• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.199-208
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• 2017

Lithium polymer batteries are used in energy storage systems (ESS), electric vehicles (EVs), etc. due to their high safety, fast charging and long lifecycle, and now they are used in agricultural drones. However, when overcharging and overdischarging, the lithium-polymer battery is destroyed in the gap structure in the lithium-ion battery and the battery life is reduced. In order to prevent overcharge and overdischarge, uneven cell voltage Cell balancing system is needed. In this paper, a fuzzy controller suitable for nonlinear systems is proposed by detecting the unbalanced cells by detecting the voltage difference between charging and discharging of each cell, and suggesting the applied cell balancing algorithm. In this paper, we have designed the cell balancing of the battery pack of agricultural drones by fuzzy control and it is designed for equal control between cells. As a final result, we checked whether cell balancing is good, and when there are two cells, Cell balancing was confirmed. We tested whether it could be used for other products. As a result, we confirmed that cell balancing is good regardless of the number of cells used.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1114-1124
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• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• Journal of Korea Port Economic Association
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• v.27 no.2
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• pp.39-57
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• 2011

Recently Incheon Port and Pyeongtak·Dangjin Port called as Capital Region Ports have enjoyed ever-increasing cargo volumes. However, there is a lack of research on this region while plenty of outputs were suggested on mega hub and regional hub ports in terms of shipping companies and stakeholders' port choice criteria. To identify and evaluate the Capital Region Ports, this paper identifies the factors and sub-components influencing their port choice and presents a structure for evaluating them. Based on the literature related to port selection and competition, a regional survey employed Factor Analysis to reveal that 'port facility and link', 'cost and service', 'port hinterland' and 'information service and port operation policy' are the determining factors in these regions. From the overall evaluation using Fuzzy Theory, Port of Incheon Port obtained high score compare to that of Port of Pyeongtak Dangjin.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.616-621
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• 2006

Recently many cars are equipping a navigation system. The main purpose of the early system guides a user through the route. A navigation system includes various abilities by development of various technologies and it has given more convenience to user. It can play various records on the tape and announces which are useful information about each road. Also it can use various multi-media contents by DMB device during driving. However, guide function of basic and important road in the navigation system has not grown greatly yet. In this paper, we proposed recommendation method of human-friendly road considering user's condition through various information of outside environment, user's velocity intention, a driver's emotion and a preference of the road. Modules consists of hierarchical structure that can easily correct and add each algorithm and those use fuzzy logic algorithm.

• Advances in concrete construction
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• v.13 no.5
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• pp.385-394
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• 2022

Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• International Journal of Safety
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• v.4 no.2
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Korean Journal of Cognitive Science
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• v.14 no.4
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• pp.33-42
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• 2003

This paper presents a new system for facial expression recognition based in dimension model of internal states. The information of facial expression are extracted to the three steps. In the first step, Gabor wavelet representation extracts the edges of face components. In the second step, sparse features of facial expressions are extracted using fuzzy C-means(FCM) clustering algorithm on neutral faces, and in the third step, are extracted using the Dynamic Model(DM) on the expression images. Finally, we show the recognition of facial expression based on the dimension model of internal states using a multi-layer perceptron. The two dimensional structure of emotion shows that it is possible to recognize not only facial expressions related to basic emotions but also expressions of various emotion.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.11-14
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• 1990

The detection of pulmonary blood vessels is very difficult owing to their complex tree structures and different widths. In this paper, We propose a new detection algorithm. The motivation of this algorithm is that Han is the best detector. So, this algorithm is developed to imitate the human searching process. To realize it, the algorithm consist of two components. One is Pyramid Images whose one pixel is median value of four pixels of the previous low level. Searching gradually from high level to low level, We concentrate on global and main information of structure at the first. Then based on it, We search the detailed data in low level. The other is fuzzy logic which makes it easy to convert searching process expressed as human language into numeric multi_value. This algorithm showes speedy and robust results. But the more study on both human searching process and the detection of small part is needed.