• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.1
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• pp.9-15
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• 2014

This paper focuses on the heat gain of a lighting system, one of the most-used appliances in buildings, and its thermal effect within a low energy building. In this study, a dynamic thermal model of a lighting system is first established based on the first principle of thermodynamics. Then, thermal parameters of this model are estimated by experiments and an optimization process. Afterward, the obtained model of the system is validated by comparing simulation results to experimental one. Finally it is integrated into a low energy building model in order to quantify its thermal influence within a low energy building. As a result, heat flux of the lighting system, indoor temperature and heating energy demands of the building are obtained and compared with the results obtained by the conventional model of a lighting system. This paper helps to understand thermal dynamics of a lighting system and to further apply lighting systems for energy management of low energy buildings.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.2
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• pp.173-184
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• 2015

The application of radio frequency identification (RFID) sensor-tags in cold chain systems has recently received a great deal of attention. To design cold chain systems with RFID sensor-tags that minimize the initial investment and operational cost while fulfilling the functional and operational requirements, simulation study is one of the preferable and effective approaches. To simulate the possible design configurations, the individual components in a cold chain system can be extracted and implemented as a DEVS (Discrete Event System Specification) model. Based on the proposed DEVS model, a new cold chain simulation model can be efficiently created by simply connecting each DEVS model around the RFID sensor-tag of interest in sequence according to the structure of the cold chain system, and then executed (or simulated) on Java programming environments by the DEVSJAVA simulator. As a result of simulation, some key performance indexes such as reliability, accuracy or timeliness can be calculated and used to choose better components or to compare different system configurations of cold chain systems.

• Journal of Navigation and Port Research
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• v.34 no.9
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• pp.719-726
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• 2010

As the competition among the container terminals in Korea has become increasingly fierce, every terminal is striving to increase its investments constantly and lower its operational costs in order to maintain the competitive edge and provide satisfactory services to terminal users. The unreasoning behavior, however, has induced that substantial waste and inefficiency exists in container terminal production. Therefore, it is of great importance for the terminal to know whether it has fully used its existing infrastructures and that output has been maximized given the input. From this perspective, data envelopment analysis (DEA) provides a more appropriate benchmark. This study applies three models of DEA to acquire a variety of analytical results about the operational efficiency to the Korean container terminals. According to efficiency value analysis, this study first finds the reason of inefficiency. It is followed by identification of the potential areas of improvement for inefficient terminals by applying slack variable method and giving the projection results. Finally, return to scale approach is used to assess whether each terminal is in a state of increasing, decreasing, or constant return to scale. The results of this study can provide terminal managers with insight into resource allocation and optimization of the operating performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.544-553
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• 2013

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.2
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• pp.45-56
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• 2013

The precise identification of vehicle positions, known as the vehicle localization problem, is an important requirement for building intelligent vehicle ad-hoc networks (VANETs). To solve this problem, two categories of solutions are proposed: stand-alone and data fusion approaches. Compared to stand-alone approaches, which use single information including the global positioning system (GPS) and sensor-based navigation systems with differential corrections, data fusion approaches analyze the position information of several vehicles from GPS and sensor-based navigation systems, etc. Therefore, data fusion approaches show high accuracy. With the position information on a set of vehicles in the preprocessing stage, data fusion approaches is used to estimate the precise vehicular location in the local map building stage. This paper proposes an efficient local map building scheme, which increases the accuracy of the estimated vehicle positions via V2V communications. Even under the low ratio of vehicles with communication modules on the road, the proposed local map building scheme showed high accuracy when estimating the vehicle positions. From the experimental results based on the parameters of the practical vehicular environments, the accuracy of the proposed localization system approached the single lane-level.

• Smart Structures and Systems
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• v.5 no.1
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• pp.1-21
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• 2009

A two-stage damage detection method is proposed and demonstrated for structural health monitoring. In the first stage, the subset selection method is applied for the identification of the multiple damage locations. In the second stage, the damage severities of the identified damaged elements are determined applying SSGA to solve the optimization problem. In this method, the sensitivities of residual force vectors with respect to damage parameters are employed for the subset selection process. This approach is particularly efficient in detecting multiple damage locations. The SEREP is applied as needed to expand the identified mode shapes while using a limited number of sensors. Uncertainties in the stiffness of the elements are also considered as a source of modeling errors to investigate their effects on the performance of the proposed method in detecting damage in real-life structures. Through a series of illustrative examples, the proposed two-stage damage detection method is demonstrated to be a reliable tool for identifying and quantifying multiple damage locations within diverse structural systems.

• Journal of Electrochemical Science and Technology
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• v.10 no.1
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• pp.1-13
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• 2019

Na-ion batteries are being considered as promising cost-effective energy storage devices for the future compared to Li-ion batteries owing to the crustal abundance of Na-ion. However, the large radius of the Na ion result in sluggish electrode kinetics that leads to poor electrochemical performance, which prohibits the use of these batteries in real time application. Therefore, identification and optimization of the anode, cathode, and electrolyte are essential for achieving high-performance Na-ion batteries. In this context, the current review discusses the suitable high-voltage cathode materials for Na-ion batteries. According to a recent research survey, sodium vanadium fluorophosphate (NVPF) compounds have been emphasized for use as a high-voltage Na-ion cathode material. Among the fluorophosphate groups, $Na_3V_2(PO_4)_2F_3$ exhibited the high theoretical capacity ($128mAh\;g^{-1}$) and working voltage (~3.9 V vs. $Na/Na^+$) compared to the other fluorophosphates and $Na_3V_2(PO_4)_3$. Here, we have also highlighted the classification of Fluorophosphates, NVPF composite with carbonaceous materials, the appropriate synthesis methods and how these methods can enhance the electrochemical performance. Finally, the recent developments in NVPF for the application in energy storage devices and its outlook are summarized.