced by air-root pruning effect from the plastic net container.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.3
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• pp.181-189
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• 2019

Discarding juvenile fishes under girth 16 cm nearly equal to inner perimeter of codend mesh size collected by a cover net method in bottom trawl. The body size of the main five species (mackerel, horse mackerel, sea bream, melon seed and black throat seaperch) was measured for their body length, girth, weight, height and width and analyzed size selectivity. Frequency of penetrating fish as retention in a cover net was less than 40% of total number of juvenile discarding fish. The most of body length or girth of five species were significantly different between in the codend and in the cover net. The 50% selection girth in the cover net ranged 8-11 cm were smaller than those in the codend ranged 9-13 cm by the species respectively. The 50% selection body length was significantly related with the ratio of body height (H) by body width (W) both for in the codend or in the cover net while 50% selection girth was not significantly related with H/W. Furthermore 50% selection fish size by fish species between in the codend and in the cover net was not significantly different both in body length or girth. Therefore, the girth selectivity represented possibly as one unique value regarding fish body shape was considered as more useful method for multi-species catch in trawl.

• Ocean and Polar Research
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• v.43 no.1
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• pp.15-30
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• 2021

In this study, using 16 ORA-IP (Ocean Reanalysis Intercomparison Project) data, we investigated spatial and temporal changes of net surface heat flux in the East Asian seas and presented a new ensemble net surface heat flux index. The ensemble net surface heat flux index is produced considering the data distribution and the standard deviation of each ORA-IP. From the correlation analysis with air temperature averaged over the Korean Peninsula, ensemble net heat flux around the Korea Strait shows the highest correlation (0.731) with a 3 month time lag. For the correlation study regarding precipitation over the Korean Peninsula, it also shows significant correlation especially in winter and spring seasons. Similar results are also found in comparison with climate indices (AO, PDO, and NINO3.4), but ensemble net surface heat flux data in winter season reveals the strongest correlation patterns especially with winter temperature and spring precipitation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.1
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• pp.74-84
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• 2023

This paper is the result of a comparative analysis of crew members' workloads using the EMG and OWAS methods according to the fishing process before and after the improvement of the operating system of the experimental vessel, with the aim of improving operational efficiency and safety work of coastal improved stow net fishing vessels. The target crew members were three people with at least five years of experience on board the same fishing vessel as the experimental vessel. After improving the operating system, such as installing a power block crane and two capstans, change the location of the ball-roller, the time required for setting and hauling work decreased (p < .01), and the evaluation results by OWAS showed that the overall workload for setting and hauling net work decreased. The results of muscle activity analysis showed a decrease in lashing anchor work (p < .01) in the case of hauling net and in anchor dropping work in the case of setting net (p < .001). It is judged that the use of ball rollers in net handling has been reduced; consequently, work safety has been improved.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.327-340
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• 2023

Purpose - The purpose of this study is to examine the information effect of securities firm's net operating capital, securities firm's regulatory purposes capital, by analyzing the value relevance of net operating capital. Design/methodology/approach - This study was empirically analyzed using samples of domestic listed securities firms in the past 12 years, and multiple regression analysis and Vuong (1989) test were used together as specific research methods. Findings - First, it was found that the net operating capital of securities firms has an incremental value relevance that is added to basic accounting information (equity book value, profit or loss information). Second, after classifying equity capital on the books into net operating capital and the rest of equity capital, the value relevance of net operating capital was significantly higher than that of other equity capital. Research implications or Originality - The results of this study indicate that the level of regulatory capital in the securities industry can be used in the process of evaluating firm value in the capital market, and it is significant in that capital market evaluation can be stratified according to regulatory purposes.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.354-361
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• 2023

Most of spinal diseases are diagnosed based on the subjective judgment of a specialist, so numerous studies have been conducted to find objectivity by automating the diagnosis process using deep learning. In this paper, we propose a method that combines segmentation and feature extraction, which are frequently used techniques for diagnosing spinal diseases. Four models, U-Net, U-Net++, DeepLabv3+, and M-Net were trained and compared using 1000 X-ray images, and key-points were derived using Douglas-Peucker algorithms. For evaluation, Dice Similarity Coefficient(DSC), Intersection over Union(IoU), precision, recall, and area under precision-recall curve evaluation metrics were used and U-Net++ showed the best performance in all metrics with an average DSC of 0.9724. For the average Euclidean distance between estimated key-points and ground truth, U-Net was the best, followed by U-Net++. However the difference in average distance was about 0.1 pixels, which is not significant. The results suggest that it is possible to extract key-points based on segmentation and that it can be used to accurately diagnose various spinal diseases, including spondylolisthesis, with consistent criteria.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.71-76
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• 2023

In this paper, we propose a fuzzy U-Net, a fuzzy deep learning model that applies fuzzy logic to improve performance in image segmentation using deep learning. Fuzzy modules using fuzzy logic were combined with U-Net, a deep learning model that showed excellent performance in image segmentation, and various types of fuzzy modules were simulated. The fuzzy module of the proposed deep learning model learns intrinsic and complex rules between feature maps of images and corresponding segmentation results. To this end, the superiority of the proposed method was demonstrated by applying it to dental CBCT data. As a result of the simulation, it can be seen that the performance of the ADD-RELU fuzzy module structure of the model using the addition skip connection in the proposed fuzzy U-Net is 0.7928 for the test dataset and the best.

• Smart Media Journal
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• v.13 no.3
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• pp.27-35
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• 2024

This study deals with a method of combining image generation using Semi Supervised Learning based on GAN (Generative Adversarial Network) and image classification using ResNet50. Through this, a new approach was proposed to obtain more accurate and diverse results by integrating image generation and classification. The generator and discriminator are trained to distinguish generated images from actual images, and image classification is performed using ResNet50. In the experimental results, it was confirmed that the quality of the generated images changes depending on the epoch, and through this, we aim to improve the accuracy of industrial accident prediction. In addition, we would like to present an efficient method to improve the quality of image generation and increase the accuracy of image classification through the combination of GAN and ResNet50.