• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.108-111
• /
• 1999

In this paper, we develope a composite control law for linear systems with norm-bounded time-varying parameter uncertainties, which consists of a basic linear robust control do-signed so as to generate a desired transient time-response for the worst-case parameter variation and a nonlinear modification term designed so as to reduce cautiousness of the linear robust control in an adaptive manner. The proposed controller is established such that the reduction of cautiousness of the linear robust control is well incorporated into the achievement of a good transient behavior.

• Journal of the Korean Society for Precision Engineering
• /
• v.6 no.4
• /
• pp.108-118
• /
• 1989

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonliearity and parameter uncertainty in robot dynamics model. In this paper, an adaptive control scheme for a robot manipulator is proposed to design robust controller using model reference adaptive control technique and hyperstability theory but it does away with] assumption that the process is characterized by a linear model remaining time invariant during the adaptation process. The performance of controller is demonstrated by the simulation about position and speed control of a six-link manipulator with disturbance and payload variation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.12
• /
• pp.2484-2491
• /
• 2009

This paper proposes robust state feedback control of asynchronous sequential machines with model uncertainty. The considered asynchronous machine is deterministic, but its state transition function is partially known before executing a control process. The main objective is to derive the existence condition for a corrective controller for which the behavior of the closed-loop system can match a prescribed model in spite of uncertain transitions. The proposed control scheme also has learning ability. The controller perceives true state transitions as it undergoes corrective actions and reflects the learned knowledge in the next step. An adaptation is made such that the controller can have the minimum number of state transitions to realize a model matching procedure. To demonstrate control construction and execution, a VHDL and FPGA implementation of the proposed control scheme is presented.

• Journal of environmental and Sanitary engineering
• /
• v.23 no.4
• /
• pp.83-93
• /
• 2008

The non-linear least squares model(NLSM) has long been the standard technique used by hydrologists for constructing rating curves. The reasons for its adaptation are vague, and its appropriateness as a method of describing discharge measurement uncertainty has not been well investigated. It is shown in this paper that the classical method of NLSM can model only a very limited class of variance heterogeneity. Furthermore, this lack of flexibility often leads to unaccounted heteroscedasticity, resulting in dubious values for the rating curve parameters and estimated discharge. By introducing a heteroscedastic maximum likelihood model(HMLM), the variance heterogeneity is treated more generally. The maximum likelihood model stabilises the variance better than the NLSM approach, and thus is a more robust and appropriate way to fit a rating curve to a set of discharge measurements.

• Journal of Mechanical Science and Technology
• /
• v.20 no.11
• /
• pp.1823-1833
• /
• 2006

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

• 한국태양에너지학회:학술대회논문집
• /
• 2011.04a
• /
• pp.54-58
• /
• 2011

According to the Fourth Assessment Report of the Inter governmental Panel on Climate Change(IPCC), climate change is already in progress around the world, and it is necessary to start mitigation and adaptation strategies for buildings in order to minimize adverse impacts. It is likely that the South Korea will experience milder winters and hotter and more extreme summers. Those changes will impact on building performance, particularly with regard to cooling and ventilation, with implications for the quality of the indoor environment, energy consumption and carbon emissions. This study generate weather data for future climate change for use in impacts studies using PRECIS (Providing REgional Climate for Impacts Studies). These scenarios and RCM (Regional Climate Model) are provided high-resolution climate-change predictions for a region generally consistent with the continental-scale climate changes predicted in the GCM (Global Climate Model).

• Applied Biological Chemistry
• /
• v.29 no.4
• /
• pp.366-371
• /
• 1986

Fatty acid compositions of Panax ginseng leaves (6 year) grown under different light intensity in field and of the detached leaves exposed to high temperature (20 hours) were investigated by gas chromatography. Linoleic, linolenic, palmitic and palmitoleic acid were the major components(80%) of leaf lipid. The higher the growth light intensity, the lower the percentage of unsaturated acids or bonds, indicating metabolic adaptation to high temperature. Pattern similarity of fatty acid composition was little changed until 20% light but significantly different at 30%, suggesting 20% as limitation light intensity. The close similarity of fatty acid composition between the loaves grown under 30% light and the one at harvest rises uncertainty between adaptation to high temperature and senescence. Total fatty acid content decreased with the increase of light intensity. Short term high temperature $(25^{\circ}C\;or\;35^{\circ}C)$ increased total fatty acid content, unsaturated acid percentage and insignificant difference in pattern similarity of composition.

• Journal of the Korea Society for Simulation
• /
• v.25 no.3
• /
• pp.65-76
• /
• 2016

Self-adaptive software is software that adapts by itself to system requirements about the recognized problems without stopping the software cycle. In order to reduce the unnecessary adaptation in the system having the critical points, we propose proactive approach which can predict the future operation after a critical point. In this paper, we predict the future operation after a critical point using a hybrid model to deal with the characteristics of the observed data with the linear and non-linear pattern. The operation of the prediction method is determined on a timing decision indicator based on the prediction accuracy. The two main points of contributions of this paper are to reduce uncertainty about the future operation by predicting the situation after a critical point using hybrid model and to reduce unnecessary adaptation implementation by deciding a timing based on a timing decision indicator.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.19 no.6
• /
• pp.19-30
• /
• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• Journal of KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1569-1580
• /
• 2004

One commonly used approach to deal with uncertainty is Bayesian network which represents joint probability distributions of domain. There are some attempts to team the structure of Bayesian networks automatically and recently many researchers design structures of Bayesian network using evolutionary algorithm. However, most of them use the only one fittest solution in the last generation. Because it is difficult to combine all the important factors into a single evaluation function, the best solution is often biased and less adaptive. In this paper, we present a method of generating diverse Bayesian network structures through fitness sharing and combining them by Bayesian method for adaptive inference. In order to evaluate performance, we conduct experiments on learning Bayesian networks with artificially generated data from ASIA and ALARM networks. According to the experiments with diverse conditions, the proposed method provides with better robustness and adaptation for handling uncertainty.