• Economic and Environmental Geology
• /
• v.1 no.1
• /
• pp.35-46
• /
• 1968

Some of geologists in Korea recently postlated that Okchon system previously known to be precambrian age was the metamorphosed sediments of post-Chosen (Ordovician and pre-Kyeongsang (late Jurassic to Cretaceous) periods, or even definitely of Triassic period simply on the basis of the fact that Okcheon system overlies the Great Limestone series of Chosen system of Camber-ordovician age, and of other few assumptions of minor importance. As a result of such correlation, thick series of metasediments and Okcheon system of unknown age were established in this particular region and vaguely correlated to Paleozoic and Mesozoic sediments. Recent study done by the author reveled that: 1) only the upper Okcheon bed of S. Nakamura was true Okcheon system, and the middle and lower Okcheon beds were excluded, because they were correlated to Cambrian and Permian sediments resfectively, 2) Sangnaeri, Seochangri, and rengam formations of unknown age, and Baekhwasan, Jobong, and Ihwaryeong formations of Okcheon system of also unknown age were the metamorphosed Yangdeok system of Cambrian age, all of these formations were differentiated by the previous workers and were equivalent to the middle Okcheon system of S. Nakamure, and. 3) These metamorphosed Yangdeok system overlaid apparently the Great Limestone series in forms of overthrust and klippe which were produced by the orogeny took place during post-Daedong and pre-Kyeongsang period (probably middle to the Jurassic). The Sobaeksan Range, folded mountain Chains was also formed by this orogeny. Thus, Okcheon system newly defined by the author is precambrain age and consists in ascending order of Kemyenogsan, Hyangsan dolomite, and Daehangsan quartzite formation which were previously classified into metasediments of unknown age, and Munjuri, and Hwangkanri, formations which were differentiated into Okcheon system unknown age by the previous workers, but are of reversed sequence. Myeongori and Bukrori formations of Okcheon System are regard by the author as part of Hwangkanri formation. Few other assumption of minor important taken by the previous workers as their positive evidences are carefully explained that they were misinterpreted.

• Transactions on Control, Automation and Systems Engineering
• /
• v.2 no.3
• /
• pp.207-213
• /
• 2000

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.5
• /
• pp.139-147
• /
• 1996

PID control has been widely used for real control systems. Particularly, there are many researches on control schemes of tuning PID gains. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown papameters and unknown time-delay system. A numerical simulation was presented to illustrate the effectiveness of this method.

• The KIPS Transactions:PartB
• /
• v.14B no.3 s.113
• /
• pp.145-152
• /
• 2007

In this paper, we propose an automatic extraction model for unknown translations and implement an unknown translation extraction system using the proposed model. The proposed model as a phrase-alignment model is incorporated with three models: a phrase-boundary model, a language model, and a translation model. Using the proposed model we implement the system for extracting unknown translations, which consists of three parts: construction of parallel corpora, alignment of Korean and English words, extraction of unknown translations. To evaluate the performance of the proposed system, we have established the reference corpus for extracting unknown translation, which comprises of 2,220 parallel sentences including about 1,500 unknown translations. Through several experiments, we have observed that the proposed model is very useful for extracting unknown translations. In the future, researches on objective evaluation and establishment of parallel corpora with good quality should be performed and studies on improving the performance of unknown translation extraction should be kept up.

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.898-901
• /
• 1988

Up to now, using adaptive control method, Identification deals with system whose entire state variables and prameters are accessible for measurement. In practical situations, all the state variables cannot be measured and it is impossible to directly apply since the parameters of the system are unknown. Therefore, in this paper, using only input-output data, such a model of the system is not available since the parameters of the system are unknown. this leads to the concept of an adptive observer in which both the parameters and the state variable of the system are identified simultaniously. Lyapunov's direct method and Kalman-Yakubovich (K-Y) lemma are employed to ensure the stability of this schemes. The feature is that the signal and adaptive gain which is generated from filter is imposed upon feedback vector and then state variables and the unknown parameters can be identified. To show the usefulness of the proposed schemes, computer simulation result of unknown second-order system shows the effectiveness of the proposed schems.

• Convergence Security Journal
• /
• v.7 no.1
• /
• pp.19-27
• /
• 2007

Detecting unknown threats is a paradox ; how do you detect a threat if it is not known to exist? The answer is that unknown threat detection is the process of making a previously unknown threat identifiable in the shortest possible time frame. This paper examines the possibility of creating an unknown threat detection mechanism that security experts can use for developing a flexible protection system for networks. A system that allows the detection of unknown threats through monitoring system and the incorporation of dynamic and flexible logics with situational knowledge is described as well as the mechanisms used to develop such a system is illustrated. The system not only allows the detection of new threats but does so in a fast and efficient manner to increase the available time for responding to these threats.

• Smart Structures and Systems
• /
• v.30 no.3
• /
• pp.273-286
• /
• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.26.4-26
• /
• 2001

This paper proposes a dynamic observer that is applicable to linear time-invariant systems subject to unknown inputs. The proposed method utilizes the output feedback control structure to design an unknown input observer. We name it as the dynamic unknown input observer(UIO). The dynamic UIO can be designed easily compared to the usual static UIO, and the system response could be improved.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.8
• /
• pp.39-46
• /
• 1994

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.

• 전기의세계
• /
• v.22 no.4
• /
• pp.17-24
• /
• 1973

In the view point of practical engineering the identification problem may be considered as a problem to determine the optimal model in the sense of minimizing a given criterion function using the input-output records of the plant. In the system identification the statistical approach has been known to be very effective when the topological structure of the system and the statistical characteristics of the observation noises are known a priori. But in the practical situation there are many cases when the inforhation about the observation noises or the system noises are not available a priori. Here, the authors propose a new identification method which can be used effectively even in the cases when the variances of observation noises are unknown a priori. In the method, the identification of unknown parameters of a linear diserete system is achieved by minimizing the improved quadratic criterion function which is composed of the term of square equation errors and the term to eliminate the affection of observation noises. The method also gives the estimate of noise variance. Numerical computations for several examples show that the proposed procedure gives satisfactory results even when the short time observation data are provided.