• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.427-432
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• 2005

This paper is concerned with a simultaneous design of TSK (Takagi-Sugeno-Kang)-linguistic fuzzy models with uncertain model output and the computationally efficient representation. For this purpose, we use the fundamental idea of linguistic models introduced by Pedrycz and develop their comprehensive design framework. The design process consists of several main phases such as (a) the automatic generation of the linguistic contexts by probabilistic distribution using CDF (conditional density function) and PDF (probability density function) (b) performing context-based fuzzy clustering preserving homogeneity based on the concept of fuzzy granulation (c) augment of bias term to compensate bias error (d) combination of TSK and linguistic context in the consequent part. Finally, we contrast the performance of the enhanced models with other fuzzy models for automobile MPG predication data and coagulant dosing process in a water purification plant.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.459-465
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• 2001

The Prefabricated Vertical Drain(PVD) method is most widely used technique to accelerate the consolidation process and to strengthen the weak clayey soil in situ. Uncertainty in the consolidation process via the Prefabricated Vertical Drain(PVD), and the effects of uncertainty on the design of PVDs, are investigated in this paper, Among the effect factors, the coefficient of horizontal(radial) consolidation, C$\sub$h/, is the most important and sensitivity analysis of the degree of consolidation with respect to the other effect factors are carried out. For the reliable determination of uncertain quantities, the laboratory and in-situ tests are carried out. Henceforth, probability analysis that take the uncertainty into account are executed and reliable design method is provided in practice.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.159-162
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• 2009

Today's construction is more various and more complex. Because of that, a lot of uncertain factors are occurred and they related uncertain construction duration. For management complex architecture project, importance of construction schedule management also increased. In previous studies, one of solutions to overcome those problems is suggested. It was BIM based construction simulation process which focused on construction schedule and construction schedule management. But latest process had limited point which has no duration estimation function. So this paper suggested duration estimation method and developed duration estimation module. Duration estimation module developed with current scheduling tool MS Project and their macro function. However, this module has just developed Reinforced Concrete Structure and has to do more development and research.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.369-374
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• 2006

Quantification of infiltration rate is an important issue in HVAC system design. The infiltration in buildings depends on many uncertain parameters that vary with significant magnitude and hence, the results from standard deterministic simulation approach can be unreliable. The authors utilize uncertainty analysis In predicting the airflow rates. The paper presents relevant uncertain parameters such as meteorological data, building parameters (leakage areas of windows, doors, etc.), etc. Uncertainties of the aforementioned parameters are quantified based on available data from literature. Then, the Latin Hypercube Sampling (LHS) method was used for the uncertainty propagation. The LHS is one of the Monte Carlo simulation techniques that is suited for our needs. The CONTAMW was chosen to simulate infiltration phenomena in a residential apartment that is typical of residential buildings in Korea. It will be shown that the uncertainty propagating through this process is not negligible and may significantly influence the prediction of the airflow rates.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.317-327
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• 2024

Along with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.93-101
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• 2007

In general, geotechnical properties have many uncertain aspects, thus probabilistic analysis have been used to consider these aspects. It is, however, quite difficult to select an appropriate target probability for a certain structure or construction process. In this study, minimum expected cost design method based on probabilistic analysis is suggested for design of vertical drains generally used to accelerate consolidation in soft clayey soils. A sensitivity analysis is performed to select the most important uncertain parameters for the design of vertical drains. Monte Carlo simulation is used in sensitivity analysis and probabilistic analysis. Total expected cost, defined as the sum of initial cost and expected additive cost, varies widely with variation of input parameters used in design of vertical drain systems. And probability of failure to get the minimum total expected cost varies under the different design conditions. A minimum value of total expected cost is suggested as a design value in this study. The proposed design concept is applicable to unit construction process because this approach is to consider the uncertainties using probabilistic analysis and uncertainties of geotechnical properties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.136-139
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• 2001

This paper develops a sensor based navigation method that utilizes fuzzy logic and the Dempster-Shafer evidence theory for mobile robot in uncertain environment. The proposed navigator consists of two behaviors: obstacle avoidance and goal seeking. To navigate reliably in the environment, we make a map building process before the robot finds a goal position and create a robust fuzzy controller. In this paper, the map is constructed on a two-dimensional occupancy grid. The sensor readings are fused into the map using D-S inference rule. Whenever the robot moves, it catches new information about the environment and replaces the old map with new one. With that process the robot can go wandering and finding the goal position. The usefulness of the proposed method is verified by a series of simulations. This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.43-51
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• 2004

During process design or process optimization, it is quite common for experimenters to find optimum operating conditions for several responses simultaneously. The traditional multiresponse surfaces optimization methods do not consider the uncertain relationship among these responses sufficiently. For this reason, the authors propose an optimization method based on evidential reasoning theory by Dempster and Shafer. By maximizing the basic probability assignment function, which indicates the degree of belief that certain operating condition is the solution of this multiresponse surfaces optimization problem, the desirable operating condition can be found.