• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.540-545
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• 2007

In this thesis, we introduce and investigate new architectures and comprehensive design methodologies of intelligent dimming converter and evaluate the proposed model and the system through a series of numeric experiments. Electronic ballast enable prolongation of life foy Fluorescent-Lamp and ballast. However, There are no merit in case that user impossible manual control. Therefore in this paper, we put emphasis on the design of electronic ballast based on intelligent dimming converter and the energy saving according to the day-light and the user settings by applying the intelligent model to a fluorescent lamp. Also, we show the superiority of the proposed Intelligent dimming converter through the evaluation of performance with conventional electronic ballast by applying the intelligent model to hardware of systems.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.287-293
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• 2021

Recently, public certificates issued by nationally-recognized certification bodies have been abolished, and internet companies have issued their own common certificates as certification authority. The Electronic Signature Act was amended in a way to assign responsibility to Internet companies. As the use of a joint certificate issued by Internet companies as a certification authority is allowed, it is expected that the fraud damage caused by the theft of public key certificates will increase. We propose an authentication model that can be used in a security gateway that combines PKI with a blockchain with integrity and security. and to evaluate its practicality, we evaluated the security of the authentication model using Sugeno's hierarchical fuzzy integral, an evaluation method that excludes human subjectivity and importance degree using Delphi method by expert group. The blockchain-based joint certificate is expected to be used as a base technology for services that prevent reckless issuance and misuse of public certificates, and secure security and convenience.

• Journal of IKEEE
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• v.28 no.1
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• pp.6-13
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• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• Structural Engineering and Mechanics
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• v.45 no.3
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• pp.323-336
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• 2013

With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.67-72
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• 2005

This paper aims to evaluates the operation efficiency with two inputs and four outputs with the use of DEA(Data Envelopment Analysis), a qualitative data analysis with the use of expert assessment in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). The tool for integrating heterogeneous data is model that applies fuzzy logic to decision support system In this paper, therefor, RCC/RSC evaluates the priority for operation efficiency. The result are found as order as Inchon, Mokpo, Jeju, Donghae, Busan, Pohang, Yosu, Sokcho, Tongyeong, Ulsan, Taean, Gunsan RSC.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.948-958
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• 2022

Since reliability and security of man-machine system increasingly depend on reliability of human, human reliability analysis (HRA) has attracted a lot of attention in many fields especially in nuclear engineering. Dependence assessment among human tasks is a important part in HRA which contributes to an appropriate evaluation result. Most of methods in HRA are based on experts' opinions which are subjective and uncertain. Also, the dependence influencing factors are usually considered to be constant, which is unrealistic. In this paper, a new model based on Dempster-Shafer evidence theory (DSET) and fuzzy number is proposed to handle the dependence between two tasks in HRA under uncertain and dynamic situations. First, the dependence influencing factors are identified and the judgments on the factors are represented as basic belief assignments (BBAs). Second, the BBAs of the factors that varying with time are reconstructed based on the correction BBA derived from time value. Then, BBAs of all factors are combined to gain the fused BBA. Finally, conditional human error probability (CHEP) is derived based on the fused BBA. The proposed method can deal with uncertainties in the judgments and dynamics of the dependence influencing factors. A case study is illustrated to show the effectiveness and the flexibility of the proposed method.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.5 s.45
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• pp.41-52
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• 2005

Using the variable control gain scheme on the basis of fuzzy-based decision-making process, Fuzzy supervisory control (FSC) technique exhibits better control performance than linear control technique with one static control gain. This paper demonstrates the effectiveness of the FSC technique by evaluating the robust performance of the FSC technique under the presence of uncertainties in the models and the excitations. Robust performance of the FSC system is compared with that of optimally designed LQG control system for the benchmark cable-stayed bridge presented by Dyke et al. Parameter studies on the robust performance evaluation are carried out by varying the stiffness of the bridge model as well as the magnitudes of several earthquakes with different frequency contents. From the comparative study of two control systems, FSC system shows the enhanced control performance against various magnitudes of several earthquakes while maintaining lower level of power required for controlling the bridge response. Especially, FSC system clearly guarantees the improved robust performance of the control system with stable reduction effects on the seismic responses and slight increases in total power and stroke for the control system, while LQG control system exhibits poor robust performance.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Journal of Korean Society of Forest Science
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• v.89 no.1
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• pp.33-40
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• 2000

This study was carried out to evaluate the fill slope failure possibility of forest road in Metamorphic rock area using fuzzy theory which is non-linear model. The results were summarized as follows. The potential slope failure by nine factors was mainly occurred under the such conditions as the total road width ranging from 4m to 5m, longitudinal gradients below $2^{\circ}$, fill slope length greater than 8m, fill slope gradients steeper than $40^{\circ}$, road on ridge position, soil types with weathered rock, slope gradients steeper than $40^{\circ}$, aspect of NW, and longitudinal slope form in convexity. The weight of importance by factors on fill slope failure was ranked in the order of fill slope length, fill slope gradient, road position, soil type, aspect and longitudinal slope form. The analysis showed that the fill slope failure possibility was low with less than 0.485 of the fuzzy integral value and high with more than 0.620 of the value. And the discriminant accuracy was 74.6%. The analysis with six out of nine factors indicated that the possibility was low with less than 0.441 of the fuzzy integral value and high with more than 0.583 of the value. In this case, the discriminant accuracy was slightly increased to 78.0%.