• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.41-46
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• 2019

In this paper, it is necessary to study the bandwidth and network system for efficient image transmission in the current era of drone imaging, and to design routing protocols to round out and cluster two or more multi-drones. First, we want to construct an ad hoc network to control the multidrone. Several studies are underway for the clustering of drones. The aircraft ad hoc network (FANET) is an important foundation for this research. A number of routing protocols have been proposed to design a FANET, and these routing protocols show different performances in various situations and environments. The routing protocol used to design the FANET is tested using the routing protocol used in the existing mobile ad hoc network (MANET). Therefore, we will use MANET to simulate the routing protocol to be used in the FANET, helping to select the optimal routing protocol for future FANET design. Finally, this paper describes the routing protocols that are mainly used in MANET and suitable for FANET, and the performance comparison of routing protocols, which are mainly used in FANET design.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Fashion & Textile Research Journal
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• v.18 no.1
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• pp.120-129
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• 2016

This study aimed to develop an optimal sizing system and a draping pattern for hip protector based on an analysis of anthropometric characteristics of Korean older women. A hip protector is a specialized form of pants or underwear containing pads along the outside of each hip. The 3D body scan data of Korean older women were analyzed to identify their anthropometric characteristics and a four-size system with 93% of population accommodation was developed by clustering analysis based on key dimensions derived from factor analysis. The sizing system consists of small/short, large/short, small/tall, and large/tall. A 3D physical model and hip pads were fabricated; then, a hip protector was draped on the 3D model and hip pads. The sizing system of hip protector was analyzed in terms of size and shape and a draping pattern was compared on back center, back side, front side, front center and pad. Lastly, the pattern deformation and clothing pressure were analyzed using the virtual clothing system CLO. Virtual system have disadvantage of not to suggest the objective value. In the future research the wearing comfort and impact absorption of the hip protector needs to be tested and then a hip protector design will be finalized by considering the hip protector's size, material, comfort testing results, aesthetic appeal, protection effectiveness, and practical utility of everyday use.

• Composites Research
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• v.30 no.1
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• pp.65-70
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• 2017

Carbon fiber reinforced composites have light weight and high mechanical properties. These materials are only applicable in limited shape structure cause by complex curing process and low drapability. To solve this problem, Long Fiber Prepreg Sheet (LFPS) has been proposed. In this research, electric device cover plate was selected and designed by using LFPS. Before the design process, we analyzed the target structure to which the rib structures were applied. And 8-inch tablet PC product was selected. For FE analysis, simple loading and boundary conditions were applied. Stiffness of rib structure was investigated according to the rib pattern and shape changes. Rib pattern and shape were selected based on fixed volume condition analysis results. And uneven rib width model was selected for the best case whose deflection was reduced 6~10% than uniform rib model.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.6
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• pp.549-557
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• 2000

Distributed Memory Machine(DMM) is necessary for the effective computation of the data which is complicated and very large. Task scheduling is a method that reduces the communication time among tasks to reduce the total execution time of application program and is very important for the improvement of DMM. Task Duplicated based Scheduling(TDS) method improves execution time by reducing communication time of tasks. It uses clustering method which schedules tasks of the large communication time on the same processor. But there is a problem that cannot optimize communication time between task sending data and task receiving data. Hence, this paper proposes a new method which solves the above problem in TDS. Modified Task Duplicated based Scheduling(MTDS) method which can approximately optimize the communication time between task sending data and task receiving data by checking the optimal condition, resulted in the minimization of task execution time by reducing the communication time among tasks. Also system modeling shows that task execution time of MTDS is about 70% faster than that of TDS in the best case and the same as the result of TDS in the worst case. It proves that MTDS method is better than TDS method.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.591-601
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• 2020

The self-organizing map (SOM) is a unsupervised learning method projecting high-dimensional data into low-dimensional nodes. It can visualize data in 2 or 3 dimensional space using the nodes and it is available to explore characteristics of data through the nodes. To understand the structure of data, cluster analysis is often used for nodes obtained from SOM. In cluster analysis, the optimal number of clusters is one of important issues. To help to determine it, various cluster validity indexes have been developed and they can be applied to clustering outcomes for nodes from SOM. However, while SOM has an advantage in that it reflects the topological properties of original data in the low-dimensional space, these indexes do not consider it. Thus, we propose a new cluster validity index for SOM based on connectivity between nodes which considers topological properties of data. The performance of the proposed index is evaluated through simulations and it is compared with various existing cluster validity indexes.

• Journal of Korean Society on Water Environment
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• v.34 no.5
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• pp.537-547
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• 2018

Sediment discharge by rainfall runoff affects water quality in rivers such as turbid water, eutrophication. In order to solve various problems caused by soil loss, it is important to establish a sediment management plan for watersheds and rivers in advance. However, there is a lack of sediment data available for estimating sediment discharge in ungauged basins.. Thus, reasonable research is very important to evaluate and predict the sediment discharge quantitatively. In this study, cluster analysis was conducted to classify gauged watersheds into hydrologically homogeneous groups based on the watershed characteristics. Also, this study suggests a method to efficiently predict the sediment discharge for ungauged basins by developing and evaluating the SDR equations based on the PA-SDR module. As the result, the SDR equations for the classified watersheds were derived to predict the most reasonable sediment discharge of ungauged basins with 0.24 % ~ 10.89 % errors. It was found that the optimal parameters for the gauged basins reflect well characteristic of sediment movement. SDR equations proposed in this study will be available for estimating sediment discharge on ungauged basins. Also it is possible to utilize establishing the appropriate sediment management plan for integrated management of watershed and river in Korea.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.635-642
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• 2003

Reinforcement learning got successful results in a lot of applications such as control and scheduling. Various function approximation methods have been studied in order to improve the learning speed and to solve the shortage of storage in the standard reinforcement learning algorithm of Q-Learning. Most function approximation methods remove some special quality of reinforcement learning and need prior knowledge and preprocessing. Fuzzy Q-Learning needs preprocessing to define fuzzy variables and Local Weighted Regression uses training examples. In this paper, we propose a function approximation method, Fuzzy Q-Map that is based on on-line fuzzy clustering. Fuzzy Q-Map classifies a query state and predicts a suitable action according to the membership degree. We applied the Fuzzy Q-Map, CMAC and LWR to the mountain car problem. Fuzzy Q-Map reached the optimal prediction rate faster than CMAC and the lower prediction rate was seen than LWR that uses training example.