• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.113-119
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• 2008

The World Wide Web is a dynamic collection of pages that includes a huge number of hyperlinks and huge volumes of usage informations. The resulting growth in online information combined with the almost unstructured web data necessitates the development of powerful web data mining tools. Recently, a number of approaches have been developed for dealing with specific aspects of web usage mining for the purpose of automatically discovering user profiles. We analyze sequence data, such as web-logs, protein sequences, and retail transactions. In our approach, we propose the clustering algorithm for sequence data using rough set theory. We present a simple example and experimental results using a splice dataset and synthetic datasets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9B
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• pp.1609-1617
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• 2000

In this paper a design of cryptographic coprocessor which implements SEED algorithm is described. To satisfy trade-off between area and speed, the coprocessor has structure in which 1 round operation is divided into three subrounds and then subround is executed for one clock. To improve clock frequency online precomputation scheme for round key is used. To apply the coprocessor to various applications, four operating modes such as ECB, CBC, CFB, and OFB are supported. Also to eliminate performance degradation due to data input and data output time between host computer and coprocesor, background input/output method is used. The cryptographic coprocessor is designed using $0.25{\mu}{\textrm}{m}$ CMOS technology and consists of about 29,300 gates. Its peak performance is about 237 Mbps encryption or decryption rate under 100 Mhz clock frequncy and ECB mode.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.495-498
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• 2005

The study is aimed at searching algorithm upgrading which can automatically compose goods displayed according to the degree of popularity regarding customer's requests, for the purpose of design of an intellectual shopping mall on the net and putting it into force by using classified technical Data-mining and statical analysis including personal information , entrance records and purchase records. This is for the study of strategic marketing. The system can automate the conventional shopping mall system by manual and personal judgements and also suggest a new formation of marketing techniques to strengthen the competition in B2B market which is steeply increasing.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.49-56
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• 2006

A special feature of P2P distributed system isn't always the guarantee of online status for peers. In other words we want to download the file from the peer when we use the P2P system but it sometimes caused this system to fail the download. Many studies to resolve this problem depend on re-transmission method. It caused to lower performance so we have to resolve this problem. In this study, we analysis the average usage time of P2P application user and raise the resource transmission guarantee to apply the selection criteria of resource supplier. Moreover the combinations of distributed object replication techniques, the role to enhance the data transmission opportunity of high popularity resource. will cause this search algorithm to advance.

• Knowledge Management Research
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• v.20 no.3
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• pp.119-137
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• 2019

With the recent spread of digital content, more people have been watching the digital content of TV programs on their PCs or mobile devices, rather than on TVs. With the change in such media use pattern, genres(types) of broadcast programs change in the flow of the times and viewers' trends. The programs that were broadcast on TVs have been released in digital content, and thereby people watching such content change their perception. For this reason, it is necessary to newly and differently classify genres(types) of broadcast programs on the basis of digital content, from the conventional classification of program genres(types) in broadcasting companies or relevant industries. Therefore, this study suggests a plan for newly classifying broadcast programs through using machine learning with the log data of people watching the programs in online media and for applying the new classification. This study is academically meaningful in the point that it analyzes and classifies program types on the basis of digital content. In addition, it is meaningful in the point that it makes use of the program classification algorithm developed in relevant industries, and especially suggests the strategy and plan for applying it.

• ETRI Journal
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• v.41 no.4
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.13-22
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• 2020

This paper presents an adaptive feedback based actuator fault detection and tolerant control algorithms for longitudinal functional safety of autonomous driving. In order to ensure the functional safety of autonomous vehicles, fault detection and tolerant control algorithms are needed for sensors and actuators used for autonomous driving. In this study, adaptive feedback control algorithm to compute the longitudinal acceleration for autonomous driving has been developed based on relationship function using states. The relationship function has been designed using feedback gains and error states for adaptation rule design. The coefficients in the relationship function have been estimated using recursive least square with multiple forgetting factors. The MIT rule has been adopted to design the adaptation rule for feedback gains online. The stability analysis has been conducted based on Lyapunov direct method. The longitudinal acceleration computed by adaptive control algorithm has been compared to the actual acceleration for fault detection of actuators used for longitudinal autonomous driving.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.