• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.365-376
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• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.110-117
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• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.6
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• pp.747-754
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• 2004

The nonlinear finite element analysis for deformation characteristics of a power steering hose during the swaging process is performed in order to investigate the stress and the strain levels of the hose components. Power steering hose consists of components such as rubber hose, nylon, nipple and sleeve. Moreover, the numerical analysis requires the consideration of material, geometry and boundary nonlinearities. To evaluate the rubber hose strength, the measured stresses and strains are compared with tension and compression test data. Contact force is also a principal factor to examine whether rubber hose is break away from sleeve and nipple or not.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.149-151
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• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.902-909
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• 2020

It is announced a new procedure for the real-time overall hull response monitoring system depends on inclinometer sensor data. The procedure requires a few inclinometer sensors' data, located on the deck. Sensor data is used to obtain curvature values; and curvature values are used to find out displacements or relevant moment values according to pre-calculated moment-curvature diagrams. Numerical studies are demonstrated with reasonable accuracy for the pre-ultimate and the post-ultimate nonlinear behaviors. Elastic, inelastic, and post-collapse structural bending moment capacity determination of the hull has been presented. The proposed inverse engineering technique will be able to see the response of the hull in real-time with high accuracy to manage the course and speed when cruising or control the loading and the unloading process at the port.

• Steel and Composite Structures
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• v.44 no.4
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• pp.545-555
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• 2022

Empirical relationships that capture the nonlinear behavior of headed steel shear stud anchors have been derived from standard push out tests, where the specimens are comprised of large wide flanged steel sections attached to flat concrete slabs via the anchors. However, many composite systems used in practice utilize much smaller steel members and/or steel decking as part of the slab system. Composite open web steel joist systems generally include both of these elements and consequently the nonlinear performance ofthe anchor is not accurately represented by existing models. In this paper, a new empirical relation is presented for open web steel joist systems based on experimental results from a modified push out test that more realistically represent a composite open web steel joist system. The methodology for obtaining the proposed nonlinear function where the response of the system is characterized by two parameters(α and β) is presented. The two-step process for obtaining the two parameters is described and the empirical relation is calibrated with the experimental data. In comparison with existing expressions, the new proposal herein more accurately predicts the high initialstiffness of the system and overall nonlinear system performance.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.04a
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• pp.155-162
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• 1994

The drying shrinkage of concrete has a close relation to the water movement, Since the diffusion process of water in concrete is strongly dependent on the temperature and pore humidity, the process is highly nonlinear phenomena. It is shown that the analytical results of this study are in good agreement with experimental data in the literatures, and results calculated by BP-KX model. The internal stress caused by moisture distribution which was resulted from the diffusion process, was calculated quantitatively. The tensile stress which occurred in the drying outer zone mostly exceeded the tensile strength of concrete, and necessarily would result in crack formation.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.133-136
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• 2000

In recent years, hybrid neural network approaches which combine neural networks and mechanistic models have been gaining considerable interests. These approaches are potentially very efficient to obtain more accurate predictions of process dynamics by combining mechanistic and neural models in such a way that the neural network model properly captures unknown and nonlinear parts of the mechanistic model. In this work, such an approach was applied in the modeling of a full-scale coke wastewater treatment process. First, a simplified mechanistic model was developed based on the Activated Sludge Model No.1 and the specific process knowledge, Then neural network was incorporated with the mechanistic model to compensate the errors between the mechanistic model and the process data. Simulation and actual process data showed that the hybrid modeling approach could predict accurate process dynamics of industrial wastewater treatment plant. The promising results indicated that the hybrid modeling approach could be a useful tool for accurate and cost-effective modeling of biochemical processes.