• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.127-139
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• 2014

This study presents a logging method to trace the usage patterns of existing smartphone apps. The actual smartphone app itself, not a specially developed similar app with usage logging, would be used best for the experiment of observing the usage patterns. For this purpose, we used a method of injecting logging codes into existing smartphone app. Using this method, we conducted an experiment to trace usage patterns of a commercial IPTV app, and found that the method is very useful for acquiring detail usage log without influencing participants.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.349-352
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• 2005

The telematics positioning testbed is an infrastructure to test and verify positioning technology, the sub-component of telernatics system. The positioning testbed provides the environment of performance analysis for acquisition of static and dynamic positioning information using telematics vehicle. This testbed consists of onboard positioning system, positioning reference station and lab positioning server. The onboard positioning system equipped in telematics vehicle, consists of target positioning system, reference positioning system, and analysis tool. A equipment acquiring high precision positioning data obtained from GPS combined with IMU was set as a reference positioning system. Analysis tool compares observed positioning data with high precision positioning information from a reference positioning system, and processes positioning information. Positioning reference station is RTK system used for reducing atmosphere error, and it transmits corrected information to reference positioning system. Positioning server which is located at laboratory manages positioning database and provides monitoring data to integrated testbed operating system. It is expected that the testbed supports commercialization of telernatics technology and services, integrated testing among component technology and verification.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.550-553
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• 2005

To acquire radar target signature, various kinds of target are necessary. Measurement is one of the data acquiring method, but much time and high cost is required to get the target data from the real targets. Even if we can afford that, the targets we can access are very limited. To obtain target signatures avoiding these problems, we build the target CAD (Computer Aided Design) model for the calculation of target signatures. To speed up RCS calculation, we applied adaptive super-sampling and tested quite complex tank CAD model which is 1.4 hundred of thousands facet. We use calculated RCS data for ID range profile and 2D ISAR (Inverse Synthetic Aperture Radar) image formation. We adopted IFFT (Inverse Fast Fourier Transform) algorithm combined with polar formatting algorithm for the ISAR imaging. We could confirm the possibility of the construction of database from the images of CAD models for target classification applications.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Smart Structures and Systems
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• v.5 no.4
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• pp.427-442
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• 2009

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2643-2645
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• 2001

In this paper, a problem of limited sampling time that occurs essentially in digital data acquisition system is discussed. Low sampling frequency and a lot of processed data in micro-controller interferes normal operation of the controller from acquiring information in some case. To settle this problem, we design the sub-system which consists of a new simple micro-controller. which is managed the particular sensor making the trouble to main-controller system.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.51-57
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• 2017

The purpose of this study is to design a system that can be used for military simulation and virtual training using the location information of individual soldier's weapons. After acquiring the location information using Arduino's GPS shield, it is designed to transmit data to the Smartphone using Bluetooth Shield, and transmit the data to the server using 3G/4G of Smartphone in real time. The server builds the system to measure, analyze and manage the current position and the tracking information of soldier. Using this proposed system makes it easier to analyze the training situation for individual soldiers and expect better training results.

• Journal of Korean Biological Nursing Science
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• v.20 no.4
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• pp.244-251
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• 2018

Purpose: The purpose of this study was to explore nursing students' experience in microbiology courses. Methods: Data were gathered through 4 focus group interviews and 1 in-depth personal interview, by 19 nursing students who attended microbiology courses. Data were collected June 15-July 20, 2018. Conventional content analysis was used for data analysis. Results: The result of this study revealed 4 categories: "facing the challenge", "types of learning", "lack of learning motivation", "acquiring knowledge of infection". Conclusion: Findings suggest that it is important to identify nursing students' perspectives, to improve microbiology curriculum in the educational process. Also, it is necessary to connect continuously, between educational and practical environments, for effective management of microbiology courses.

• Journal of the Korean Society of Visualization
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• v.21 no.1
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• pp.119-126
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• 2023

Acquiring experimental data for PIV verification or machine learning training data is resource-demanding, leading to an increasing interest in synthetic particle images as simulation data. Conventional synthetic particle image generation algorithms do not follow physical laws, and the use of CFD is time-consuming and requires computing resources. In this study, we propose a new method for synthetic particle image generation, based on a Physics-Informed Neural Networks(PINN). The PINN is utilized to infer the flow fields, enabling the generation of synthetic particle images that follow physical laws with reduced computation time and have no constraints on spatial resolution compared to CFD. The proposed method is expected to contribute to the verification of PIV algorithms.