• International journal of advanced smart convergence
• /
• v.9 no.3
• /
• pp.42-48
• /
• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• International journal of advanced smart convergence
• /
• v.5 no.1
• /
• pp.66-72
• /
• 2016

In this paper, we proposed the clustering algorithm using fuzzy inference system for improving adaptability the cluster head selection of TEEN. The stochastic selection method cannot guarantee available of cluster head. Furthermore, because the formation of clusters is not optimized, the network lifetime is impeded. To improve this problem, we propose the algorithm that gathers attributes of sensor node to evaluate probability to be cluster head.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.135-138
• /
• 1997

In this paper, we proposed effective modeling method to nonlinear complex system. Fuzzy Neural Network(FNN) was used as basic model. FNN was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, we used FNN which was proposed by Yamakawa. The FNN used Simple Inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. This structure has better property than other structure at learning speed and convergence ability. But it has difficulty at definition of membership function. We used Hard c-Mean method to overcome this difficulty. To evaluate proposed method. We applied the proposed method to waste water treatment process. We obtained better performance than conventional model.

• International journal of advanced smart convergence
• /
• v.6 no.3
• /
• pp.59-64
• /
• 2017

A wireless sensor network is a network in which nodes equipped with sensors capable of collecting data from the real world are configured wirelessly. Because the sensor nodes are configured wirelessly, they have limited power such as batteries. If the battery of the sensor node is exhausted, the node is no longer usable. If more than a certain number of nodes die, the network will not function. There are many wireless sensor network protocols to improve energy efficiency, among which LEACH Protocol is a typical example. The LEACH protocol is a cluster-based protocol that divides sensor space into clusters and transmits and receives data between nodes. Therefore, depending on how the cluster is structured, the shape of the energy cow may decrease or increase. We compare the network lifetimes of the existing LEACH protocols and the three types of protocols that have been improved using fuzzy methods for cluster selection.

• Journal of information and communication convergence engineering
• /
• v.15 no.2
• /
• pp.131-136
• /
• 2017

Chest X-ray image cannot be focused in the same manner as optical lenses and the resultant image generally tends to be slightly blurred. Therefore, appropriate methods to improve the quality of chest X-ray image have been studied in this paper. As the frequency domain filters work well for slight blurring and moderate levels of additive noises, we propose an algorithm that is particularly suitable for enhancing chest image. First, the chest image using Gaussian high pass filter and the optimal high frequency emphasis filter shows improvements in the edges and contrast of the flat areas. Second, as compared to using histogram equalization where each pixel of chest image is characterized by a loss of detail and much noises, in using fuzzy logic, each pixel of chest image shows the detail preservation and little noise.

• International journal of advanced smart convergence
• /
• v.1 no.2
• /
• pp.30-33
• /
• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• Journal of information and communication convergence engineering
• /
• v.13 no.4
• /
• pp.264-269
• /
• 2015

The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

• Journal of information and communication convergence engineering
• /
• v.7 no.3
• /
• pp.268-274
• /
• 2009

Human beings can represent state of mind such as psychological state, personality or emotional trouble by the pictures painted on one's own initiative. But in general, it is hard to understand a consulter's unconscious state through one's objective and intentional descriptions only. So one's psychological state and emotional trouble can be understood and cured by color and location information of objects drawn in one's picture. By this reason, a consultant can help and settle a consulter's growth stages of life and emotional trouble through treatment by pictures. In this paper, we proposed a method to find out state of sensitivity by analysis of color and location information represented in a picture and fuzzy inference rules. We applied the proposed method to the states of sensitivity from color information proposed by Alschuler and Hattwick and the psychological states from location information proposed by Grunwald. In the experimental results by the two applications, we verified the proposed sensitivity analysis method is efficient.

• Journal of information and communication convergence engineering
• /
• v.7 no.3
• /
• pp.383-391
• /
• 2009

In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.445-445
• /
• 2000

This paper proposes the MIMO indirect adaptive fuzzy controller to control the two-link manipulators. The input-output linearization technique, equivalent control input plus integral term, augmented error model and recursive least square adaptive law are used fer the controller. The linear type of fuzzifier-defuzzifier fuzzy logic system used for nonlinear function makes easy to farm the error model and able to follow the adaptive system approach. Such that control approach, the control system is not required joint speed and accerelation measurement and easy to implement and tune. The simulation results showed that the proposed controller has good control performance, stability, very small tracking error, decoupling, fast convergence, robust to parameter variation and load.