• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.92.6-92
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• 2001

MSC is being developed to be installed on KOMPSAT(Korea Multi-Purpose Satellite-II and to provide high resolution multi-spectral. MSC consists of three main subsystems. One is EOS(Electro-Optics Subsystem), another is PMU(Payload Management Unit) and the other is PDTS(Payload Data Transmission Subsystem). There is an SBC(Single Board Computer) in the PMU to control all MSC subsystems. SBC incorporates Intel 80486 as a main processor and VxWorks as a real-time operating system. SBC software consists of four main tasks and several modules to deal with all control information for imaging and all the state of health telemetrv data, and to perform interface with another MSC units. SBC software also has to handle a lot of commands in order for MSC to perform his mission. One mission command consists of a series of related commands, which are In be executed in the designated sequence, with a specified time ...

• Current Industrial and Technological Trends in Aerospace
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• v.9 no.1
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• pp.90-101
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• 2011

In this article, we introduce the technologies and trend of technical evolution of Payload Data Handling Unit (PDHU) for Earth Observation Satellites. As well, we review the efforts for the Koreanization of PDHU so far, and conclude with some suggestions for future work.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1121-1123
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• 2003

The CCD (Charge coupled device) detector that is used to convert the light into electronic data is very important component in satellite camera. A Linear CCD Spectral detector shall be used in the MSC (Multi-Spectral Camera, to obtain data for high-resolution images) Payload. In this paper, the design concept of the CCD detector control module in the MSC CEU (Camera electronic unit) system which will be a payload on KOMPSAT is described in terms of H/W (clock speed and accuracy).

• Annual Conference of KIPS
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• 2022.11a
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• pp.228-230
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• 2022

딥러닝 기반의 인공지능 기술이 발달함에 따라 이상 탐지 방법에도 딥러닝이 적용되었다. 네트워크 트래픽으로부터 요약 및 집계된 Feature 를 학습하는 방법과 Packet 자체를 학습하는 등의 방법이 있었다. 그러나 모두 정보의 제한적으로 사용한다는 단점이 있었다. 본 연구에서는 Http Request에 대한 사전학습 기반의 효과적인 이상 탐지 방법을 제안한다. 사전학습에 고려되는 토큰화 방법, Padding 방법, Feature 결합 방법, Feature 선택 방법과 전이학습 시 Numerical 정보를 추가하는 방법을 소개하고 각 실험을 통해 최적의 방법을 제안한다.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1323-1337
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• 2020

GEO-KOMPSAT-2A AMI (Advanced Meteorological Imager) radiometric calibration evaluation is an essential element not only for functional and performance verification of the payload but for the quality of the sensor data. AMI instrument consists of six reflective channels and ten thermal infrared ones. One of the key parameters representing radiometric properties of the sensor is a SNR (Signal-to-Noise Ratio) for the reflective channels and a NEdT (Noise Equivalent delta Temperature) for the IR ones respectively. Other important radiometric calibration parameters are a dynamic range and a gain value related with the responsivity of detectors. To verify major radiometric calibration performance of AMI, an offline radiometric evaluation tool was developed separately with a real-time AMI data processing system. Using the evaluation tool, validation activities were carried out during the GEO-KOMPSAT-2A In-Orbit Test period. The results from the evaluation tool were cross checked with those of the HARRIS, which is the AMI payload vendor. AMI radiometric evaluation activities were conducted through three phases for both sides (Side 1 and Side 2) of AMI payload. Results showed that performances of the key radiometric properties were outstanding with respect to the radiometric requirements of the payload. The effectiveness of the evaluation tool was verified as well.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.478-481
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• 2005

The PMU (Payload Management Unit) is the main subsystem for the management, control and power supply of the MSC (Multi-Spectral Camera) Payload operation. It is the most important function for the electro-optical camera system that performs the Non-Uniformity Correction (NUC) function of the raw imagery data, rearranges the data from the CCD (Charge Coupled Device) detector and output it to the Data Compression and Storage Unit (DCSU). The NUC board in PMU performs it. In this paper, the NUC board system is described in terms of the configuration and the function, the efficiency for non-uniformity correction, and the influence of the data compression upon the peculiar feature of the CCD pixel. The NUC board is an image-processing unit within the PMU that receives video data from the CEV (Camera Electronic Unit) boards via a hotlinkand performs non-uniformity corrections upon the pixels according to commands received from the SBC (Single Board Computer) in the PMU. The lossy compression in DCSU needs the NUC in on-orbit condition.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.143-153
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• 2022

With the development of networks and the increase in the number of network devices, the number of cyber attacks targeting them is also increasing. Since these cyber-attacks aim to steal important information and destroy systems, it is necessary to minimize social and economic damage through early detection and rapid response. Many studies using machine learning (ML) and artificial intelligence (AI) have been conducted, among which payload learning is one of the most intuitive and effective methods to detect malicious behavior. In this study, we propose a preprocessing method to maximize the performance of the model when learning the payload in term units. The proposed method constructs a high-quality learning data set by eliminating unnecessary noise (stopwords) and preserving important features in consideration of the machine language and natural language characteristics of the packet payload. Our method consists of three steps: Preserving significant special characters, Generating a stopword list, and Class label refinement. By processing packets of various and complex structures based on these three processes, it is possible to make high-quality training data that can be helpful to build high-performance ML/AI models for security monitoring. We prove the effectiveness of the proposed method by comparing the performance of the AI model to which the proposed method is applied and not. Forthermore, by evaluating the performance of the AI model applied proposed method in the real-world Security Operating Center (SOC) environment with live network traffic, we demonstrate the applicability of the our method to the real environment.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.241-241
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• 2002

MSC(Multi-Spectral Camera) is a main payload of KOMPSAT(Korea Multi-Purpose Satellite)-II which will be launched in 2004. MSC will perform his mission with the GSD(Ground Sample Distance) of 1m, swath width of 15km and spectral range of 450nm~900nm at the altitude of 685km. MSC consists of three main subsystems. One is EOS(Electro-Optics Subsystem), another is PMU(Payload Management Unit) and the other is PDTS(Payload Data Transmission Subsystem). There is an SBC(Single Board Computer) in the PW to control all the other units and SBC software performs the interface with spacecraft and control all MSC sub-units. SBC software consists of a lot of tasks and manages them with the time criticalness. All tasks are designed to be scheduled and executed at the predetermined time in order to make sure that the mission of MSC system is achieved successfully. In this paper, the real-time task scheduling of the SBC software will be described and analyzed.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.509-511
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• 2005

KOMPSAT -2 equipped with an optical telescope(MSC) will be launched in this year. It can take images of the earth with push-broom scanning at altitude 685Km. Its resolution is 1m in panchromatic channel with a swath width of 15 km After the MSC is tested and the performance is measured at instrument level, it is installed on satellite. The image passes through the electro-optical system, compression and storage unit and fmally downlink sub-systems. This integration procedure necessitates the functional test of all subsystems participating in the image chain. The objective of functional test at satellite level(Quick Look test) is to check the functionality of image chain by real target image. Collimated moving image is input to the EOS in order to simulate the operational environments as if KOMPSAT -2 is being operated in orbit. The image chain from EOS to data downlink subsystem will be verified through Quick Look test. This paper explains the Quick Look test of KOMPSAT -2 and compares the taken images with collimated input ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.615-622
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• 2013

Fast and accurate signature extraction is essential to improve the performance of the payload signature-based traffic analysis methods. However the slow manual process in extracting signatures make difficult to deal with the rapidly changing application in current Internet environment. Therefore, in this paper we propose a system automatically generating signatures from ground-truth traffic data. In addition, we improve the efficiency of signature extraction by recognizing the application protocol using a protocol filters and generating signatures automatically according to the application-specific protocol contents. In order to verify the validity of the system proposed in this paper, we compared the signatures automatically generated from our system with the signatures manually created for a few popular applications.