• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.513-525
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• 2017

In the estimation of the exploitable carrying capacity (ECC) in the Korean water of the East China Sea, two approaches, which are the ecosystem modeling method (EMM) and the holistic production method (HPM), were applied. The EMM is accomplished by Ecopath with Ecosim model using a number of ecological data and fishery catch for each species group, which was categorized by a self-organizing mapping (SOM) based on eight biological characteristics of species. In this method, the converged value during the Ecosim simulation by setting the instantaneous rate of fishing mortality (F) as zero was estimated as the ECC of each group. The HPM is to use surplus production models for estimateing ECC. The ECC estimates were 4.6 and 5.1 million mt (mmt) from EMM and HPM, respectiverly. The estimate from the EMM has a considerable uncertainty due to the lack of confidence in input ecological parameters, especially production/biomass ratio (P/B) and consumption/biomass ratio (Q/B). However, ECC from the HPM was estimated on the basis of relatively fewer assumptions and long time-series fishery data as input, so the estimate from the HPM is regarded as more reasonable estimate of ECC, although the ECC estimate could be considerd as a preliminary one. The quality of input data should be improved for the future study of the ECC to obtain more reliable estimate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.421-430
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Improved Seeded Region Growing (ISRG) and Region merging. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained multi-spectral edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying ISRG to consider spectral and edge information. Finally the region merging process, integrating region texture and spectral information, was carried out to get the final segmentation result. The accuracy assesment was done using the unsupervised objective evaluation method for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.179-186
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• 2018

The number of devices connect to the internet is rapidly increasing with the advent of the IoT(Internet of Things). The IoT has improved the convenience of life. However, it makes security issues such as privacy violations. Therefore cybersecurity is the most important issue to be discussed nowadays. Especially, various protocols are used for same purpose due to rapidly increase of IoT market. To deal with this security threat noble vulnerability analysis is needed. In this paper, we contribute to the IoT security by proposing a new randomness-based test case evaluation methodology using variance and entropy. The test case evaluation method proposed in this paper can evaluate the test cases at a high speed regardless of the test set size, unlike the traditional technique.

• Polymer(Korea)
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• v.24 no.3
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• pp.374-381
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• 2000

Composites of polypropylene (PP) and organically modified montmorillonite (org-MMT) were prepared by melt mixing in an intensive mixer. Three grades of PP's having different melt viscosities were employed to investigate the dispersion characteristics of the composites with various org-MMT's. Depending on the matrix viscosity and nature of the interlayer in org-MMT significant variations of the phase structure were found. Under the constant mixing condition and matrix viscosity, intercalation of PP chains into the interlayer of org-MMT was possible when initial interlayer distance and packing density were maintained in the optimum range; by which the loss in entropy associated with the confinement of polymer chains was compensated. The state of org-MMT particle dispersion was improved by increasing the matrix viscosity only in the case that dispersed phase is suitable for intercalation process thermodynamically, otherwise little variation was occurred regardless of the matrix viscosity. Due to the lack of specific interaction between PP and erg-MMT considered here, although the intercalation was possible for an appropriate org-MMT, the composites revealed unstable phase structure upon increasing the mixing time, which was characterized by agglomeration of the org-MMT domains.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.5
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• pp.733-742
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• 2010

In this study, optimization was performed to improve the conventional liquefaction process of offshore plants, such as a LNG-FPSO(Liquefied Natural Gas-Floating, Production, Storage, and Offloading unit) by maximizing the energy efficiency of the process. The major equipments of the liquefaction process are compressors, expanders, and heat exchangers. These are connected by stream which has some thermodynamic properties, such as the temperature, pressure, enthalpy or specific volume, and entropy. For this, a process design problem for the liquefaction process of offshore plants was mathematically formulated as an optimization problem. The minimization of the total energy requirement of the liquefaction process was used as an objective function. Governing equations and other equations derived from thermodynamic laws acted as constraints. To solve this problem, the sequential quadratic programming(SQP) method was used. To evaluate the proposed method in this study, it was applied to the natural gas liquefaction process of the LNG-FPSO. The result showed that the proposed method could present the improved liquefaction process minimizing the total energy requirement as compared to conventional process.

• Current Optics and Photonics
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• v.4 no.4
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• pp.286-292
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• 2020

Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.60-69
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• 2019

In this paper, we deal with forward-looking imaging, and focus on forward-looking synthetic inverse scattering imaging for a vehicle with curved motion. For image formation, time domain correlation(TDC) is used and a 2D image of the ground in front of the vehicle is generated. Because TDC is a technique that implements matched filtering for a space-variant system, it is robust to Gaussian additive noise of measurements. Furthermore, comparison and analysis between images from linear motion and curved motion show that the resolution of the image is improved; however, the entropy of the image is increased owing to curved motion.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1375-1385
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• 2022

In this paper, we propose a semi-supervised domain adaptation solution to deal with practical face recognition (FR) scenarios where a single face image for each target identity (to be recognized) is only available in the training phase. Main goal of the proposed method is to reduce the discrepancy between the target and the source domain face images, which ultimately improves FR performances. The proposed method is based on the Domain Adatation network (DAN) using an MMD loss function to reduce the discrepancy between domains. In order to train more effectively, we develop a novel loss function learning strategy in which MMD loss and cross-entropy loss functions are adopted by using different weights according to the progress of each epoch during the learning. The proposed weight adoptation focuses on the training of the source domain in the initial learning phase to learn facial feature information such as eyes, nose, and mouth. After the initial learning is completed, the resulting feature information is used to training a deep network using the target domain images. To evaluate the effectiveness of the proposed method, FR performances were evaluated with pretrained model trained only with CASIA-webface (source images) and fine-tuned model trained only with FERET's gallery (target images) under the same FR scenarios. The experimental results showed that the proposed semi-supervised domain adaptation can be improved by 24.78% compared to the pre-trained model and 28.42% compared to the fine-tuned model. In addition, the proposed method outperformed other state-of-the-arts domain adaptation approaches by 9.41%.

• Journal of Soil and Groundwater Environment
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• v.10 no.5
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• pp.52-60
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• 2005

The conceptual model of under-sampled study area will include a great amount of uncertainty. In this study, we investigate the applicability of Markov chain model in a spatial domain as a tool for minimizing the uncertainty arose from the lack of data. A new formulation is developed to generalize the previous two-dimensional coupled Markov chain model, which has more versatility to fit any computational sequence. Furthermore, the computational algorithm is improved to utilize more conditioning information and reduce the artifacts, such as the artificial parcel inclination, caused by sequential computation. A generalized 20 coupled Markov chain (GCMC) is tested through applying a hypothetical soil map to evaluate the appropriateness as a substituting model for conventional geostatistical models. Comparing to sequential indicator model (SIS), the simulation results from GCMC shows lower entropy at the boundaries of indicators which is closer to real soil maps. For under-sampled indicators, however, GCMC under-estimates the presence of the indicators, which is a common aspect of all other geostatistical models. To improve this under-estimation, further study on data fusion (or assimilation) inclusion in the GCMC is required.