• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1017-1020
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• 1993

This paper describes a neo fuzzy neuron which was produced by a fusion of fuzzy logic and neuroscience. Some learning algorithms are presented. The guarantee for the global minimum on the error-weight space is proved by a reduction to absurdity. Enhanced is that the learning speed of the neo fuzzy neuron exceeds 100,000 times of that of conventional multi-layer neural networks.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.273-296
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• 2013

The purpose of this study is to examine the data coverage and citation threshold for analyzing SPLC(Search Path Link Count) as a main path of a historiograph of a certain topic in order to provide 'core' papers of global research trends to a researcher affiliated with a local R&D institution. 5 datasets were constructed by retrieving and collecting 2,318 articles on RGB LED on Web of Science published from 1990-2013 and 20,109 articles which cited these original 2,318. The SPLC analysis was performed on each dataset by increasing the threshold of citation counts, and the changes and resilience of the 28 extraced networks were compared. The results of user feedback on 198 unique core papers from 28 SPLC networks received from LED researchers affiliated with a Korean government-sponsored research institution were also analyzed. As a result, it is found that the nodes in each SPLC network in each dataset were differentiated by the citation counts, while the changes in the structure of SPLC networks were slight after the networks' citation counts were set at 40. Additionally, the user feedback showed that personalized research interest generally matched to the global research trends identified by the SPLC analysis.

• Journal of Veterinary Clinics
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• v.33 no.5
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• pp.278-285
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• 2016

Epidemiological studies have shown the association between transportation of live animals and the potential transmission of infectious disease between premises. This finding was also observed in the 2014-2015 foot-and-mouth disease (FMD) outbreak in Korea. Furthermore, slaughterhouses played a key role in the global spread of the FMD virus during the epidemic. In this context, in-depth knowledge of the structure of direct and indirect contact between slaughterhouses is paramount for understanding the dynamics of FMD transmission. But the social network structure of vehicle movements to slaughterhouses in Korea remains unclear. Hence, the aim of this study was to configure a social network topology of vehicle movements between slaughterhouses for a better understanding of how they are potentially connected, and to explore whether FMD outbreaks can be explained by the network properties constructed in the study. We created five monthly directed networks based on the frequency and chronology of on- and off-slaughterhouse vehicle movements. For the monthly network, a node represented a slaughterhouse, and an edge (or link) denoted vehicle movement between two slaughterhouses. Movement data were retrieved from the national Korean Animal Health Integrated System (KAHIS) database, which tracks the routes of individual vehicle movements using a global positioning system (GPS). Electronic registration of livestock movements has been a mandatory requirement since 2013 to ensure traceability of such movements. For each of the five studied networks, the network structures were characterized by small-world properties, with a short mean distance, a high clustering coefficient, and a short diameter. In addition, a strongly connected component was observed in each of the created networks, and this giant component included 94.4% to 100% of all network nodes. The characteristic hub-and-spoke type of structure was not identified. Such a structural vulnerability in the network suggests that once an infectious disease (such as FMD) is introduced in a random slaughterhouse within the cohesive component, it can spread to every other slaughterhouse in the component. From an epidemiological perspective, for disease management, empirically derived small-world networks could inform decision-makers on the higher potential for a large FMD epidemic within the livestock industry, and could provide insights into the rapid-transmission dynamics of the disease across long distances, despite a standstill of animal movements during the epidemic, given a single incursion of infection in any slaughterhouse in the country.

• Journal of Korea Port Economic Association
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• v.31 no.2
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• pp.69-83
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• 2015

This study proposes effective overseas market entry strategies that could allow Korean logistics companies to develop international capabilities and become global firms by adapting to environmental changes in global logistics. First, it reviews the overseas networks of Korean international freight forwarders and the recent trends in the global logistics market. Then, it surveys the conditions of two groups of freight forwarders, namely partnerships and subsidiaries, which are categorized according to type of entry into foreign markets. These companies' networks are concentrated in East and Southeast Asia regardless of network type. As a result, the ability for partnerships to network is higher than that of subsidiaries. However, subsidiaries are small in number and located in various regions because of their initial stage, which is why their businesses depend on the price competitiveness of the parent company. The satisfaction and performance of subsidiaries are both generally low according to the presented findings. In addition, the successful strategies of international freight forwarders include following operations, specializing their logistics services, building collaborations among small and medium-sized companies, recruiting and training professional human resources in international logistics, and entering markets together with their customers. Overall, this study highlights the importance of measuring and evaluating objectively the level and performance of overseas networking through a survey about the internationalization of Korean freight forwarder companies. To conclude, this study is considered to contribute to raising their global competitiveness by suggesting strategies derived from the survey findings and SWOT analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.379-387
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• 2013

In large-scale wireless sensor networks, tremendous amount of dummy packets is usually accompanied by keeping location privacy of the communication source and destination against global eavesdropping. In our earlier work we designed a location privacy routing protocol, ELPR(End-node Location Privacy Routing) in which the generation of dummy packets at each idle time-slot while transferring data packets are restricted to only the nodes within certain areas of encompassing the source and destination, respectively. In this paper, it is given that ELPR provides various degrees of location privacy while PCM(Periodic Collection Method) allows the only fixed level. Simulation results show that as the number of nodes or data packets increases ELPR permits in terms of the number of generated packets more cost-effective location privacy than PCM.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.49-56
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• 2011

One of the most important VANET applications is providing active safety by broadcasting emergency messages. In order to prevent broadcast storm of flooding-based broadcasting scheme in which any node receiving message will rebroadcast, the existing protocols use the different methods to limit the number of relay nodes. Nevertheless, the existing protocols have low delivery ratio with high traffic density and cause message overhead. Currently, the dramatic increase in the number of vehicles equipped with Global Positioning System (GPS) and onboard radar created new application scenarios that were not feasible before. Consequently, we proposed a broadcasting protocol that selects relay node by using GPS-based position information and detecting neighboring vehicles with the help of onboard radar to. Simulation results show that our proposed protocol has better performance than the existing schemes.

• STI Policy Review
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• v.6 no.1
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• pp.85-110
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• 2015

Science and technology diplomacy has become an important policy agenda because of its diplomatic utility and enhancing of international science networks. However, different countries possess different objectives and governance of S&T diplomacy. In this context, this paper seeks to answer the following questions: what are the similarities and differences of S&T diplomacy in countries and what shapes these characteristics? To answer these questions, this paper conducts a comparative case study with five countries - Switzerland, Germany, Japan, the United Kingdom, and the United States - whose S&T diplomatic programs are highly recognized and benchmarked by other countries. A useful typology is devised to conduct a systematic comparison. For S&T diplomatic objectives, this paper suggests five types by elaborating concepts from the previous literature: access diplomacy, promotion diplomacy, public aid diplomacy, functional diplomacy, and global leadership diplomacy. Also, in terms of a governance model for S&T diplomacy, three models - a sciencecentered model, a science-outsourcing model and a top-down coordinating model - are suggested based on leadership organization. This paper reveals the different characteristics of the selected countries in S&T diplomacy. While the selected countries pursue almost every type of S&T diplomatic objective, the US and the UK tend to conduct influence-based diplomacy more than other countries do. In addition, different countries each have unique governance models for S&T diplomacy. While more research is necessary for vigorously testing the causes of different objectives and their relationship with governance models, this paper suggests more general policy implications throughout. The strength of the country's S&T base is fundamentally important for the success of S&T diplomacy. However, domestic S&T assets need to be transferred to its diplomatic capabilities. In this sense, the appropriate governance that fits best with the country's S&T mission should be established, while S&T communities should increasingly play a leadership role in evolving global S&T networks.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.930-940
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• 2008

This paper proposes a prototype realization of multi-view HD video transport system with synchronized multiplexing over IP networks. The proposed synchronized multiplexing considers the synchronization during video acquisition and the multiplexing for the interactive view-selection during transport. For the synchronized acquisition from multiple HDV camcorders through IEEE 1394 interface, we estimate the timeline differences among MPEG-2 compressed video streams by using global time of network between the cameras and a server and correct timelines of video streams by changing the time stamp of the MPEG-2 system stream. Also, we multiplex a selected number of acquired HD views at the MPEG-2 TS (transport stream) level for the interactive view-selection during transport. Thus, with the proposed synchronized multiplexing scheme, we can display synchronized HD view.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3567-3582
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• 2020

Human activities are often affected by weather conditions. Automatic weather recognition is meaningful to traffic alerting, driving assistance, and intelligent traffic. With the boost of deep learning and AI, deep convolutional neural networks (CNN) are utilized to identify weather situations. In this paper, a three-channel convolutional neural network (3C-CNN) model is proposed on the basis of ResNet50.The model extracts global weather features from the whole image through the ResNet50 branch, and extracts the sky and ground features from the top and bottom regions by two CNN5 branches. Then the global features and the local features are merged by the Concat function. Finally, the weather image is classified by Softmax classifier and the identification result is output. In addition, a medium-scale dataset containing 6,185 outdoor weather images named WeatherDataset-6 is established. 3C-CNN is used to train and test both on the Two-class Weather Images and WeatherDataset-6. The experimental results show that 3C-CNN achieves best on both datasets, with the average recognition accuracy up to 94.35% and 95.81% respectively, which is superior to other classic convolutional neural networks such as AlexNet, VGG16, and ResNet50. It is prospected that our method can also work well for images taken at night with further improvement.