• Spatial Information Research
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• v.18 no.1
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• pp.69-76
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• 2010

Spatial data streams from sensors are useful in context-awareness for many types of applications. However, an important gap is found between spatial data stream management in real-time and complex computation for spatial context-awareness, and this brings about serious difficulty to integrate spatial data stream processing and context-awareness. In this paper, we present a system called SCONSTREAM(Spatial CONtext STREAm Management) that we have developed to resolve the gap between spatial data stream and context-awareness. The key approach of our system is to filter off unnecessary spatial data streams and convert them to the spatial context streams, which are smaller and more suitable to be processed by the context-awareness module than raw data from sensors. By experimentation, We show that SCONSTREAM resolves the functional gap between spatial stream processing and spatial context-awareness module.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.171-174
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• 2006

A large number of service providers in countries all over the world have established GNSS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. In current GNSS network processing, all the geometric related information such as ionospheric free carrier phase ambiguities from all stations and satellites, tropospheric effects, orbit errors, receiver and satellite clock errors are estimated in one centralized Kalman filter. Although this approach provides an optimal solution to the estimation problem, however, the processing time increases cubically with the number of reference stations in the network. Until now one single Personal Computer with Pentium 3.06 GHz CPU can only process data from a network consisting of no more than 50 stations in real time. In order to process data for larger networks in real time and to lower the computational load, a federated filter approach can be considered. The main benefit of this approach is that each local filter runs with reduced number of states and the computation time for the whole system increases only linearly with the number of local sensors, thus significantly reduces the computational load compared to the centralized filter approach. This paper presents the technical aspect and performance analysis of the federated filter approach. Test results show that for a network of 100 reference stations, with the centralized approach, the network processing including ionospheric modeling and network ambiguity fixing needs approximately 60 hours to process 24 hours network data in a 3.06 GHz computer, which means it is impossible to run this network in real time. With the federated filter approach, only less than 1 hour is needed, 66 times faster than the centralized filter approach. The availability and reliability of network processing remain at the same high level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.1107-1111
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• 2005

The DVR system realization with watermarking and MPEG-4 for real time processing speed improvement is presented in this paper. For the real time processing the system is used the DSP processor, Quick DMA for data transmission, watermarking for security and MPEG-4 compression for facility. The algorithms are that the operational structure has the internal memory of processor, and the optimal realization is suitable to form the DSP processor structure r processed for the iterative operations. The experimental result shows the real time processing is improved 12% over for the D1 image in comparison with the other system.

• International Journal of Contents
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• v.10 no.2
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• pp.47-58
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• 2014

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

• Journal of Astronomy and Space Sciences
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• v.30 no.3
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• pp.207-212
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• 2013

Ionosphere is one of the largest error sources when the navigational signals produced by Global Positioning System (GPS) satellites are transmitted. Therefore it is very important to estimate total electron contents (TEC) in ionosphere precisely for navigation, precise positioning and some other applications. When we provide ionospheric TEC values in real-time, its application can be expanded to other areas. In this study we have used data obtained from nine Global Navigation Satellite System (GNSS) reference stations which have been operated by Korea Astronomy and Space Science Institute (KASI) to detect ionospheric TEC over South Korea in real-time. We performed data processing that covers converting 1Hz raw data delivered from GNSS reference stations to Receiver INdependent Exchange (RINEX) format files at intervals of 5 minutes. We also analyzed the elevation angles of GPS satellites, vertical TEC (VTEC) values and their changes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.377-380
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• 2019

To accommodate for the difficult task of satisfying application's system timing constraints, we are developing Splash, a real time stream processing language for embedded AI applications. Splash is a graphical programming language that designs applications through data flow graph which, later automatically generates into codes. The codes are compiled and executed on top of the Splash runtime system. The Splash runtime system supports two aspects of the application. First, it supports the basic stream processing functions required for an application to operate on multiple streams of data. Second, it supports the checking and handling of the user configurated timing constraints. In this paper we explain the implementation of the first aspect of the Splash runtime system which is being developed using a real time communication middleware called DDS.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• Journal of Korea Multimedia Society
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• v.25 no.4
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• pp.583-599
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• 2022

Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.76-86
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• 2023

Radars used by air-crafts have two important characteristics; First, they should have a real-time signal processing system finishing signal processing before deadline while getting and processing successive in-phase and quadrature data. Second, they can cover a lot of modes including A2A(Air to Air), A2G(Air to Gound), A2S(Air to Sea), and Ground Map(GM). So the structure of radar signal processing SWs in modern airborne radars are becoming more complicate. Also, the implementation of radar signal processing SW needs to reuse common code blocks between other modes for efficiency or change some of the code blocks into alternative algorithm blocks. These are the reason why the radar signal processing SW framework suggested in this paper is taking advantage of modular programming. This paper proposes an modular framework applicable on the airborne radar signal processing SW maintaining the real-time characteristic using the signal processing procedures for A2G/A2S as examples.

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.276-283
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• 2000

This paper presents the configuration of simulator for the functional test of TCMS on developing. This simulator is composed of three cabinets. There is equipped the TCMS system which have the same configuration of 1 train(SMG Line 7＆8) on the two cabinets. And there are equipped VME PC, VME CPU Board and I/F modules on the rest cabinet. This simulator has tow parts, which are user I/F part and Real-time signal processing part. We programed user I/F part with Visual C++ to support GUI function, and signal processing part with RTOS to support Real-Time signal processing. This simulator can produce all the signal and data for the TCMS functional testing.