• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1039-1041
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• 2003

Ocean color products derived from remote sensing satellite data are useful for monitoring the sea water quality such as the concentrations of chlorophyll, sediments and dissolved organic matter. Currently, ocean color products derived from MODIS data can be requested from NASA over the internet. However, due to the bandwidth limitation of most users in this region, and the time delay in data delivery, the products cannot be use for near-real time monitoring of sea water chlorophyll. CRISP operates a MODIS data receiving station for environmental monitoring purposes. MODIS data have been routinely received and processed to level 1B. We have adapted the higher level processing algorithms from the Institutional Algorithms provided by NASA to run in a standalone environment. The implemented algorithms include the MODIS ocean color algorithms. Seasonal chlorophyll concentration composite can be compiled for the region. By comparing the near-real time chlorophyll product with the seasonal composite, anomaly in chlorophyll concentration can be detected.

• Bulletin of the Korean Chemical Society
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• v.29 no.10
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• pp.2017-2022
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• 2008

Of the experiments to shorten NMR measuring time by sparse sampling, non-uniform sampling (NUS) is advantageous. NUS miminizes systematic errors which arise due to the lack of samplings by randomization. In this study, I report the real-time acquisition of 3D NMR data using NUS and maximum-entropy (MaxEnt) data processing. The real-time acquisition combined with NUS can reduce NMR measuring time much more. Compared with multidimensional decomposition (MDD) method, which was originally suggested by Jaravine and Orekhov (JACS 2006, 13421-13426), MaxEnt is faster at least several times and more suitable for the realtime acquisition. The designed sampling schedule of current study makes all the spectra during acquisition have the comparable resulting resolutions by MaxEnt. Therefore, one can judge the quality of spectra easily by examining the intensities of peaks. I report two cases of 3D experiments as examples with the simulated subdataset from experimental data. In both cases, the spectra having good qualitie for data analysis could be obtained only with 3% of original data. Its corresponding NMR measuring time was 8 minutes for 3D HNCO of ubiquitin.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.1-16
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• 2011

The real-time ubiquitous environment is required to be able to process a series of sensor data within limited time. The whole sensor data processing consists of several phases : getting data out of sensor, acquiring context and responding to users. The ubiquitous computing middleware is aware of the context using the input sensor data and a series of data from database or knowledge-base, makes a decision suitable for the context and shows a response according to the decision. When the real-time ubiquitous environment gets a set of sensor data as its input, it needs to be able to estimate the delay-time of the sensor data considering the available resource and the priority of it for scheduling a series of sensor data. Also the sensor data of higher priority can stop the processing of proceeding sensor data. The research field for such a decision making is not yet vibrant. In this paper, we propose a queuing time computation algorithm for sensor data processing in real-time ubiquitous environment.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.324-334
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• 2023

In this paper, the target simulator for RF signals was developed by using VST(Vector Signal Transceiver) and set by real-time signal processing SW programs. A function to process RF signals using FPGA(Field Programmable Gate Array) board was designed. The system functions capable of data processing, raw signals monitoring, target signals(simulated range, velocity) generating and RF environments data analyzing were implemented. And the characteristics of modulated signal were analyzed in RF environment. All function of programs for processing RF signal have options to store signal data and to manage the data. The validity of the signal simulation was confirmed through verification of simulated signal results.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2581-2595
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• 1997

In this paper, the real-time facility of the operating system for a VOD(Video On Demand) server have been analyzed and implemented. The requirements of the real-time scheduling have been gathered by analyzing the model of the video-data-transfer-path. Particularly, the influence of the bottleneck subsystem have been analyzed. Thus, we have implemented the real-time scheduler and primitives which is proper for processing the digital video. In performance measurements, the degree of the guarantee of the real-time scheduler have been experimented. The measured data show that the most time constraints of the process is satisfied. But, the network protocol processing by the interrupt is a major obstacle of the real-time scheduling. We also have compared the difference between the real-time scheduler and the non-real-time scheduler by measuring the inter-execution time. According to the measured results, the real-time scheduler should be used for efficient video service because the processor time allocated to the process can't be estimated when the non-real-time scheduler is used.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.1-6
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• 2018

In smart cities, data from various kinds of sensors are collected and processed to provide smart services to the citizens. Noise information services with noise maps using the collected sensor data from various kinds of ubiquitous sensor networks is one of them. This paper presents a research result which generates three dimensional (3D) noise maps in real-time for smart cities. To make a noise map, we have to converge many informal data which include big image data of geographical Information and massive sensor data. Making such a 3D noise map in real-time requires the processing of the stream data from the ubiquitous sensor networks in real-time and the convergence operation in real-time. They are very challenging works. We developed our own methodology for real-time distributed and parallel processing for it and present it in this paper. Further, we developed our own real-time 3D noise map generation system, with the methodology. The system uses open source softwares for it. Here in this paper, we do introduce one of our systems which uses Apache Storm. We did performance evaluation using the developed system. Cloud computing was used for the performance evaluation experiments. It was confirmed that our system was working properly with good performance and the system can produce the 3D noise maps in real-time. The performance evaluation results are given in this paper, as well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.547-549
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• 2021

In Artificial Intelligence analysis, the process of creating a model and verifying it is a task that requires computational processing time because it is Batch Processing performed with already generated data. We need to model, validate, and predict real-time data, such as stocks and defense information, with data generated directly in front of us. As a solution to this, we solve it by applying techniques to segment the data required for artificial intelligence modeling tasks in order of time processing and distribute the data across multiple processes.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.345-352
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• 2001

In this paper, a VoIP (Voice on Internet Protocol) terminal system, which can process voice in real time based on Linux, is designed and implemented. The hardware of it is designed using a i486 processor and a DSP codec chip which encodes and decodes voice data in real time. As an operating system, RTLinux, which is a real-time operating system based on Linux, is ported to manage real-time voice processing. The voice processing module of the system uses G.723.1 voice codec of ITU-T standard. It transfers voice data within 30ms to assure good voice quality. In order to satisfy the real time requirements and QoS (Quality-of-Service) for the voice data, the real-time voice processing device driver is designed and implemented. To verify the system, the chatting application program is developed and tested for QoS of the system.

• Applied Microscopy
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• v.46 no.1
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• pp.6-13
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• 2016

Fluorescence lifetime imaging microscopy (FLIM) has been considered an effective technique to investigate chemical properties of the specimens, especially of biological samples. Despite of this advantageous trait, researchers in this field have had difficulties applying FLIM to their systems because acquiring an image using FLIM consumes too much time. Although analog mean-delay (AMD) method was introduced to enhance the imaging speed of commonly used FLIM based on time-correlated single photon counting (TCSPC), a real-time image reconstruction using AMD method has not been implemented due to its data processing obstacles. In this paper, we introduce a real-time image restoration of AMD-FLIM through fast parallel data processing by using Threading Building Blocks (TBB; Intel) and octa-core processor (i7-5960x; Intel). Frame rate of 3.8 frames per second was achieved in $1,024{\times}1,024$ resolution with over 4 million lifetime determinations per second and measurement error within 10%. This image acquisition speed is 184 times faster than that of single-channel TCSPC and 9.2 times faster than that of 8-channel TCSPC (state-of-art photon counting rate of 80 million counts per second) with the same lifetime accuracy of 10% and the same pixel resolution.