• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.748-751
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• 1997

The White Beamline of the Pohang Accelerator Laboratory(PAL) consists of main and second slits, a microprobe system, two ion chambers, a video-microscope, and a Si(Li) detector. These machine components must be controlled remotely through computer system to make user experiments precise and speedy. A real-time computer control system was developed to control and monitor these machine components. A VNIEbus computer with OS-9 real-time operating system was used for low-level data acquisition and control. VME I/O modules were used for step motor control and scaler control. The software has modular structure for maximum performance and easy maintenance. We developed database, I/O driver, and control software. We used PC/Window95 for data logging and operator interface. Visual C++ was used graphical user interface programming. RS232C was used for communication between VME and PC.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.531-535
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• 2002

This paper presents a real-time collaborative system for distributed design. The aim of the system is to provide designers with a virtual workspace where they can collaborate and exchange their design knowledge in distributed environment. The system consists of two subsequent modules. One is for the visualization of design data including CAD data, documents, images, and the other is real-time collaboration module. They make it possible for distributed designers to review the design data collaboratively and to share their design knowledge. The system was implemented by using the Internet protocols such as TCP/IP and IP multicast on the peer-to-peer based network. An example is presented and discussed to validate the proposed system.

• Korean Journal of Computational Design and Engineering
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• v.19 no.3
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• pp.294-304
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• 2014

This paper presents a real-time rendering algorithm of large-scale geometric data using GLSL (OpenGL shading language). It details the VAO (vertex array object) and VBO(vertex buffer object) to be used for up-loading the large-scale point clouds and polygon meshes to a graphic video memory, and describes the shader program composed by a vertex shader and a fragment shader, which manipulates those large-scale data to be rendered by GPU. In addition, we explain the global rendering procedure that creates and runs the shader program with the VAO and VBO. Finally, a rendering performance will be measured with application examples, from which it will be demonstrated that the proposed algorithm enables a real-time rendering of large amount of geometric data, almost impossible to carry out by previous techniques.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.

• Nuclear Engineering and Technology
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• v.43 no.2
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• pp.99-104
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• 2011

A Structure Health Monitoring (SHM) system needs a real-time remote data acquisition system to monitor the status of a structure from anywhere via Internet access. In this paper, we present a data acquisition system that monitors up to 40 Fiber Bragg Grating Sensors remotely in real-time. Using a TCP/IP protocol, users can access information gathered by the sensors from anywhere. An experiment in laboratory conditions has been done to prove the feasibility of our proposed system, which is built in special-purpose monitoring system.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.688-692
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• 2010

Wireless sensor network system collects and analyzes real-time data that have been requested by the many application nodes. This paper has constructed a sensor network cluster with various elements in the Gunsan City area of Jeollabuk-do, S.korea. The purpose of this paper is to utilize the constructed system in order to illustrate the real-time data in a diagram and analyze it to deduce the change ratio. The resulting analysis contents allow simple data interpretation by illustrating the data in change ratio by time, space, and motional directions. This analytical method will offer great benefit to those users using the wireless sensor network.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.57-66
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• 2008

Most concurrency control protocols have been devised for resolving data conflicts among real-time transactions of a single type. Recent real-time database systems should support various types of real-time transactions due to needs of many different types of applications and steady improvement of hardware. In this paper, we propose integrated concurrency control protocol to resolve data conflicts among hard and soft real-time transactions. Our proposed protocol, based on PCP(Priority Ceiling Protocol) for a hard real-time transactions and MVPR(Multiversion with Precedence Relationship), guarantees that hard real-time transactions meet their deadline, and decreases the deadline miss ratio of soft real-time transactions. We also proved that the proposed protocol guarantees serializable schedules and no deadlocks. The performance of the proposed protocol has been compared with other real-time concurrency protocols.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.125-128
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• 1999

In this paper, we propose a QoS-sensitive admission threshold method for the transmission of the non-real tine data packet such that the quality of services for both voice and data traffics are maintained to a required level. By detecting the active voice traffic during the current time slot, the non-real-time data packets are transmitted up to an admission threshold level during the next time slot. We found out that the optimum admission threshold is four voice traffic resources lower from the maximum allowable threshold to maintain the outage probability within 1% when the connected voice users are 15.

• 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.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.