• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.551-562
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• 2009

In this paper, I would present a using method for The Fishing Operation Information(FOI) of National Federation of Fisheries Cooperatives(NFFC) through the availabilities analysis and put out the similarities by the section of the sea through classifying characteristics of fishing patterns by their locations. As a result, although the catch of FOI is nothing more than 33% level to National Fishery Production Statistics(NFPS), FOI data is useful in understanding the patterns of fishing operation by the location because both patterns and correlation were very similar in the usability analysis, comparing the FOI data with NFPS. So I classified optimal clusters for catch, the number of fishing days and the number of fishing vessels through 2 step cluster analysis by the big marine zone and divided fishing patterns.

• The Korean Journal of Food And Nutrition
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• v.30 no.4
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• pp.719-727
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• 2017

The objective of this study was to investigate the effect of alcohol content in Makgeolli made with Chinese matrimony vine (M-CMV) on the sensory profile and consumer acceptability. The M-CMVs were prepared with 6, 7, 8, and 9% alcohol content. Descriptive analysis of M-CMV was performed with six trained panelists. Thirteen attributes were generated and their intensities were alcohol content dependent. The consumer acceptance test was conducted with 57 consumers. M-CMV samples with 7% alcohol had the highest acceptance rate (5.8) followed by 6% M-CMV (5.6). Commercial rice Makgeolli (CRM) had the lowest consumer acceptance. Consumers were divided into two groups by clustering analysis. The majority of consumers (n=38) preferred M-CMV and did not like the commercial sample. Only 19 consumers indicated high acceptance ratings for CRM. However, these consumers also preferred 6 and 7% M-CMV. Partial least-squares regression analysis revealed moderate attribute intensities were related to greater consumer acceptability. The optimal alcohol content for the greatest consumer acceptance predicted by linear regression was 6.7%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.331-348
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• 2019

Understanding the tumor heterogeneity due to differences in the growth pattern of metastatic tumors and rate of change is important for understanding the sensitivity of tumor cells to drugs and finding appropriate therapies. It is often possible to test for differences in population means using t-test or ANOVA when the group of N samples is distinct. However, these statistical methods can not be used unless the groups are distinguished as the data covered in this paper. Statistical methods have been studied to test heterogeneity between samples. The minimum combination t-test method is one of them. In this paper, we propose a maximum combinatorial t-test method that takes into account combinations that bisect data at different ratios. Also we propose a method based on the idea that examining the heterogeneity of a sample is equivalent to testing whether the number of optimal clusters is one in the cluster analysis. We verified that the proposed methods, maximum combination t-test method and gap statistic, have better type-I error and power than the previously proposed method based on simulation study and obtained the results through real data analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.379-384
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• 2022

In a wireless sensor network, a large number of sensor nodes are deployed in an environment where direct access is difficult. It is difficult to supply power, such as replacing the battery or recharging it. It is very important to use the energy with the sensor node. Therefore, an important consideration to increase the lifetime of the network is to minimize the energy consumption of each sensor node. If the energy of the wireless sensor node is exhausted and discharged, it cannot function as a sensor node. Therefore, it is a method proposed in various protocols to minimize the energy consumption of nodes and maintain the network for a long time. We consider the center point and residual energy of the cluster, and the plot point and K-means (WSN suggests optimal clustering). We want to evaluate the performance of the KOCED protocol. We compare protocols to which the K-means algorithm, one of the latest machine learning methods, is applied, and present performance evaluation factors.

• Journal of Korean Society on Water Environment
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• v.39 no.4
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• pp.316-328
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• 2023

In this study, the characteristics of nonpoint pollutant outflow and contribution rate of pollution in Songya-stream mainstream and tributaries were analyzed. Further, water pollution management and improvement measures for pollution-oriented rivers were proposed. An on-site investigation was conducted to determine the inflow of major pollutants into the basin, and it was found that pollutants generated from agricultural land and livestock facilities flowed into the river, resulting in a high concentration of turbid water. Based on the analysis results of the pollution load data calculated through actual measurement monitoring (flow and water quality) and the occurrence and emission load data calculated using the national pollution source survey data, the S3 and S6 were selected as the concerned pollution tributaries in the Songya-stream basin. Results of cluster analysis using Pearson correlation coefficient evaluation and Density based spatial clustering of applications with noise (DBSCAN) technique showed that the S3 and S6 were most consistent with the C2 cluster (a cluster of Songya-stream mainstream owned area) corresponding to the mainstream of Songya-stream. The analysis results of the major pollutants in the concerned pollution tributaries showed that livestock and land pollutants were the major pollutants. Consequently, optimal management techniques such as fertilizer management, water gate management in paddy, vegetated filter strip and livestock manure public treatment were proposed to reduce livestock and land pollutants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Journal of Animal Reproduction and Biotechnology
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• v.39 no.3
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• pp.212-219
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• 2024

Background: This study explores the potential of discarded male layer embryos as a sustainable and non-GMO cell source for cultivated chicken meat production. The research aims to identify efficient methods for isolating muscle progenitor cells (MPCs) with high proliferative potential by conducting transcriptome analysis on thigh muscle tissues from both male and female chick embryos. Methods: Transcriptome analysis was performed on the thigh muscle tissues of male and female chick embryos, aged 12-13 days, (n = 4 each), to investigate the gene expression profiles and identify strategies for efficiently isolating MPCs. This approach aims to pinpoint techniques that would allow for the selection of MPCs with optimal growth and proliferation capabilities. Results: Using heatmap, hierarchical clustering, and multidimensional scaling (MDS), we found no significant sex-based differences in gene expression, except for the overexpression of the female-specific gene LIPBLL. The expression of muscle stem cell factors, including PAX3, PAX7, and other myogenic regulatory genes, showed no significant variation. However, to recover MPC-rich cells isolated from male thigh muscle, we found that by the pre-plating 7 stage, myogenesis-related genes, MYHs and MUSTN1 were minimally expressed, while the cell cycle arrest gene CDKN1A sharply increased. Conclusions: Our findings suggest that simple cell isolation directly from tissue is a more scalable and efficient approach for cultivated meat production, compared to labor-intensive pre-plating methods, making it a viable solution for sustainable research and resource recycling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.36-43
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• 2005

This paper suggests a method to effectively apply an application model of fuzzy-neural network to the optimal load distribution algorithm, considering the complication and non-linearity of the web server environment. We use the clustering web server in the linux system and it consists of a load balancer that distributes the network loads and some of real servers that processes the load and responses to the client. The previous works considered only with the scrappy decision information such as the connections. That is, since the distribution algorithm depends on the input of the whole network throughput, it was proved inefficient in terms of performance improvement of the web server. With the proposed algorithm, it monitors the whole states of both network input and output. Then, it infers CPU and memory states of each real server and effectively distributes the requests of the clients. In this paper, the proposed model is compared with the previous method through simulations and we analysis the results to develop the optimal and intelligent load balancing model.