• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Smart Structures and Systems
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• v.20 no.1
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• pp.85-97
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• 2017

The aim of this research is to develop a new method to use magnetorheological (MR) damper for vibration control. It is a new way to achieve the MR damper response without the need to have detailed constant parameters estimations. The methodology adopted in designing the control structure in this work is based on the experimental results. In order to investigate and understand the behaviour of an MR damper, an experiment is first conducted. Force-displacement and force-velocity responses with varying current have been established to model the MR damper. The force for upward and downward motions of the damper piston is found to be increasing with current and velocity. In cyclic motion, which is the combination of upward and downward motions of the piston, the force with hysteresis behaviour is seen to be increasing with current. In addition, the energy dissipated is also found to be linear with current. A proportional-integral-derivative (PID) controller, based on the established characteristics for a quarter car suspension model, has been adapted in this study. A fuzzy rule based PID controller (F-PID) is opted to achieve better response for a varying frequency input. The outcome of this study can be used in the modelling of MR damper and applied to control engineering. Moreover, the identified behaviour can help in further development of the MR damper technology.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.125-134
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• 2000

In this paper, we proposed a new circuit construction method that reduced the number of CMOS devices of singleton fuzzy controller(SFC) through the proposing a new membership function circuit(MFC) which uses the language variable selecting and the coefficient selecting circuit. According to the range of input values, we can choose the language variables beforehand which will be used in the inference. So we proposed the new MFC which generates the only necessary language variables. Also, we removed all rules of which adapting degree of their antecedents is zero through proposing the coefficient selecting circuit which beforehand selects the coefficients which will influence the inference result. Though this method, we simplified the structure of SFC and reduced the size of hardware. And to solve the problem in the current mode with respect to the restriction of the fan-out number, voltage-input and current-out membership function circuits are constituted of operational transconductance amplifiers. A membership function circuit which includes the language variable selecting circuit, a minimum operation circuit we implemented by current mode CMOS devices. As a result of applying proposed method, total numbers of blocks and devices wave decreased. If the number of variables and antecedents are getting larger, this method is more efficient.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.15-25
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• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.7
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• pp.3244-3251
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• 2012

In the construction and operation of large marine structure, hazard risk analysis is one of important factors. Therefore, this paper investigates the hazard risk indexes and evaluates the risk level in the construction and operation of SFT on the basis of expert survey and Fuzzy analytic hierarchy process. Hazard risk is divided into natural hazard risk (earthquake, typhoon, tsunami, and ice collision) and human factor hazard risk (fire, explosion, traffic accident, ship or submarine collision). Also, the influence of hazard risk indexes on SFT was evaluated in tunnel tube, supporting system, ventilation tower, foundation, and connection part. As the hazard risk level of SFT is compared with those of bridge, underwater tunnel, and immersed tunnel, the intrinsic risk level of SFT was evaluated. Tsunami and earthquake had higher risk level in natural hazard risk, and the risk levels of fire and explosion were higher in human factor hazard risk. Hazard risk level of SFT was 1.4 times higher than immersed tunnel, and 3.2 times higher than bridge.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.33-44
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• 2014

Recently, blogs have attracted much attention as personal media. The power of blogs as a way to provide valuable resources on Internet is so tremendous because of the high speed of information dissemination and the huge influence of the circulated information on Internet users even when the information itself is not true. Especially, contents on blogs that attract a lot of public attention are sometimes reproduced or magnified in an inappropriate way. In this paper, a method to measure the trust level of contents posted on personal blogs is proposed to reduce the damage of wrong information circulated along with blog networks. Trust variables such as relationship data in SNS are used to measure the comparative trust level of blog contents. The structure of the prototype system is also designed to apply this framework to blogsphere.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.7
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• pp.605-613
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• 2013

This research proposes a vibration pattern algorithm to implement the haptic joystick to control a mobile robot at the remote site without watching the navigation environment. When the user cannot watch the navigation environment of the mobile robot, the user may rely on the haptic joystick solely to avoid obstacles and to guide the mobile robot to the target. To generate vibration patterns, there is a vibration motor at the bottom of the joystick which is held by the user to control the motion direction of the mobile robot remotely. When the mobile robot approaches to an obstacle, a pattern of vibration is generated by the motor, and by feeling the vibration pattern which is determined by the relative position of the mobile robot to the obstacle, the user can move the joystick to avoid the collision to the obstacle for the mobile robot. To generate the vibration patterns to convey the relative location of the obstacle near the mobile robot to the user, Fuzzy interferences have been utilized. To measure the distance and location of the obstacle near the mobile robot, ultrasonic sensors with the ring structure have been adopted and they are attached at the front and back sides of the mobile robot. The precise location of the obstacle is obtained by fusing the multiple data from ultrasonic sensors. Effectiveness of the proposed algorithm has been verified through the real experiments and the results are demonstrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.72-78
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• 2003

A new self-organizing neuro-fuzzy network based rate control algorithm for MPEG-4 video encoder is proposed in this paper. Contrary to the traditional methods that construct the rate-distorion (RD) model based on experimental equations, the proposed method effectively exploits the non-stationary property of the video date with neuro-fuzzy network that self-organizes the RD model online and adaptively updates the structure. The method needs not require off-line pre-training; hence it is geared toward real-time coding. The comparative results through the experiments suggest that our proposed rate control scheme encodes the video sequences with less frame skip, providing good temporal quality and higher PSNR, compared to VM18.0.