• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.449-454
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• 2000

A new fuzzy logic control design algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is widely used for design of experiments in statistics and industrial engineering. The essence of the algorithm is to introduce Nth-certainty factor defined from the F-value of the ANOVA(analysis of variance) table, in order to effectively exclude the less confident rules. The proposed algorithm with multi-objective decision table(MODT) is found to be capable of the detection of inconsistency and the rule classification, reduction and modification. It is also shown that the algorithm can be successfully applied to the fuzzy controller design of an active magnetic bearing system.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.2
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• pp.127-132
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• 2001

In turning, the chip may be produced in the form of continuous chip or discontinuous chip. The continuous chips are dangerous to the operator and difficult to be handled at high speed machining. The signal of AE(Acoustic Emission) is found out to be related to cutting conditions, tool materials, test conditions and tool geometry in turning. In this study, the relationship between AE signal and chip form was experimentally investigated. The experimental results show that the types of chip form are possible to be classified from the AE signal using fuzzy logic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.356-361
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• 2006

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault detection method is applied to an inverted pendulum system and computer simulations are performed.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.17-22
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• 2024

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.120-125
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• 2016

Usually, a risk can be expressed as a product of likelihood and consequence of a hazard factor. Therefore, conventional risk assessment is carried out by frequency analysis and severity analysis, in turns. However, it is well known that intuitive thinking is another excellent way of thinking of human beings. This study aimed to confirm whether there exist any difference in risk assessment results derived by two different procedures - intuitive and analytical. Thus, the present study showed 10 different illustrations to 30 undergraduate students. Their responses were organized as fuzzy membership functions, and summarized as risk assessments, and compared. The results were also verified with the help of statistical hypothesis testing, which showed no significant difference. On the contrary, however, similarity measure used in fuzzy set theory was not credible as anticipated. Many cases failed to satisfy statistical hypothesis even with similarity measure higher than 0.60 so that only a trend could be accepted. In addition, a subject showed a somewhat consistent logical discrepancy in his response, which implied the necessity of sincere analysis in fuzzy formulations.

• Tunnel and Underground Space
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• v.14 no.4
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• pp.275-286
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• 2004

It is studied to select the reinforcement method of an abandoned tunnel which are intersected under the new roadway line. In the various decision makings, the reasonability for the reinforcement method of an abandoned tunnel was estimated using the pair-wise comparison and the fuzzy approximate reasoning to simplify the process of survey research. And there is reflected all the qualitative and quantitative characterizations by investigation items. In order to select the reinforcement method of an abandoned tunnel, 4 characteristic factors of construction, economical efficiency, safety and maintenance were used. Using the simple survey research and pair-wise comparison matrix, the weight of 4 factors was decided. The fuzzy approximate reasoning was used to calculate the quantitative value of each factor And then reflecting each weight to these results, the final reinforcement method of an abandoned tunnel could be determined.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.56-61
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• 2001

Activated sludge processes are broadly used in the biological wastewater treatment processes. The activated sludge processes are complex systems because of the many factors such as the variation of influent flowrate and ingredients, the complexity of biological reactions, and the various operation conditions. The main motivation o this research is to develop an intelligent control strategy for activated sludge process (ASP). ASP is a complex and nonlinear dynamic system owing to the characteristic of wastewater, the change in influent flowrate, weather conditions, and so on. The mathematical model of ASP also includes the uncertainty which is a ignored or unconsidered factor from process designers. The ASP model based on Matlabⓡ/Simulinkⓡ is developed in this paper. And the model performance is examined by IWA (International Water Association) and COST (European Cooperation in the filed of Scientific and Technical Research) data. The model tests derive steady-state results of 14 days. In this paper, fuzzy logic control approach is applied to handle DO concentrations. The fuzzy logic controller includes two inputs and one output to adjust air flowrate. The objective function for the optimization, in the implemented evolutionary strategy, is formed with focusing on improving the effluent quality and reducing the operating cost.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.167-172
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• 2006

As ubiquitous devices are fast spreading, the communication problem between humans and these devices is on the rise. The use of context is important in interactive application such as handhold and ubiquitous computing. Context is not crisp data, so it is necessary to introduce the fuzzy concept. The proxity relation is represented by the degree of closeness or similarity between data objects of a scalar domain. A context manager of context-awareness system evaluates imprecise queries with the proximity relations. in this paper, a systematic proximity elicitation method are proposed. The proposed generation method is simple and systematic. It is based on the well-known fuzzy set theory and applicable to the real world applications because it has tuning parameter and weighting factor. The proposed representations of proximity relation is more efficient than the ordinary matrix representation since it reflects some properties of a proximity relation to save space. We show an experiments of quantitative calculate for the proximity relation. And we analyze the time complexity and the space occupancy of the proposed representation method.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.