• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.15-21
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• 2020

In this paper, the observation performance of the electro-optical telescope system which surveils the unknown space objects, is analyzed by the Modeling & Simulation(M&S). The operation concept for the observation of the unknown space objects using two telescope systems is considered and the M&S models are constructed. Based on the operation concept for observing the unknown space objects, the estimated orbit is generated by Initial Orbit Determination(IOD) and the observation performance is analyzed according to the offset compensation cycle for the estimated orbit. The result of the M&S based analysis in this paper shows that the observation performance increases with the shorter offset compensation cycle, and decreases with the longer offset compensation cycle. Therefore, to improve the performance of the telescope system which surveils the unknown space objects, the observation system with accurate initial orbit determination or shorter offset compensation cycle should be designed and constructed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1826-1834
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• 2021

In this paper, we check the possibility of continuous tracking when photographing unknown space objects in a short period of time in an optical observation system on the ground. Simulated observation data were generated for target limited to low-orbit areas. The performance index of the prediction error was set in consideration of the property of targets. Kalman Filter was applied to predict the next location of the target. A constant velocity/acceleration dynamic model was applied to the two axes of the azimuth/elevation of the unknown space object respectively. As a result of performing the Monte Carlo simulation, the maximum error ratio of the maximum nonlinear section was less than 2%, which could be determined to ensure continuous tracking. The CA model had little change in the prediction error value for each case, making it more suitable for tracking unknown space objects. This analysis could provide a foundation for determining the orbit of unknown space objects using optical observation.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.83-91
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• 2011

The roadmap DB update for unknown roads is a very important factor for car navigation systems. In this paper, we propose a cloud computing based roadmap generation method for use between heterogeneous navigation system for unknown roads. While the drivers drive on unknown roads, the proposed method extracts the road attribute information, and then generates the metadata in an XML format that is available for the heterogeneous navigation systems in a cloud environment. The metadata is proposed to be used as a replacement for conventional proprietary roadmap formats which used by roadmap providers, which is efficient for heterogeneous navigation system providers in a cloud computing environment. Then, this metadata is provided to the roadmap DB providers through the cloud computing interfaces. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented road map generation method can more efficiently update the unknown road information.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.89-95
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• 2002

This paper considers the problem of estimating paramaters of the bivariate exponential distribution with a loaction parameter for a two-component shared parallel system using component data from system-level life test terminated at the time of the prespecified number of system failure. In the system-level life testing, there are three patterns of failure types; 1) both component failed 2) both component censored 3) one is failed and the other is censored. In the third case, we assume that the failure time might be known or unknown. The maximum likelihood estimators are obtained for the case of known/unknown failure time when the other component is censored.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.33-36
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• 2002

In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.347-353
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• 2019

Electric-hydraulic sever cylinders are used in many offshore applications such as wind energy farms, solar farms and plants. Jack-up barges are often used for these offshore system operations. Jack-up barge control is up/down by hydraulic cylinder position control. Working in harsh environments can lead to changes in internal parameters. This nonlinearity makes precise control difficult. In order to overcome the problems, we proposed a method of unknown-parameter estimation algorithm based on measurements obtained by system. In this paper, we employee Unscented Kalman filter (UKF) to estimate states and unknown-parameter from augmented nonlinear equation. Performance of estimation results is verified in simulation on an environments of Matlab. The estimation results of the state and unknown-parameter show that the estimation error of unknown-parameter is reduced according to decreasing the state estimation error.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.27-35
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• 2021

With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.4
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• pp.284-290
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• 2013

The authors propose a nonlinear unknown input observer to estimate the angular speed of a satellite and to detect faults of reaction wheels. Input values are necessary to estimate the angular speed. Therefore, estimation errors are inevitable if faults occur in actuators or reaction wheels. Unknown input observers are useful to estimate the states of a system without being affected by unknown faults. The authors have designed a nonlinear unknown input observer by using the SDRE method and verified the proposed observer via numerical simulations. In spite of various and simultaneous faults, we have estimated the states and detected faults exactly by the proposed nonlinear unknown input observer.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.9
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• pp.381-388
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• 2014

In this paper, we deal with implementation issues of an unknown word classifier for middle-school level English writing test. We define the type of unknown words occurred in English text and discuss the detection process for unknown words. Also, we define the type of swear words occurred in students's English writings, and suggest how to handle this type of words. We implement an unknown word classifier with a swear detection module for developing an automatic English writing scoring system. By experiments with actual test data, we evaluate the accuracy of the unknown word classifier as well as the swear detection module.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.251-255
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• 2019

On today, Protocol reverse-engineering technique can be used to extract the specification of an unknown protocol. However, there is no standardized method, and in most cases, the extracting process is executed manually or semi-automatically. If the information about the structure of an unknown protocol could be acquired in advance, it would be easy to conduct reverse engineering. the feature extraction is an important step in unknown protocol classification. However, in this paper, we present a comparison several feature extraction techniques and suggests a method of feature extraction algorithm for recognizing unknown protocol. In order to verify the performance of the proposed system, we performed the training using eight open protocols to evaluate the performance using unknown data.