• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.10
• /
• pp.141-151
• /
• 1999

The resistance Spot Welding is widely used in the field of assembling the plates. However we don't still have any satisfactory solution, which is non-destructive quality evaluation in real-time or on-line, against it. Moreover, even though the rate of welding under the condition of expulsion has been high until now, quality control of welding against expulsion hasn't still been established. In this paper, it was proposed on the quality assurance technique of resistance spot welding pieces using Neuro-Fuzzy algorithm. Four parameters from electrode separation signal in the case of non-expulsion, and dynamic resistance patterns in the case of expulsion are selected as fuzzy input parameters. The parameters consist of Fuzzy Inference System are determined through Neuro-Learning algorithm. And then, fuzzy Inference System is constructed. It was confirmed that the fuzzy inference values of strength have within ${\pm}$4% error specimen in comparison with real strength for the total strength range, and the specimen percent having within ${\pm}$1% error was 88.8%. According to KS(Korean Industrial Standard), tensile-shear strength limit for electric coated of zinc is 400kgf/mm2. Judging to the quality of welding is good or bad, according to this criterion and the results of inference, the probability of misjudgement that good quality is valuated into poor one was 0.43%, on contrary it was 2.59%. Finally, the proposed Neuro-Fuzzy Inference System can infer the tensile-shear strength of resistance spot welding pieces with high performance for all cases-non-expulsion and expulsion. And On-Line Welding Quality Inspection System will be realized sooner or later.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.4
• /
• pp.1025-1034
• /
• 1997

Inference process of resolution-based automated reasoning easily consumes the memory of computer without giving any useful result by priducing lots of fruioless information which are not necessary for the conslusion. This paper suggests a control strategy for saving the space of computer memory and reducing the inference time. The strategy uses a restriction that comparatively irrelevant axioms do mot take pare in the resoluition. In order to analyze and determine the priorities of the input axioms of joning the inference process, the system employs the fuzzy relational products.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.120-123
• /
• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.473-479
• /
• 2008

Generally, HDDs are widely used as data storage device in office, home and mobile machineries. So, it is used for various applications or tasks, such as file copy, file download, music and movie play etc., in various environment. In spite of this kind of varieties in tasks and environment in which HDDs perform, most commercial HDDs hardly control its operations adaptively to these varieties. Thus, it is preferred to optimize the performance and energy consumption of HDDs according to the task and/or the environment. So, this paper proposes a new fuzzy inference algorithm which adaptively controls HDDs operations and may also easily be implemented as the firmware of HDDs and run in the restricted environment such as embedded systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.952-955
• /
• 1996

It is difficult to determine the feeding rate of coagulant in water treatment process, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the fusion of genetic algorithms and fuzzy inference system was used in determining of feeding rate of coagulant. The genetic algorithms are excellently robust in complex operation problems, since it uses randomized operators and searches for the best chromosome without auxiliary information from a population consists of codings of parameter set. To apply this algorithms, we made the look up table and membership function from the actual operation data of water treatment process. We determined optimum dosages of coagulant (PAC, LAS etc.) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.1
• /
• pp.1-6
• /
• 2002

In this paper, we propose temperature inference system for indoor and outdoor temperature of the Air-Conditioner with limited sensors. The proposed system based on the network theory of biological immune system consists of indoor and outdoor temperature inference process. It is designed that on-line temperature inference is possible. This system is admirable for unlearned data as well as given input data by making efficient use of previous information.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.179.5-179
• /
• 2001

Generally, while PD-type fuzzy controller has good performance in transient period, it has uniform steady state error of response. To improve limitations of PD-type fuzzy controller, we propose a new fuzzy controller to improve the performance of transient response and to eliminate the steady state error of response. In this paper, PD-type fuzzy controller is used a simplified indirect inference method(SIIM). When the SIIM is applied, the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. The outputs of this controller are the output calculated by PD-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted by fuzzy rule according to the system state variables. To show the usefulness of the proposed controller, it is applied to 0-type 2nd-order linear system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.15-15
• /
• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.370-370
• /
• 2000

In this paper, we design a Multi-Fuzzy model by means of HCM clustering and genetic algorithms for a nonlinear system. In order to determine structure of the proposed Multi-Fuzzy model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy ate identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy mode] and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Communications for Statistical Applications and Methods
• /
• v.25 no.5
• /
• pp.559-568
• /
• 2018

Gene classification can involve complex order-restricted inference. Examining gene expression pattern across groups with order-restriction makes standard statistical inference ineffective and thus, requires different methods. For this problem, Roy's union-intersection principle has some merit. The M-estimator adjusting for outlier arrays in a microarray study produces a robust test statistic with distribution-insensitive clustering of genes. The M-estimator in conjunction with a union-intersection principle provides a nonstandard robust procedure. By exact permutation distribution theory, a conditionally distribution-free test based on the proposed test statistic generates corresponding p-values in a small sample size setup. We apply a false discovery rate (FDR) as a multiple testing procedure to p-values in simulated data and real microarray data. FDR procedure for proposed test statistics controls the FDR at all levels of ${\alpha}$ and ${\pi}_0$ (the proportion of true null); however, the FDR procedure for test statistics based upon normal theory (ANOVA) fails to control FDR.