• 한국전산유체공학회:학술대회논문집
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• 2007.04a
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• pp.159-162
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• 2007

This study presents a numerical procedure to optimize the shape of a staggered dimpled surface to enhance the turbulent heat transfer in a rectangular channel. A optimization technique based on neural network is used with Reynolds-averaged Navier-Stakes analysis of the fluid flow and heat transfer with Shear Stress Transport turbulence model. The dimple depth-to-dimple print diameter ratio, channel height-to-dimple print diameter ratio, and dimple print diameter-to-pitch ratio are chosen as design variables. The objective function is defined as a linear combination of terms related to heat transfer and friction loss with a weighting factor. Latin Hypercube Sampling is used to determine the training points as a mean of the Design of Experiment. Optimal values of the design variables were obtained in a range of the weighting factor.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.335-354
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• 2014

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.907-908
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• 2006

We describe a method for localizing a mobile robot in its working environment using a vision system and Virtual Reality Modeling Language (VRML). The robot identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

• IE interfaces
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• v.10 no.3
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2492-2494
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• 2000

Design of Fuzzy cotroller consists of intuition of human expert, and any other information about how to control system. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we designed simply a fuzzy controller based on human knowledge, but it has errors showing some vibrations. So we updated the optimal parameters of fuzzy controller using Neural Network algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.12
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• pp.954-962
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• 2008

This study presents a numerical procedure to optimize the shape of cylindrical cooling hole to enhance film-cooling effectiveness. The RBNN method is used as an optimization technique with Reynolds-averaged Navier-Stokes analysis of fluid flow and heat transfer with shear stress transport turbulent model. The hole length-to-diameter ratio and injection angle are chosen as design variables and film-cooling effectiveness is considered as objective function which is to be maximized. Twelve training points are obtained by Latin Hypercube Sampling for two design variables. In the sensitivity analysis, it is found that the objective function is more sensitive to the injection angle of hole than the hole length-to diameter ratio. Optimum shape gives considerable increase in film-cooling effectiveness.

• International Journal of Fluid Machinery and Systems
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• v.3 no.1
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• pp.29-38
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• 2010

This paper presents a procedure for the design optimization of a centrifugal compressor. The centrifugal compressor consists of a centrifugal impeller, vaneless diffuser and volute. And, optimization techniques based on the radial basis neural network method are used to optimize the impeller of a centrifugal compressor. The Latin-hypercube sampling of design-of-experiments is used to generate the thirty design points within design spaces. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximations and solved on hexahedral grids to evaluate the objective function of the total-to-total pressure ratio. Four variables defining the impeller hub and shroud contours are selected as design variables in this optimization. The results of optimization show that the total-to-total pressure ratio of the optimized shape at the design flow coefficient is enhanced by 2.46% and the total-to-total pressure ratios at the off-design points are also improved significantly by the design optimization.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.21-23
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• 1994

The degree of forecast accuracy with BP-algorithm largely depends upon the neuron number in hidden layer. In order to construct the optimal structure, first, we prescribe the error bounds of learning procedure, and then, we provid the method of incrementing the number of hidden neurons by using the derivative of errors with respect to an output neuron weights. For the case study, we apply the proposed method to forecast the component-wise residential load, and compare this results to that of time series forecasting.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.281-294
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• 2021

In this paper, we propose an adaptive regression method based on the single-layer neural network structure. We adopt a symmetric activation function as units of the structure. The activation function has a flexibility of its form with a parametrization and has a localizing property that is useful to improve the quality of estimation. In order to provide a spatially adaptive estimator, we regularize coefficients of the activation functions via ℓ₁-penalization, through which the activation functions to be regarded as unnecessary are removed. In implementation, an efficient coordinate descent algorithm is applied for the proposed estimator. To obtain the stable results of estimation, we present an initialization scheme suited for our structure. Model selection procedure based on the Akaike information criterion is described. The simulation results show that the proposed estimator performs favorably in relation to existing methods and recovers the local structure of the underlying function based on the sample.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.