• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.304-313
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• 2005

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robot need to recognize his position and direction for intelligent performance in an unknown environment. And the mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this research, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. As a general approach of sensor fusion, a UT -Based Sensor Fusion(UTSF) scheme using Unscented Transformation(UT) is proposed for either joint or disjoint data structure and applied to the landmark identification for mobile robot navigation. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, UT-Based UTSF scheme is applied to the navigation of a mobile robot in an unstructured environment as well as structured environment, and its performance is verified by the computer simulation and the experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.630-634
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• 2005

본 논문은 신경망을 이용한 간섭 신호 제어 복합 다층 퍼셉트론에서 DS/SS 이동 통신에서 수신된 신호를 검출하는 것에 대하여 연구한다. 수신 신호가 일정한 비트율을 갖는 채널에 전송하기 위하여 신경망을 이용한 새로운 탭 가중치 갱신 제어 방법을 제안한다. 적응 횡단선 필터는 상호 심볼간 간섭을 억압하기 위해 LMS 알고리즘 사용하고, 응답과 실제 출력간의 차인 에러를 이용하여 탭 가중치 조절 메카니즘을 통해 탭 가중치를 갱신함으로서 효과적으로 간섭을 제거한다. 본 논문은 상호 심볼간 간섭을 효율적으로 억압해온 다계층 퍼셉트론 조합을 이용하여 제안된 알고리즘을 통해 탭 가중치 갱신이 보다 효율적으로 이루어질 수 있도록 한다. 시뮬레이션을 통해 평균자승 에러의 수렴 특성이 우월하다는 것을 연구한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1201-1208
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• 2010

In this paper, we propose an image encryption method using two linear MLCA(Maximum Length Cellular Automata). The encryption method first sets arbitrary 8 bit initial values. Next, we create high quality PN(pseudo noise) sequences by converting rows and columns with the set initial values. hen we generate a basis image using the set PN sequences. Lastly, the final image with high encryption level is produced by XOR operation of the basis image and the original image. In order to verify that the proposed method has the high encryption level, we performed histogram and stability analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Structural Engineering and Mechanics
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• v.57 no.1
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• pp.45-63
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• 2016

Fiber metal laminates (FMLs) represent a high-performance family of hybrid materials which consist of thin metal sheets bonded together with alternating unidirectional fiber layers. In this study, the buckling behavior of a FML circular cylindrical shell under axial compression is investigated via both analytical and finite element approaches. The governing equations are derived based on the first-order shear deformation theory and solved by the Navier solution method. Also, the buckling load of a FML cylindrical shell is calculated using linear eigenvalue analysis in commercial finite element software, ABAQUS. Due to lack of experimental and analytical data for buckling behavior of FML cylindrical shells in the literature, the proposed model is simplified to the full-composite and full-metal cylindrical shells and buckling loads are compared with the available results. Afterwards, the effects of FML parameters such as metal volume fraction (MVF), composite fiber orientation, stacking sequence of layers and geometric parameters are studied on the buckling loads. Results show that the FML layup has the significant effect on the buckling loads of FML cylindrical shells in comparison to the full-composite and full-metal shells. Results of this paper hopefully provide a useful guideline for engineers to design an efficient and economical structure.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.126-131
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• 2000

Through many users employ TCP of which the performance has been proved in Internet, but many papers Proposed to improve TCP performance according to varying network architecture. In Particular, BWDP(bandwidth-delay Product) grew larger because of the increasing delay in satellite link and the network's speed-up. To consider these increased bandwidth-delay product, it is suggested that TCP options include Window Scale option. TimeStamp option, and PAWS. Because TCP window size should be commonly high in the network with these increased bandwidth-delay product, the multiple decrease and linear increase scheme of current TCP would cause underflow and instability within network. Then TCP performance is reduced as a result. Thus, to improve TCP congestion control algorithm in the network which has large sized window, this paper proposes the congestion control scheme that controls window size by using TimeStamp option.

• Korean Journal of Plant Resources
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• v.21 no.3
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• pp.210-215
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• 2008

Gene-expression analysis is increasingly important in biological research, with real-time reverse PCR (RTPCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. However, this technique requires important preliminary work for standardizing and optimizing the many parameters involved in the analysis. Plant stress studies are more and more based on gene expression. The analysis of gene expression requires sensitive and reproducible measurements for specific mRNA sequence. Several genes are regulated in response to abitoic stresses, such as salinity, and their gene products function in stress response and tolerance. The design of the primers and TaqMan probes for real-time PCR assays were carried out using the Primer $Express^{TM}$ software 3.0. The PCR efficiency was estimated through the linear regression of the dilution curve. To understand the expression pattern of various genes under salt stressed condition, we have developed a unique public resource of 9 stress-related genes in poplar. In this study, real-time RT-PCR was used to quantify the transcript level of 10 genes (9 stress-related genes and 1 house keeping gene) that could play a role in adaptation of Populus davidiana. Real-time RT-PCR analyses exhibited different expression ratios of related genes. The data obtained showed that determination of mRNA levels could constitute a new approach to study the stress response of P. davidiana after adaptation during growth in salinity condition.

• Journal of Power Electronics
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• v.19 no.4
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• pp.1000-1010
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• 2019

The use of internal-model-based linear controller, such as resonant controller, is a well-established technique for the current control of grid-connected systems. Attractive properties for resonant controllers include their two-sequence tracking ability, the simple control structure, and the reduced computational burden. However, in the case of continuous-designed resonant controller, the transient performance is inevitably degraded at a low switching frequency. Moreover, available design methods for resonant controller is not able to realize the direct design of transient performances, and the anticipated transient performance is mainly achieved through trial and error. To address these problems, the zero-order-hold (ZOH) characteristic and inherent time delay in digital control systems are considered comprehensively in the design, and a corresponding hold-equivalent discrete model of the grid-connected converter is then established. The relationship between the placement of closed-loop poles and the corresponding transient performance is comprehensively investigated to realize the direct mapping relationship between the control gain and the transient response time. For the benefit of automatic tuning and real-time adaption, analytical expressions for controller gains are derived in detail using the required transient response time and system parameters. Simulation and experimental results demonstrate the validity of the proposed method.

• ETRI Journal
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• v.44 no.5
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.