• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.12
• /
• pp.5073-5086
• /
• 2015

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.392-412
• /
• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• Journal of Information Technology Services
• /
• v.10 no.3
• /
• pp.203-212
• /
• 2011

In the every day life of the people, the Internet is widely used. Currently over 1.9 billion people have one or more email addresses and over 600 million people use the Facebook. People are collaborating via the Internet more and more. When people are collaborating through the Internet, the differences of the message delivery delay are the biggest problem that disturbs the collaborative work over the network. To solve the differences of the message delivery delay, we introduce the delay-gap method. An experimental code have been implemented and the efficiency of the delay-gap is presented through the results from the experiment that have many participants.

• Journal of The Korean Association of Information Education
• /
• v.12 no.2
• /
• pp.163-171
• /
• 2008

To cope actively in the knowledge based society, we deepen a specialty territory and magnify this by applying Information Communications Technology(ICT) effectively. It needs the ability that it will be able to solve the problem from daily life effectively. Accordingly Ministry of Education & Resource Development of Korea make an effort for ICT application education. In spite of this effort, digital divide problem is serious socially and the computer subject is not adopted with formal subject from school. In this study, we analyzed the computer application ability gap and recognized the importance of computer education by analyzing the difference in computer application ability between the students who take a computer class and who do not take a class. Also by comparing the difference of computer application ability between the city and rural student, we tried to find a solution to reduce the regional disparity.

• Journal of Information Technology Applications and Management
• /
• v.25 no.4
• /
• pp.67-77
• /
• 2018

With the types and targets of cyber attacks expanding and with personal information leaks increasing, the quantitative demand for information security specialists has increased. The base for training the workforce has expanded accordingly, but joblessness and job-seeking still coexist. To resolve the gap between labor demand and supply, education and training systems that can supply demand quickly are needed. It takes a considerable amount of time for information security education and new manpower supply through universities and graduate schools to be reflected in the market. However, if information security retraining is carried out in terms of career development of information security and related workforce, the problem of lack of experts could be solved in a relatively short period. This paper investigates and analyzes the information security work of the information security workforce, the degree of skill level, the need for retraining, and the workplace migration experience; it also discusses the direction of career development retraining.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.122-125
• /
• 2003

Aged people are increasing, and IT gap is also expanding. In order to solve the problem, we composed the intelligent space and eye contact communication. Intelligent space can pursue by getting a color from the camera

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.04a
• /
• pp.47-53
• /
• 1995

The Generalized Assignment (GAP) determines the minimum assignment of n tasks to m workstations such that each task is assigned to exactly one workstation, subject to the capacity of a workstation. In this paper, we presented a new heuristic search algorithm for GAPs. Then we tested it on 4 different benchmark sample sets of random problems generated according to uniform distribution on a microcomputer.

• Journal of The Korean Association of Information Education
• /
• v.20 no.1
• /
• pp.21-28
• /
• 2016

As the Programming education is organized in Elementary Education Curriculum by the 2015 Revised Curriculum, The study of teaching and learning methods are needed. In this study, we applied the Pair Programming in programming teaching and learning methods that can be applied at Elementary school level. The result of applying creative problem solving ability of the proposed teaching strategy appeared significant difference. Also it showed that a significant difference in the effectiveness of the completed programs and The Gap between Men and Women.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.4B
• /
• pp.455-463
• /
• 2004

In this paper, we deal with a ring-mesh network design problem arising from the deployment of WDM for the optical internet. The ring-mesh network consists of ring topology and full mesh topology for satisfying traffic demand while minimizing the cost of OAOMs and OXCs. The problem seeks to find an optimal clustering of traffic demands in the network such that the total number of node assignments is minimized, while satisfying ring capacity and node cardinality constraints. We formulate the problem as a mixed-integer programming model and prescribe a tabu search heuristic procedure Promising computational results within 3% optimality gap are obtained using the proposed method.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.85-89
• /
• 2010

Most images are composed as union of the various objects which can describe meaning respectively. Unlike human perception, The general computer systems used for image processing analyze images based on low level features like color, texture and shape. The semantic gap between low level image features and the richness of user semantic knowledges can bring about unsatisfactory classification results from user expectation. In order to deal with this problem, we propose a semantic cue based image classification method using salient points from object of interest. Salient points are used to extract low level features from images and to link high level semantic concepts, and they represent distinct semantic information. The proposed algorithm can reduce semantic gap using salient points modeling which are used for image classification like human perception. and also it can improve classification accuracy of natural images according to their semantic concept relative to certain object information by using salient points. The experimental result shows both a high efficiency of the proposed methods and a good performance.