• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.425-429
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• 2014

Half-metallic nanostructure is highly applicable in the field of Spintronics and electronic device technology. We examine the electronic properties of a ferrocene-based nanowire as a possible candidate for a half-metallic nanostructure using VASP and SIESTA. Ferrocene-based nanowire shows high stability in both binding energy simulation and molecular dynamics (MD) simulation. The density of states (DOS) and the projected DOS of the ferrocene-based nanowire indicate that one-dimensional clustering of ferrocene molecules can be explained because of p-d orbital hybridization between iron and carbon. Half-metallic property and energy dispersion at the Fermi level due to one-dimensional structure is also observed from the DOS results.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of KIISE
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• v.45 no.3
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• pp.294-302
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• 2018

With the emergence of the new service industry due to the development of information and communication technology, cyber space risks such as personal information infringement and industrial confidentiality leakage have diversified, and the security problem has emerged as a critical issue. In this paper, we propose a behavior-based anomaly detection method that is suitable for real-time and large-volume data analysis technology. We show that the proposed detection method is superior to existing signature security countermeasures that are based on large-capacity user log data according to in-company personal information abuse and internal information leakage. As the proposed behavior-based anomaly detection method requires a technique for processing large amounts of data, a real-time search engine is used, called Elasticsearch, which is based on an inverted index. In addition, statistical based frequency analysis and preprocessing were performed for data analysis, and the DBSCAN algorithm, which is a density based clustering method, was applied to classify abnormal data with an example for easy analysis through visualization. Unlike the existing anomaly detection system, the proposed behavior-based anomaly detection technique is promising as it enables anomaly detection analysis without the need to set the threshold value separately, and was proposed from a statistical perspective.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.11-20
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• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.373-379
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• 2019

In this study, image-based flock monitoring and density evaluation were conducted for broiler chicks welfare. Image data were captured by using a mono camera and region of broiler chicks in the image was detected using converting to HSV color model, thresholding, and clustering with filtering. The results show that region detection was performed with 5% relative error and 0.81 IoU on average. The detected region was corrected to the actual region by projection into ground using coordinate transformation between camera and real-world. The flock density of broiler chicks was estimated using the corrected actual region, and it was observed with an average of 80%. The developed algorithm can be applied to the broiler chicks house through enhancing accuracy of region detection and low-cost system configuration.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.252-253
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• 2023

Container terminal automation might offer many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A robust lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure lane positioning robustness. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions and tested the robustness of the lane detection method with stochastic noise.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2273-2286
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• 2000

In order to acquire the precise and fast response for an analytical query, proper selection of the views to materialize in data warehouse is very crucial. In traditional view selection algorithms, the whole relations are considered to be selected as materialized views. However, materializing the whole relations rather than a part of relations results in much worse performance in terms of time and space cost. Therefore, we present an improved algorithm for selection of views to materialize using clustering method to overcome the problem resulted from conventional view selection algorithms. In the presented algorithm, ASVMRT(Algorithm for Selection of Views to daterialize using Iteduced Table). we first generate reduced tables in clata warehouse using automatic clustering based on attrihute-values density, then we consider the combination of reduced tables as materialized views instead of the combination of the original hase relations. For the justification of the proposecl algorithm. we show the experimental results in which both time and space cost are approximately 1.8 times better than the conventional algorithms.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.187-201
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• 2005

With the trend of urban polynucleation, the issue of detecting economic clusters or urban employment centers has been considered as crucial. However, the prior researches had some limitations in detecting economic clusters in the empirical analysis: i.e. inherent inefficiency of density-based clustering methods, difficulty in detecting linear types of spatial clusters and lacks of consideration of economic significance. The purpose of this paper is to propose the clustering method with the procedure of testing statistical and economic significance named as VCEC (Variable Clumping method for Economic Clusters) and to apply it to a case analysis of Erie County, New York, in order to test its validity. By applying a search radius and a total employment as an economic threshold, 'the both statistically and economically significant clusters' were detected in the Erie County, and proved to be efficient.

• Annual Conference of KIPS
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• 2000.04a
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• pp.28-35
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• 2000

In order to acquire the precise and fast response for an analytical query, proper selection of the views to materialize in data warehouse is very crucial. In traditional algorithms, the whole relation is considered to be selected as materialized views. However, materializing the whole relation rather than a part of relation results in much worse performance in terms of time and space cost. Therefore, we present a new algorithm for selection of views to materialize using clustering method in order to improve the performance of data warehouse including this problem. In the presented algorithm, ASVMR(Algorithm for Selection of Views to Materialize using Reduced table), we first generate reduced tables in data warehouse using automatic clustering based on attribute-values density, then we consider the combination of reduced tables as materialized views instead of the combination of the original base relations. We also show the experimental results in which both time and space cost are approximately 1.8 times better than the conventional algorithms.