• Journal of Astronomy and Space Sciences
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• v.36 no.2
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• pp.97-103
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• 2019

An automated signal-acquisition method for the NASA's space geodesy satellite laser ranging (SGSLR) system is described as a selection of two system parameters with specified probabilities. These parameters are the correlation parameter: the minimum received pulse number for a signal-acquisition and the frame time: the minimum time for the correlation parameter. The probabilities specified are the signal-detection and false-acquisition probabilities to distinguish signals from background noise. The steps of parameter selection are finding the minimum set of values by fitting a curve and performing a graph-domain approximation. However, this selection method is inefficient, not only because of repetition of the entire process if any performance values change, such as the signal and noise count rate, but also because this method is dependent upon system specifications and environmental conditions. Moreover, computation is complicated and graph-domain approximation can introduce inaccuracy. In this study, a new method is proposed to select the parameters via a conditional equation derived from characteristics of the signal-detection and false-acquisition probabilities. The results show that this method yields better efficiency and robustness against changing performance values with simplicity and accuracy and can be easily applied to other satellite laser ranging (SLR) systems.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.829-833
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• 1992

This paper presents the design of an automated EEG analyzing system. The design considerations including processing speed, A/D conversion, filtering, and waveforms detection, are overviewed with the description of the associated EEG characteristics. The architecture of the currently implemented system consists of a p-controller based front-end signal processing unit and a host computer system. The data acquisition procedures are described along with a couple of illustrations of the acquired EEG/EOG signal.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.293-295
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• 1992

This Paper presents the design of an automated EEG analyzing system. The design considerations including processing speed, A/D conversion, filtering, and waveforms detection, are overviewed with the description of the associated EEG charateristics. The architecture of the currently implemented system consists of a -controller based front-end signal processing unit and a host computer system. The data acquisition procedures are described along with a couple of illustrations of the acquired EEG/EOG signal.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.41-43
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• 2022

In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.445-450
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• 2019

This study proves that syringe reuse of automated injection system entails a risk of contrast media reflux and saline solution contamination which are pumped by a piston into the patients' venous cannula in the dynamic MR images, we will be aware of the serious problem. To quantify the contrast media contamination effect on the saline solution, identical volume of the saline solution was collected before and after the contrast injection to the patients' venous cannula following T1 weighted image scanning to verify whether signal intensities differences are observed. The signal intensity of saline solution after the contrast injection was significantly higher than that of saline before injection by 523.43%. This result is due to the backflow that contaminates the saline solution on the opposite side when the contrast agent is injected. In conclusion, the syringe used to inject contrast medium. causes cross-contamination due to contrast reflux. Therefore, even if the same patient's examination is used for quantitative analysis, the error should be avoided by changing the acquisition sequence or replacing the syringe.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.6 no.1
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• pp.37-42
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• 2010

The stay cylinder weld at the steam generator of Korean Standard Nuclear Power Plants is safety class I component and is subjected to be inspected by the volumetric examination such as ultrasonic method. As accessibility of this area is limited due to the narrow space and high radiation, the existing manual inspection method involves various difficulties. Moreover operators may be exposed to internal contamination by contaminated dust during the surface buffing process to improve the inspection reliability of this area. Recently the new automatic inspection system for stay cylinder welds has been developed. The inspection system basically consists of a driving assembly, data acquisition device and signal processing units. The driving assembly is classified by 1) the scanner for inspecting and buffing the weld, 2) pillars for guiding the scanner and 3) the base frame for loading and supporting pillars. The scanner has 4 sensor modules to inspect in 4 refracted angles and 4 incident directions. These components can be inserted into the skirt of the stay cylinder through the manway hole and assembled easily by one-touch in the skirt. Data acquisition device and signal processing units developed in previous works are also newly upgraded for better processing of data analysis and evaluation. The system has been successfully demonstrated not only in the mock-up but also in the field. In this paper, newly developed inspection system for the stay cylinder weld of the steam generator is introduced and their field applications are discussed.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.145-152
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• 2009

Currently almost all in-service-inspection techniques, applied in domestic nuclear power plants, are partial to field inspection technique. These kinds of techniques are related to managing nuclear power plants by the operation of foreign-produced inspection devices. There have been so many needsfor development of native in-service-inspection device because there is no native diagnosis device for nuclear power plant inspection yet in Korea. In this research, we developed several core techniques to make an automated ultrasonic pipe inspection system for nuclear power plants. A high performance multi-channel ultrasonic pulser/receiver module, an A/D converter module and a digital main CPU module were developed and the performance of the developed modules was verified. The S/N ratio, noise level and signal acquisition performance of the developed modules showed proper level as we designed in the beginning.

• Journal of Biosystems Engineering
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• v.33 no.3
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• pp.186-195
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• 2008

In this paper, a prototype tele-operative system with a mobile base was developed in order to automate cultivation of house melon. A man-machine interactive hybrid decision-making system via tele-operative task interface was proposed to overcome limitations of computer image recognition. Identifying house melon including position data from the field image was critical to automate cultivation. And it was not simple especially when melon is covered partly by leaves and stems. The developed system was composed of 5 major modules: (a) main remote monitoring and task control module, (b) wireless remote image acquisition and data transmission module, (c) three-wheel mobile base mounted with a 4 dof articulated type robot manipulator (d) exchangeable modular type end tools, and (e) melon storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. Once task was selected from the task control and monitoring module, the analog signal of the color image of the field was captured and transmitted to the host computer using R.F. module by wireless. A sequence of algorithms to identify location and size of a melon was performed based on the local image processing. Laboratory experiment showed the developed prototype system showed the practical feasibility of automating various cultivating tasks of house melon.