• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Explosives and Blasting
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• v.39 no.3
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• pp.44-64
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• 2021

With the upcoming 4^th industrial revolution era, the applications of artificial intelligence(AI) and big data in engineering are increasing. In the field of blasting, there have been various reported cases of the application of AI. In this paper, AI techniques, such as artificial neural network, fuzzy logic, generic algorithm, swarm intelligence, and support vector machine, which are widely applied in blasting area, are introduced, The studies about the application of AI for the prediction of ground vibration, rock fragmentation, fly rock, air overpressure, and back break are surveyed and summarized. It is for providing starting points for the discussion of active application of AI on effective and safe blasting design, enhancing blasting performance, and minimizing the environmental impact due to blasting.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.23-26
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• 2019

본 논문에서는 X-Ray 영상에서 용접한 부분의 기공이나 균열 등의 결함 영역을 추출하는 새로운 방법을 제안한다. 제안된 방법은 세라믹 X-Ray 영상에서 비등방성 확산 필터를 적용하여 영상의 잡음을 제거하고, 수직 및 수평 히스토그램을 각각 적용하여 용접 영역을 추출한 후, 최소 자승법을 적용하여 배경 밝기를 제거하고, 사다리꼴 형태의 Fuzzy Stretching기법을 적용하여 명암 값을 강조하여 결함 영역과 그 외의 영역간의 명암 대비를 강조한다. 그리고 Fuzzy C_Means 알고리즘을 적용하여 결함 영역을 세분화한 후, Fuzzy C_Means을 적용하여 생성된 클러스터들의 중심 명암 값을 이용하여 ${\alpha}_-cut$을 설정한 후에 임계구간을 구하고 영상을 이진화하여 최종적으로 결함 영역을 추출한다. 제안된 방법의 결함 추출 성능을 확인하기 위하여 세라믹 X-Ray 영상을 대상으로 실험한 결과, 기존의 방법보다 결함 영역이 정확히 추출되는 것을 확인할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.215-221
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• 2006

This paper is concerned with the control of multiple nonlinearities included in a humanoid robot system. A humanoid robot has some problems such as the structural instability, which leads to consider the control of multiple nonlinearities caused by driver parts as well as gear reducer. Saturation and backlash are typical examples of nonlinearities in the system. The conventional algorithms of backlash control were fuzzy algorithm, disturbance observer and neural network, etc. However, it is not easy to control the system by employing only single algorithm since the system usually includes multiple nonlinearities. In this paper, a switching Pill is considered for a control of saturation and a dual feedback algorithm is proposed for a backlash control. To implement the above algorithms, the system identification is firstly performed for the minimization of the difference between the results of simulation and experiment, and then the switching Pill gains are determined using genetic algorithm with some heuristic approach. The performance of the switching Pill controller for saturation and the dual feedback for backlash control is investigated through the simulation. Finally, it is shown that the implemented control system has good results and can be applied to the real humanoid robot system ISHURO.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.173-178
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• 2024

This study presents a technique for assessing the dimensional quality of assembly parts in Prefabricated Steel Structures (PSS) using a stereo vision sensor. The stereo vision system captures images and point cloud data of the assembly area, followed by applying image processing algorithms such as fuzzy-based edge detection and Hough transform-based circular bolt hole detection to identify bolt hole locations. The 3D center positions of each bolt hole are determined by correlating 3D real-world position information from depth images with the extracted bolt hole positions. Principal Component Analysis (PCA) is then employed to calculate coordinate axes for precise measurement of distances between bolt holes, even when the sensor and structure orientations differ. Bolt holes are sorted based on their 2D positions, and the distances between sorted bolt holes are calculated to assess the assembly part's dimensional quality. Comparison with actual drawing data confirms measurement accuracy with an absolute error of 1mm and a relative error within 4% based on median criteria.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.571-576
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• 2002

There have been a lot of considerations dealing with improving the performance of neuro-fuzzy system. The studies on the neuro-fuzzy modeling have largely been devoted to two approaches. First is to improve performance index of system. The other is to reduce the structure size. In spite of its satisfactory result, it should be noted that these are difficult to extend to high dimensional input or to increase the membership functions. We propose a novel neuro-fuzzy system based on the efficient clustering method for initializing the parameters of the premise part. It is a very useful method that maintains a few number of rules and improves the performance. It combine the various algorithms to improve the performance. The Expectation-Maximization algorithm of Gaussian mixture model is an efficient estimation method for unknown parameter estimation of mirture model. The obtained parameters are used for fuzzy clustering method. The proposed method satisfies these two requirements using the Gaussian mixture model and neuro-fuzzy modeling. Experimental results indicate that the proposed method is capable of giving reliable performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.17-26
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• 1998

Ultrasonic sensors are widely used in various applications due to advantages of low cost, simplicity in construction, mechanical robustness, and little environmental restriction in usage. But for the application of object recognition, ultrasonic sensors exhibit several shortcomings of poor directionality which results in low spatial resolution of objects, and specularity which gives frequent erroneous range readings. The time-of-flight(TOF) method generally used for distance measurement can not distinguish small object patterns of plane, corner or edge. To resolve the problem, an increased number of the sensors in the forms of a linear array or 2-dimensional array of the sensors has been used. Also better resolution has been obtained by shifting the array in several steps using mechanical actuators. Also simple patterns are classified based on analyzing signal reflections. In this paper we propose a method of a sensor array system with improved capability in pattern distinction using electronic circuits accompanying the sensor array, and intelligent algorithm based on neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. A set of different return signals from neighborhood sensors is manipulated to provide enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.49-59
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• 2004

In order to be a hub-port in Northeast Asia, current China government has intensively invested in port development. Further, this development Project is significantly big scale, compared with those projects which Korea and Japan have. Thus, China is beginning to threaten Korean ports, especially Busan port which try to be a hub port in Northeast Asia. For this reason, recently many studies to evaluate competitiveness between Korean ports, especially Busan and Gwangyang, and Chinese ports have been conducted. In the mean time, implications of those pervious research has mainly been based on evaluation of port competitiveness using evaluation methodologies, such as AHP(Analytical Hierarchy Process) and HFP(Hierarchical Fuzzy Process). However, as previous evaluation algorithms are methodologies that only calculate ranking of ports by competitiveness level, from the results of analysis, critical weak points affected current port competitiveness could not clearly fine out. That is, because there has not been any algorithm that can extract critical points from the evaluation results. The aim of this paper is to present critical points that affect port competitiveness using an algorithm based on IRFE(Inverse Relation of Fuzzy Evaluation), and scenario analysis, from previous results of evaluation of port competitiveness. And The research scope is to covey the subjective ports of Korea and China's 7 major ports (Busan, Gwangyang, Sanghai, Qingdao, Tienjin, Dalian and Kaoshuing). From analysis, it was found that critical weak point of Busan port is the level of hinterland including availability of free trade zone.

• Journal of Energy Engineering
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• v.25 no.3
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• pp.27-33
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• 2016

The heat recovery system that was applied in this study, is the energy-saving type that can produce the maximum cooling capacity less power in use. In order to have a more precise control function the temperature and humidity of the constant temperature and humidity machine, control algorithm is applied to designed a fuzzy PID controller, and the outside air compensation device (air-cooled) demonstrated excellent ability to dehumidify the moisture, $-20^{\circ}C$ in winter. High efficiency and the low-noise type sirocco fan operate quitely and designed to fit the bottom-up and top-down in accordance with the characteristics of equipment. as a result of experiment data, the conversion efficiency is 95% or more, power recovery time is within 5sec, stop delay time is within 30sec, pump down time is 10sec, pump delay time is 5sec, heating delay time is 5sec, temperature deviation is ${\pm}2^{\circ}C$ (cooling deviation: $2^{\circ}C$, Heating deviation : $2^{\circ}C$), humidity deviation is a ${\pm}5%$ (humidification deviation 3.0%, dehumidification deviation 3.0%). Recently, ubiquitous technology is important. so, the constant temperature and humidity machine designed to be able to remotely control to via the mobile phone, and more scalable to support MMI software and automatic interface. Further, the life of the parts and equipment is extended by the failure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.1
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• pp.35-44
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• 2009

This paper describes an application of domain decomposition method for parallel finite element analysis which is required to large scale 3D structural analysis. A parallel finite element method system which adopts a domain decomposition method is developed. Node is generated if its distance from existing node points is similar to the node spacing function at the point. The node spacing function is well controlled by the fuzzy knowledge processing. The Delaunay triangulation method is introduced as a basic tool for element generation. Domain decomposition method using automatic mesh generation system holds great benefits for 3D analyses. Aa parallel numerical algorithm for the finite element analyses, domain decomposition method was combined with an iterative solver, i.e. the conjugate gradient(CG) method where a whole analysis domain is fictitiously divided into a number of subdomains without overlapping. Practical performance of the present system are demonstrated through several examples.