• The Journal of Information Technology
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• v.5 no.4
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• pp.57-64
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• 2002

The system that is thermal test system for elements has been controlled generally by PID algorithm because of its characteristic. There is not a mathematical model for the system. So the system that is use the PID controller is not properly operated. To solve this problem, we propose a fuzzy algorithm that parameters and rule base is selected by self-searched algorithm for each system. The input fuzzy membership function is adapted based on the set stable range. Output membership function is nearly fixed but some parameter is adjustable. The rule base is changed under basis on the system response. The output value computed through inference and defuzzification is mapped into a value that is proper for the system operation. Through this regulation, it will be possible to prevent the temperature of system to go into the unstable temperature.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.1-22
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• 1995

본 연구에서 퍼지인식도(Fuzzy Cognitive Map) 개념을 기초로 하여 (1) 특정 문제영역에 대한 전문가의 인과관계 지식(causal knowledge)을 추출하는 알고리즘을 제시하고, (2) 이 알고리즘에 기초하여 작성된 해당 문제영역에 대한 여러 전문가들의 인과관계 지식을 계층별로 분해하여, (3) 해당 계층간의 양방향 추론이 가능한 추론메카니즘을 제시하고자 한다. 특정 문제영역에 있어서의 인과관계 지식이란 해당 문제를 구성하는 여러 개념간에 존재하는 인과관계를 표현한 지식을 의미한다. 이러한 인과관계 지식은 기존의 IF-THEN 형태의 규칙과는 달리 행렬형태로 표현되기 때문에 수학적인 연산이 가능하다. 특정 문제영역에 대한 전문가의 인과관계 지식을 추출하는 알고리즘은 집합연산에 의거하여 개발되었으며, 특히 상반된 의견을 보이는 전문가들의 의견을 통합하여 하나의 통합된 인과관계 지식베이스를 구축하는데 유용하다. 그러나, 주어진 문제가 복잡하여 다양한 개념들이 수반되면, 자연히 인과관계 지식베이스의 규모도 커지게 되므로 이를 다루는데 비효율성이 개재되기 마련이다. 따라서 이러한 비효율성을 해소하기 위하여 주어진 문제를 여러계측(Hierarchy)으로 분해하여, 해당 계층별로 인과관계 지식베이스를 구축하고 각 계층별 인과관계 지식베이스를 연결하여 추론하는 메카니즘을 개발하면 효과적인 추론이 가능하다. 이러한 계층별 분해는 행렬의 분해와 같은 개념으로도 이해될 수 있다는 특징이 있어 그 연산이 간단명료하다는 장점이 있다. 이와같이 분해된 인과관계 지식베이스는 계층간의 추론메카니즘을 통하여 서로 연결된다. 이를 위하여 본 연구에서는 상향 또는 하향방식이 추론이 가능한 양방향 추론방식을 제시하여 주식시장에서의 투자분석 문제에 적용하여 그 효율성을 검증하였다.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1964-1966
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• 2001

Active Noise control(ANC) is rapidly becoming the most effective way to reduce noises that can otherwise be very difficult and expensive to control. This research presents ANFIS (Adaptive Network Fuzzy Inference System) controller for adaptively noise cancelling in a duct. ANC system generates secondary control sound pressure with same amplitude and with opposite phase as noise to be eliminated. ANFIS controller is trained to optimize its parameters for adaptively cancelling noise. That is ANFIS train its parameters by gradient descent and LSE method so called hybrid method. This paper present ANFIS in active noise control which provides an improvement convergence speed and limitation of linearity condition. It can model nonlinear functions of arbitrary complexity and ANFIS can construct an input-ouput mapping based on both human knowledge in the form of Takagi and Sugeno's fuzzy if-then rules and stipulated input-output data pairs. This paper also shows that the proposed ANFIS active noise control system successfully cancelled noise.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.129-140
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• 1998

An interface system was developed to offer the welding capability to a robot controller which had not any embedded function for arc welding before, and also an arc sensor algorithm was proposed for weld seam tracking of the welding robot. For the interface system between the robot controller and welding equipments, data communication software and interface connections were composed. The interface system was mae to correspond welding condition, correction data, operation sequence and current status with the robot controller by mutual had shaking and digital signal transfer. Graphic user interface program developed under the environment of windows made it easy to monitor data communication and operation status, and to control welding and sensing sequence. Arc sensing algorithm proposed in this study to compensate torch position error was based on a fuzzy logic with the variables of current difference and current differenced change at torch weaving extremities. The developed interface system could be successfully implemented in between welding equipments and the robot controller, and showed normal status and exact function in data and signal communication between the systems. The whole robot welding system was then examined to verify its welding and seam tracking capabilities in horizontal fillet, vertical fillet, and 3-dimensional fillet weldment. The experiments revealed sound weld bead shapes and also good seam tracing results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.142-145
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• 2019

In recent years, the global GEF(Green Energy Family) activities to preserve the global environment due to energy consumption have been implemented under the Kyoto Protocol for the Prohibition of Carbon Dioxide Emissions, RoHS (Restriction of Hazardous Substances directive), and WEEE(Waste Electrical and Electronice Equipment) are required to collect waste for the purpose of minimizing waste by integrating lighting and communication. In this paper, we constructed a controller that can control the illumination of RGB LED module by using fuzzy inference system and checking environmental factors(Illumination, distance to the subject, etc.) using microprocessor in real time.

• Steel and Composite Structures
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• v.43 no.5
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• Advances in nano research
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• v.12 no.4
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• pp.375-386
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• 2022

Synthesis of acrylate-based dispersion resins involves many parameters including temperature, ingredients concentrations, and rate of adding ingredients. Proper controlling of these parameters results in a uniform nano-size chain of polymer on one side and elimination of hazardous residual monomer on the other side. In this study, we aim to screen the process parameters via Internet of Things (IoT) to ensure that, first, the nano-size polymeric chains are in an acceptable range to acquire high adhesion property and second, the remaining hazardous substance concentration is under the minimum value for safety of public and personnel health. In this regard, a set of experiments is conducted to observe the influences of the process parameters on the size and dispersity of polymer chain and residual monomer concentration. The obtained dataset is further used to train an Adaptive Neural network Fuzzy Inference System (ANFIS) to achieve a model that predicts these two output parameters based on the input parameters. Finally, the ANFIS will return values to the automation system for further decisions on parameter adjustment or halting the process to preserve the health of the personnel and final product consumers as well.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.17-22
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• 2001

Because the quantitative relationships between welding parameters and welding result are not yet blown, optimal values of welding parameters for $CO_2$ robotic arc welding is a difficult task. Using the various artificial data processing methods may solve this difficulty. This research aims to develop an expert system for $CO_2$ robotic arc welding to recommend the optimal values of welding parameters. This system has three main functions. First is the recommendation of reasonable values of welding parameters. For such work, the relationships in between the welding parameters are investigated by the use of regression analysis and fuzzy system. The second is the estimation of bead shape by a neural network system. In this study the welding current voltage, speed, weaving width, and root gap are considered as the main parameters influencing a bead shape. The neural network system uses the 3-layer back-propagation model and a generalized delta rule as teaming algorithm. The last is the optimization of the parameters for the correction of undesirable weld bead. The causalities of undesirable weld bead are represented in the form of rules. The inference engine derives conclusions from these rules. The conclusions give the corrected values of the welding parameters. This expert system was developed as a PC-based system of which can be used for the automatic or semi-automatic $CO_2$ fillet welding with 1.2, 1.4, and 1.6mm diameter the solid wires or flux-cored wires.

• Structural Engineering and Mechanics
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• v.82 no.4
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• pp.445-458
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• 2022

Some of the control systems used in engineering structures that use sensors and decision systems have some time delay reducing efficiency of the control system or even might make it unstable. In this research, in addition to considering the effect of the time delay in vibration control process, predictive control is used to compensate the time delay. A semi-active vibration control approach with the help of magneto-rheological dampers is implemented. In addition to using fuzzy inference system to determine the appropriate control voltage for MR damper, structural behavior prediction system and specifying future responses are also used such that the time delays occurring within control process are overcome. For this purpose, determination of prediction horizon is conducted for one, five, and ten steps ahead for single degree of freedom structures with periods ranging from 0.1 to 4 seconds, subjected to twenty earthquake excitations. The amount of time delay applied to the control system is 0.1 seconds. The obtained results indicate that for 0.1 second time delay, average prediction error values compared to the case without time delay is 3.47 percent. Having 0.1 second time delay in a semi-active control system reduces its efficiency by 11.46 percent; while after providing the control system with structure behavior prediction, the difference in the results for the control system without time delay is just 1.35 percent on average; indicating a 10.11 percent performance improvement for the control system.