• International Journal of Contents
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• v.10 no.4
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• pp.11-17
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• 2014

The influence of acquisition environment on music score images captured by a camera has not yet been seriously examined. All existing Optical Music Recognition (OMR) systems attempt to recognize music score images captured by a scanner under ideal conditions. Therefore, when such systems process images under the influence of distortion, different viewpoints or suboptimal illumination effects, the performance, in terms of recognition accuracy and processing time, is unacceptable for deployment in practice. In this paper, a novel, lightweight but effective approach for dealing with the issues caused by camera based music scores is proposed. Based on the staff line information, musical rules, run length code, and projection, all regions of interest are determined. Templates created from inverse filter are then used to recognize the music symbols. Therefore, all fragmentation and deformation problems, as well as missed recognition, can be overcome using the developed method. The system was evaluated on a dataset consisting of real images captured by a smartphone. The achieved recognition rate and processing time were relatively competitive with state of the art works. In addition, the system was designed to be lightweight compared with the other approaches, which mostly adopted machine learning algorithms, to allow further deployment on portable devices with limited computing resources.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.21-29
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• 2012

In ubiquitous computing environment, rising large scale data exchange through sensor network with sharply growing the internet, the processing of the continuous stream data is required. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the active window sliding using trigger rule. The proposed method is a basic research to control the stream data flow for data mining and continuous query by trigger rules.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.807-825
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• 2020

In recent years, business environments have become more complex; therefore, enterprises must be capable of responding flexibly and agilely. For these purposes, effective enterprise systems service monitoring and early decision making based on the same, emerge as core competency of the enterprise. In addition, enterprise system techniques that filter meaningful data are needed to event processing. However, the existing study related with this is nothing but discovering of service faults by monitoring depending upon API of BPEL engine or middleware, or is nothing but processing of simple events based on low-level events. Accordingly, there would be limitations to provide useful business information. In this study, we present an extended event processing model that enables delivery of more valuable and useful business information through situation detection. Primarily, the event processing architecture in an enterprise system is proposed as a definite approach, and then define an event meta-model suitable for the proposed architecture. Based on the defined model, we propose the syntax and semantics of the elements that make up the event processing language include various and progressive event operators, the rules, complex event pattern, etc. In addition, an event context mechanism is proposed to analyze more delicate events. Finally, the effectiveness and applicability of proposed approach is presented through a case study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1831-1853
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• 2012

By sending periodically short bursts of traffic to reduce legit transmission control protocol (TCP) traffic, the low-rate denial of service (LDoS) attacks are hard to be detected and may endanger covertly a network for a long period. Traditionally, LDoS detecting methods mainly concentrate on the attack stream with feature matching, and only a limited number of attack patterns can be detected off-line with high cost. Recent researches divert focus from the attack stream to the traffic anomalies induced by LDoS attacks, which can detect more kinds of attacks with higher efficiency. However, the limited number of abnormal characteristics and the inadequacy of judgment rules may cause wrong decision in some particular situations. In this paper, we address the problem of detecting LDoS attacks and present a scheme based on the fluctuant features of legit TCP and acknowledgment (ACK) traffic. In the scheme, we define judgment criteria which used to identify LDoS attacks in real time at an optimal detection cost. We evaluate the performance of our strategy in real-world network topologies. Simulations results clearly demonstrate the superiority of the method proposed in detecting LDoS attacks.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.71-77
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• 2011

CEP(Complex Event Processing) is a technique to find complex event pattern in a massive information system. Based on CEP technique, an abuse pattern monitoring method has been developed to provide an real-time detection. In the method, the events occurred by game-play are observed to be against the rules using CEP. User abuse patterns are pre-registered in CEP engine. And CEP engine monitors user abuse after aggregating the game data transferred by game logging server. This article provides the performance analysis results of the abuse pattern monitoring method using real game DB. We results that the method proposed in previous study is effective to monitor abusing users.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.378-382
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• 2006

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM). NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The two most important coefficients are selected by the non-overlap area distribution measurement method to minimize the classification rules that show PVC classification rate of 99.90%. By Presenting locations of the extracted two coefficients based on the R wave location, it is shown that PVC can be detected using only information of the two portions.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.389-390
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• 1996

The CCDs are widely used in astronomical observations either in direct imaging use or spectroscopic mode. However, the areas of available sensors are too small for large imaging format. One possibility to obtain large detection area is to assemble mosaics of CCD, and drive them simultaneously. Parallel driving of many CCDs together rules out the possibility of individual tuning; however, such optimisation is very important, when the ultimate low light level performance is required, particularly for new, or mixed devices. In this work, a new concept is explored for an entirely novel approach, where the drive waveforms are multiplexed and interleaved. This simultaneously reduces the number of leadout connections and permits individual optimisation efficiently. The digital controller can be designed within a single EPLD (Erasable Programmable Logic Device) chip produced by a CAD software package, where most of the digital controller circuits are integrated. This method can minimise the component. count., and improve the system efficiency greatly, based on earlier works by Han et a1. (1996, 1994). The system software has an open architecture to permit convenient modification by the user, to fit their specific purposes. Some variable system control parameters can be selected by a user with a wider range of choice. The digital controller design concept allows great flexibility of system parameters by the software, specifically for the compatibility to deal with any number of mixed CCDs, and in any format, within the practical limit.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.

• Journal of Magnetics
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• v.19 no.3
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• pp.255-260
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• 2014

A portable clip-type pulsimeter equipped with a magnetic sensing Hall device has been developed to raise the accuracy of oriental disease diagnosis and therapy by convergence of magnetism and oriental medicine. To improve accuracy and reliability of conventional pulsimeter due to subjective analysis of the pulse wave and measuring position dependency of the arterial pulse sensor, the fuzzy algorithm was applied to analyze the strong- and weak-pulse wave symptom. Optimal time for electronic acupuncture was calculated using fuzzy rules and interference were drawn from objective data in view of pre-treatment. Moreover, the electrical characteristics of the pain parts that respond to acupuncture point were applied in view of post-treatment to propose the scientific pulse wave algorithm and simulation experiment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.197-202
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• 2014

It is used the important method that Oriental doctor determines patient's disease status observing patient's state of tongue in Oriental medicine clinic. In this paper, it developed the how to use the pulse diagnosis and tongue diagnosis based on s mart based electronic acupuncture. It will do objective judgment without wrong diagnosis. In this paper, we developed the algorithm that it automatically determines patient health condition and smart electronic acupuncture kit using fuzzy logic and fuzzy reasoning system were completed. In this paper, Simulation results proved that acupuncture is effective than the traditional method of using electronic intelligence.