• Tunnel and Underground Space
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• v.14 no.6 s.53
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• pp.399-410
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• 2004

The utilization of AS(Artificial Intelligence) and Database could be considered as an useful access for the application of underground information from the point of a geotechnical methodology. Its detailed usage has been recently studied in many fields of geo-sciences. In this paper, the target of usage is on controlling the injection of grout which more scientific access is needed in the grouting that has been used a major method in many engineering application. As the proposals for this problem it is suggested the methodology consisting of a fuzzy-neural hybrid system and a database. The database was firstly constructed for parameters dynamically varied according to the conditions of rock mass during the injection of grout. And then the conceptional model for the fuzzy-neural hybrid system was investigated fer optimally finding the controlling range of the grout valve. The investigated model applied to four cases, and it is found that the controlling range of the grout valve was reasonably deduced corresponding to the mechanical phenomena occurred by the injection of grout. Consequently, the algorithm organizing the fuzzy-neural hybrid system and the database as a system can be considered as a tool for controlling the injection condition of grout.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.959-981
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• 2017

Existing Android malware detection approaches mostly have concentrated on superficial features such as requested or used permissions, which can't reflect the essential differences between benign apps and malware. In this paper, we propose a quantitative calculation model of application risks based on the key observation that the essential differences between benign apps and malware actually lie in the way how permissions are used, or rather the way how their corresponding permission methods are used. Specifically, we employ a fine-grained analysis on Android application risks. We firstly classify application risks into five specific categories and then introduce comprehensive risk, which is computed based on the former five, to describe the overall risk of an application. Given that users' risk preference and risk-bearing ability are naturally fuzzy, we design and implement a fuzzy logic system to calculate the comprehensive risk. On the basis of the quantitative calculation model, we propose a risk classification based approach for Android malware detection. The experiments show that our approach can achieve high accuracy with a low false positive rate using the RandomForest algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1792-1795
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• 1997

It is difficulte to control in the flow system because there are many disturbance. So it is impossible to control delicately sometimes by PI or PID. In this paper, we study on the application of intellignet control algorithms such as 2DOF PID control, neural network, Fuzzy contro, Relay feedback to the flow control system. the resultings are 2DOF-PID control is more good response.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.42-49
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• 2000

This paper presents the application of a FARMA controller to stabilization of nonlinear Two-Generator Five-Bus power Systems. The control rules and the membership functions of the FARMA controller are generated automatically without using any plant model high complexity and severe nonlinearity of power systems are introduced and two-Machine Five -Bus Power system stabilization problem is formulated. The simulation results demonstrate the effectiveness and application possibility of the FARMA controller to the control problem of high order and nonlinear plants.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1106-1108
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• 1993

This paper deals with an industrial application of a fuzzy feedback combined learning control to an industrial batch free radical polymerization reactor. As a result, the plant has reduced the batch reaction time by 50 minute and stabilized both by 40 percent reduction of the standard deviations of product qualities, such as the total solid content and the graft gum, and by 45 percent reduction of the standard deviation of the batch reaction end time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.373-378
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• 2002

In fuzzy inferencing, most of conventional fuzzy controllers have problems of speed down in floating point operations of fuzzy membership functions in (0,1) as compared with integer operations. Therefore, in this paper, we propose a high-speed fuzzy controller with only integer operations. In this, for fast fuzzy computations, we use a scan line conversion algorithm to convert lines of each fuzzy linguistic term to the set of the closest integer pixels. We also implement a GUI (Graphic User Interface) application program for the convenient environments to modify and input fuzzy membership functions.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.569-573
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• 2001

An approximate representation of discrete functions {f$_{i,j}\mid$｜i, j=-1, 0, 1, …, N+1}in two variables by a fuzzy system is described. We use the cubic B-splines as fuzzy sets for the input fuzzification and spike functions as the output fuzzy sets. The ordinal number of f$_{i,j}$ in the sorted list is taken to be the out put fuzzy set number in the (i, j) th entry of the fuzzy rule table. We show that the fuzzy system is an exact representation of the cubic spline function s(x, y)=$\sum_{N+1}^{{i,j}=-1}f_{i,j}B_i(x)B_j(y)$ and that the approximation error S(x, y)-f(x, y) is surprisingly O($h^2$) when f(x, y) is three times continuously differentiable. We prove that when f(x, y) is a gray scale image, then the fuzzy system is a smoothed representation of the image and the original image can be recovered exactly from its fuzzy system representation when it is a digitized image.e.

• 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.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.3 no.2
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• pp.31-37
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• 2007

The fuzzy control algorithm for HVAC system has been developed for minimizing energy consumption while maintaining the comfort of indoor thermal environment in terms of the environmental variables such as time varying indoor cooling load and outdoor temperatures. The optimal set-points of control parameters with fuzzy control are supply air temperature, chilled water temperature and condenser temperature. This study has been done by using TRNSYS program in order to analyze the HVAC system response. As a result, the fuzzy control algorithm with PID algorithm shows good energy performance in comparison with conventional one.