• Journal of Korea Port Economic Association
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• v.18 no.2
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• pp.109-133
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• 2002

The China entered World Trade Oganization(WTO) last year, thus opening its border to more - and freer - trade. With its foreign trade rapidly expanding and with economic growth continuing at a substantial -rate, China will be the largest container traffic generating country in the world. In the light of this potential trade bonanza, regional ports in North-East Asia strive to gain a competitive-edge. The Port of Busan, the world's third largest container port, wants to capture a significant share of the china's container cargoes. In this circumstance, development strategies of the Port of Busan are suggested as follows. First, to cope with increasing volumes, the New Busan Port on Gaduk island should be constructed without failure. Second, it is necessary to add modernized high-performance gantry cranes and to train crane operators' skill. Third, it needs to apply Dwell Time- Sliding Scale System for transshipment cargoes. Fourth, it needs to develop the EDI network in terminal areas or adjacent hub ports to exchange trustworthy and satisfactory informations Fifth, port authority -needs to enlarge designated Free Trade Zone to facilitate the free flow of cargoes. Sixth, the restoration of rail links between North and South Korea is abundantly clear. Thus it needs to enlarge railroad facilities in advance. Seventh, it needs to establish the Port Authority of Busan immediately. Finally, it needs to strengthen port sales and to open events like 'Marine Week 2001' regularly to attract potential canters or big shippers.

• Korean Journal of Optics and Photonics
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• v.32 no.3
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• pp.108-113
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• 2021

We propose and experimentally demonstrate a method of remote sound extraction using laser Doppler interferometry. The output frequency of a laser Doppler interferometer changes to be the same as the frequency of the acoustic wave from than object vibrated by the sound due to the Doppler effect. Based on this phenomenon, we measure the vibrational frequency of a remote target affected by a sound wave in real time, via laser Doppler interferometry. We track the peak frequency of the interferometer's output via appropriate signal processing, which confirms that the characteristics of the so detected wave are the same as that of the original sound source. We also confirm that the same method can retrieve the sound waves not only from remote sources of single tones, but from those of any sound.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.449-458
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• 2023

Artificial Intelligence is providing convenience in various fields using big data and deep learning technologies. However, deep learning technology is highly vulnerable to adversarial examples, which can cause misclassification of classification models. This study proposes a method to detect and purification various adversarial attacks using StarGAN. The proposed method trains a StarGAN model with added Categorical Entropy loss using adversarial examples generated by various attack methods to enable the Discriminator to detect adversarial examples and the Generator to purification them. Experimental results using the CIFAR-10 dataset showed an average detection performance of approximately 68.77%, an average purification performance of approximately 72.20%, and an average defense performance of approximately 93.11% derived from restoration and detection performance.

• Korean Journal of Ecology and Environment
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• v.54 no.2
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• pp.130-141
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• 2021

By reviewing previous literature, we analyzed the food sources of 20 out of 29 endangered fish species from freshwater ecosystems in South Korea. A total of 19 studies reported that food sources of 20 endangered fish species included 20 phyla, 31 classes, 58 orders, 116 families, and 154 genera. Arthropod, insecta, diptera, and chironomidae were the most fed animal food sources according to different resolution of taxa index on phylum, class, order and family. Similarity, bacillariophyta, bacillariophyceae, naviculales, and cymbellaceae were the most fed abundant plant sources. A larger number of fish species were reliant on animal food sources than plant food sources. 18 of the endangered fish preyed on arthropods, whereas only 6 species consumed bacillariophyta. To characterize the feeding groups of the 20 fish species, a hierarchical clustering analysis and non-metric multidimensional scaling analysis were conducted. The fish species were divided into two groups: 1) insectivores and 2) planktivores. A network analysis, which associated the link between endangered fishes and food sources, also revealed the same two groups. The highest hub score of food sources was for macroinvertebrates, including diptera (0.47), ephemeroptera (0.42), and trichoptera (0.38), based on the network analysis. Niche breadth was used to calculate the diversity of the food sources. Phoxinus phoxinus (0.57) showed thehighest food source diversity among the fish species, whereas Iksookimia pacifica (0.01) showed the lowest. This study will be utilized for the conservation and restoration of the endangered fish species.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.17-29
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• 2021

Recently, as social distancing have been raised due to the re-spread of COVID-19, the number of serial entrepreneurs who are closing their business is rapidly increasing. Learning from failure is a source of success, but business failure can result in psychological and economic losses and negative emotions of the serial entrepreneur. At this point, it is very important to find a way to recover the negative emotions caused by business failures of serial entrepreneurs. Recently, a strategic model has emerged to deal with the negative emotions of grief caused by business failures of serial entrepreneurs. This study identified the recovery factors from the grief of business failures of serial entrepreneurs and analyzed Shepherd's(2003) three areas: loss orientation, restoration orientation, and dual process. To this end, individual in-depth interviews were conducted with 12 small business serial entrepreneurs who challenged re-startup to identify the attributes of recovery factors that were not identified with quantitative data. As a result of the study, first, recovery factors were investigated in three areas: individual orientation, family orientation, and network orientation. It was found to help improve recovery in nine categories: self-esteem, persistence, personal competence, hobbies, self-confidence, family support, networks, religion, and social support. Second, recovery obstacle factors were investigated in three areas: psychological, economic, and environmental factors. Nine categories including family, health, social network, business partner, competitor, partner, fund, external environment, and government policy were found to persist negative emotions. Third, the emotional processing process for grief was investigated in three areas: loss orientation, restoration orientation, and dual process. Ten categories such as family, partner support, social member support, government support, hobbies, networks, change of business field, moving, third-party perspective, and meditation were confirmed to enhance rapid recovery in the emotional processing process for grief. The implications of this study are as follows. The process of recovering from the grief caused by business failures of serial entrepreneurs was attempted by a qualitative study. By extending the theory of Shepherd(2003), This study can be applied to help with recovery research. In addition, conceptual models and propositions for future empirical research were presented, which can be discussed in carious academic ways.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.351-375
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• 2018

This paper aims to i) introduce the characteristics of HF ocean radar and the major results and information produced by the radar networks in the Korean coasts to the readers, ii) make an up-to-date inventory of the existing radar systems, and iii) share the information related to the radar operating skill and the ocean current data application. The number of ocean radars has been showing a significant growth over the past 20 years, currently deploying more than 44 radars in the Korean coasts. Most of radars are in operation at the present time for the purposes related to the marine safety, tidal current forecast and understanding of ocean current dynamics, mainly depending on the mission of each organization operating radar network. We hope this overview paper may help expand the applicability of the ocean radar to fisheries, leisure activity on the sea, ocean resource management, oil spill response, coastal environment restoration, search and rescue, and vessel detection etc., beyond the level of understanding of tidal and ocean current dynamics. Additionally we hope this paper contributes further to the surveillance activity on our ocean territory by founding a national ocean radar network frame and to the domestic development of ocean radar system including signal processing technology.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.4
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• pp.743-758
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• 2014

It has widely been recognized that the EXPO 2012 Yeosu Korea was a succeeded mega event, according to, at least, the international media's attention and reports. This study analysed and compared the trends of the international media's reports on Yeosu in terms of before, preparing, during, and, after periods of the event, through a semantic network analysis. It was revealed that the images of Yeosu have dramatically been upgraded. The city of Yeosu, before the event, was a small port city of South Korea's southern part of peninsula. The city, after the nomination for the next host city of the exposition, was described to a city who had a full potentiality to host a world exposition, not a southern port city of South Korea. After the event was opened, Yeosu was a city of cutting-edge technology and cultural creativity, who had contributed to solve our humankind's pending ecological problems. Even after the events closed, Yeosu was continuously impressed as a ex-city of world exposition, a hub city of Asia trade, and a center for marine ecological restoration. It was suggested that extended monitoring, differentiated communication strategies, long-term planning, and professionalization of the staffs.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.59-81
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• 2020

The purpose of this study is to compare and analyze the trend of reading research in Korea's LIS Fields in the past 20 years, divided into the 2000s and 2010s, by establishing a keyword network. To achieve this purpose, keywords were extracted from 489 related articles in the four major journals in the LIS field sourced from the Korean Journal Citation Index (KCI) and then analyzed using NetMiner4. The results of the study were as follows: First, in the case of the 2000s, 'Public Library', 'Bibliotherapy', 'Reading Education', and 'School Library' showed high values of Frequency Analysis, Degree Centrality, and Betweenness Centrality. In the 2010s, 'Reading Education', 'School Library', 'Children', 'Adolescents', and 'Public Library' showed high values of the aforementioned measures. Second, in the 2000s, the establishment of library infrastructure for reading and reading education, the improvement of policies and systems, and reading research through the reading movement were actively conducted. In the 2010s, based on the work and research done in the 2000s, customized user reading studies and various detailed reading research were conducted. Third, to meet the demands of the times for the restoration of humanity with creativity and imagination in the Fourth Industrial Revolution, reading research and professional in-depth research should be conducted in various environments beyond public and school libraries and interdisciplinary research and active joint research between the field and academia are needed.