• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.142-142
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• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.75-85
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• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.

• The KIPS Transactions:PartC
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• v.17C no.3
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• pp.271-280
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• 2010

For the next generation mobile communication system, diverse wireless network techniques such as beyond 3G LTE, WiMAX/WiBro, and next generation WLAN etc. are proceeding to the form integrated into the All-IP core network. According to this development, Beyond 3G integrated into heterogeneous wireless access technologies must support the vertical handover and network to be used of several radio networks. However, unified management of each network is demanded since it is individually serviced. Therefore, in order to solve this problem this study is introducing the theory of Common Radio Resource Management (CRRM) based on Generic Link Layer (GLL). This study designs the structure and functions to support the vertical handover and propose the vertical handover algorithm of which policy-based and MCDM are composed between LTE and WLAN systems using GLL. Finally, simulation results are presented to show the improved performance over the data throughput, handover success rate, the system service cost and handover attempt number.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Spatial Information Research
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• v.11 no.2
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• pp.101-117
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• 2003

High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

• Journal of Sensor Science and Technology
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• v.6 no.1
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• pp.6-15
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• 1997

This paper presents the multi-objective optimal design of thermopile sensor having beam or membrane structure. The thermopile sensor is composed of $Si_{3}N_{4}/SiO_{2}$ dielectric membrane, Al-polysilicon thermocouples and $RuO_{2}$ thin film for black body. The sensing method is based on the Seebeck effect which is originated from the temperature difference of the two positions, black body and silicon rim. The objective functions of the presented design are sensitivity, detectivity and thermal time constant. The modelling of the sensor is proposed including the package. The multi-objective optimization technique is applied to the design of the sensor not only inspecting the modelling equation but also simulating mathematical programming method. Especially, fuzzy optimization technique is adapted to get the optimal solution which enables the designer to reach the more practical solution. The design constraint of the voltage output originated from the change of the environmental temperature is included for practical use.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1429-1434
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• 2017

In automatic defect detection problem for spectacle lenses, it is important to extract lens area accurately. Many existing detection methods fail to do it due to insufficient minute noise removal. In this paper, we propose an automatic defect detection method using Bresenham algorithm and adaptive binarization strategy. After usual average binarization, we apply Bresenham algorithm that has the power in extracting ellipse shape from image. Then, adaptive binarization strategy is applied to the critical minute noise removal inside the lens area. After noise removal, We can also compute the influence factor of the defect based on the fuzzy logic with two membership functions such as the size of the defect and the distance of the defect from the center of the lens. In experiment, our method successfully extracts defects in 10 out of 12 example images that include CHEMI, MID, HL, HM type lenses.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.696-704
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• 2013

In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.8
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• pp.686-696
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• 2017

The major objective of this study is to develop and verify the KAUSAT-5 based on the modular 3U CubeSat standard platform. In the mechanical system design of a 3U standard platform, subsystem and micro equipment functions/performance should be integrated and miniaturized on micro-sized PCBs and electrical capability was maximized to accommodate multiple payloads. KAUSAT-5 is 3U-sized Cubesat which will be operated in Low Earth Orbit(LEO), which implements mainly two scientific missions; one is to observe the Earth through infrared camera and the other is to measure space radiation with a Geiger Muller tube. An additional mission is to verify the equipment(device) such as VSCMG and fuzzy logic-based MPPT internally developed. The results of ETB, qualification and acceptance level environmental tests were shown to verify standard platform and KAUSAT-5 Cubesat.