• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.479-491
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• 2007

This paper presents a new adaptive observer design method for a class of uncertain nonlinear systems by using spline approximation. This scheme leads to a simplified observer structure which requires only fixed number of integrations, regardless of the number of parameters to be estimated. This benefit can reduce the number of integrations of the observer filter dramatically. Moreover, the proposed adaptive observer automatically generates the required spline elements according to the varying output value and, as a result, does not requires the pre-knowledge of upper and lower bounds of the output. This is another benefit of our approach since the requirement for known output bounds have been one of the main drawbacks of practical universal approximation problems. Both of the benefits stem from the local support property, which is specific to splines.

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

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.426-426
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• 2000

When real robot manipulators arc mathematically modeled, uncertainties are not avoidable. The uncertainties are often nonlinear and time varying, The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance and etc. We proposed a class of robust hybrid position/force control of manipulators and provided the stability analysis in the previous work. In the work, we propose a class of adaptive robust hybrid position/force control of manipulators with bound estimation and the stability based on Lyapunov function is presented. Especially, this controller does not need the information of uncertainty bound. The simulation results are provided to show the effectiveness of the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.980-984
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• 2001

When robot manipulatros as mathematically modeled. uncetainties may not be avoided. The uncertain factors come from imperfect knowledge of system parameters, payload change. friction, external disturbance and etc. In this work, we proposed a class of robust hybrid control of manipulatosrs. We propose a class of expanded robust hybrid control with the separated bound function and the simulation results are provided to show the effectiveness of the algorithm.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2320-2322
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• 2001

This paper describes the design of a robust adaptive neural-net(NN) observer for uncertain nonlinear dynamical system. The Lyapunov synthesis approach is used to guarantee a uniform ultimate boundedness property of the state estimation error, as well as of all other signals in the closed-loop system. Especially, for reducing the dynamic oder of the observer, we propose a new method in which no strictly positive real(SPR) condition is needed with on-line estimation of weights of the NNs. No a priori knowledge of an upper bounds on the uncertain terms is required. The theoretical results are illustrated through a simulation example.

• Korean Medical Education Review
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• v.16 no.3
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• pp.111-118
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• 2014

Over the last century, medical education in North America has evolved by identifying educational challenges within its own socio-cultural context and by appropriately responding to these challenges. A discipline-based curriculum, organ-system or integrated curriculum, problem-based curriculum, and competency-based curriculum are historical examples of the educational solutions that have been developed and refined to address specific educational challenges, such as students' lack of basic scientific knowledge, lack of integration between scientific knowledge and clinical practice, and lack of clinical practice. In contrast, Korean medical education has evolved with the influence of two forces: (1) the adoption of educational solutions developed in North America by pioneers who have identified urgent needs for medical education reform in Korea over the last three decades, and (2) the revitalization of Korean medical schools' curricula through medical education accreditation and national medical licensing examination. Despite this progressive evolution in Korean medical education, we contend that it faces two major challenges in order to advance to the next level. First, Korean medical education should identify its own problems in medical education and iteratively develop educational solutions within its own socio-cultural context. Secondly, to raise reflective doctors who have scientific knowledge and professional commitment to deal with different types of medical problems within a continuum from well-defined to ill-defined, medical education should develop innovative ways to provide students with a balanced spectrum of clinical problems, including uncertain, ill-defined problems.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.672-674
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• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.61-69
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• 2001

We present a probabilistic reasoning method for inferring knowledge about mathematical truth before an automated theorem prover completes a proof. We use a Bayesian analysis to update beleif in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Knowledge Management Research
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• v.19 no.2
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• pp.163-185
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• 2018

In order to solve problems in an uncertain and complicated management environment of the modern world, a creative solution that combines diverse perspectives, knowledge, and effort based on diversity within an organization is required. Smart work environment provides an opportunity to express the potential diversity of an individual, extending the source of ideas to the organization, enhancing communication, and linking and sharing information and knowledge. So, this results in increased creative behavior. The purpose of this study is to investigate the effect of the process of connecting and sharing information and knowledge of organizational members on creative behavior and the effect of smart work environment in the process. The purpose of this study is to identify roles of job autonomy, self-efficacy, knowledge sharing and smart work environment in creative process. For the study, 353 surveys with work use group(156 people) and unused group(197 people). Statistical analysis included validity and reliability analysis, structural model analysis. The results showed that self-efficacy and job autonomy had positive effects on creative behavior and knowledge sharing, and job autonomy had a positive effect on self-efficacy. Knowledge sharing has a positive effect on creative behavior, and mediates the relationship between self-efficacy, job autonomy and creative behavior. Particularly, knowledge sharing has a more positive effect on smart work use group. In case of smart work use group, self-efficacy and job autonomy have a relatively high influence on knowledge sharing rather than direct influence on creative behavior appear. This result implies that the achievements of smart work are revealed in terms of knowledge sharing and creative behavior.