• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.3
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• pp.348-356
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• 2014

The existing effective path-length model of ITU-R has some drawbacks: The prediction error is quite large compared to domestic measurement data and it is an empirical model in which the physical characteristics of rain cells are not considered. In this paper, a theoretical model for effective path-length using the rain-cell concept was proposed and its validity was verified using the measurement data. To analyze the statistical characteristics of rain cell parameters, the weather-radar data(CAPPI) measured by Korea Meterological Administration were analyzed and the correction factor was properly introduced to fit the Chollian beacon measurement data of ETRI(Electronics and Telecommunications Research Institute). To verify the proposed effective path-length model, it was compared with the Mugunghwa No. 5 beacon data measured in Chungnam National University with the support of ADD(Agency for Defense Development). It was confirmed that the prediction results of the proposed model are in good agreement with the measurement data.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN 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 Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.78-86
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• 2014

In this paper, a novel intensity-based fiber optic vibration sensor using a mass-spring structure, which consists of four serpentine flexure springs and a rectangular aperture within a proof mass, is proposed and its feasibility test is given by the simulation and experiment. An optical collimator is used to broaden the beam which is modulated by the displacement of the rectangular aperture within the proof mass. The proposed fiber optic vibration sensor has been analyzed and designed in terms of the optical and mechanical parts. A mechanical structure has been designed using theoretical analysis, mathematical modeling, and 3D FEM (Finite Element Method) simulation. The relative aperture displacement according to the base vibration is given using FEM simulation, while the output beam power according to the relative displacement is measured by experiment. The simulated sensor sensitivity of $15.731{\mu}W/G$ and detection range of ${\pm}6.087G$ are given. By using reference signal, the output signal with 0.75% relative error shows a good stability. The proposed vibration sensor structure has the advantages of a simple structure, low cost, and multi-point sensing characteristic. It also has the potential to be made by MEMS (Micro-Electro-Mechanical System) technology.

• 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.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.411-422
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• 2016

Rainfall-runoff modeling in conjunction with rainfall frequency analysis has been widely used for estimating design floods in South Korea. However, uncertainties associated with underlying distribution and sampling error have not been properly addressed. This study applied a Bayesian method to quantify the uncertainties in the rainfall frequency analysis along with Gumbel distribution. For a purpose of comparison, a probability weighted moment (PWM) was employed to estimate confidence interval. The uncertainties associated with design rainfalls were quantitatively assessed using both Bayesian and PWM methods. The results showed that the uncertainty ranges with PWM are larger than those with Bayesian approach. In addition, the Bayesian approach was able to effectively represent asymmetric feature of underlying distribution; whereas the PWM resulted in symmetric confidence interval due to the normal approximation. The use of long period data provided better results leading to the reduction of uncertainty in both methods, and the Bayesian approach showed better performance in terms of the reduction of the uncertainty.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2233-2244
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• 1999

It is essential to guarantee high reliability of embedded real-time systems since the failure of such systems may result in large financial damage or threaten human life. Though many researches have devoted to fault tolerant mechanisms, most of them are object-level fault tolerant mechanisms that can detect errors occurred in a single object and treat the errors in object-level. As embedded real-time systems become more complex and larger, there exist faults that cannot be detected by or tolerated with object-level fault tolerance. Hence, system-level fault tolerance is needed. System-level fault tolerance examines the status of a system whether the system is normal or not by analyzing the status of objects. When an error is detected it should be capable of locating the fault and performing an appropriate recovery and reconfiguration action. In this paper, we propose RobustRTO(Robust Real-Time Object) that provides object-level fault tolerance capability and RMO(Region Monitor real-time Object) that offers system-level fault tolerance capability. Then we show how highly dependable fault tolerant systems can be modeled by RobustRTO and RMO. The model is presented based on real-time objects.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.3
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• pp.139-146
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• 2018

Underwater acoustic communication can be applied to various area such as scientific, commercial and military survey using Autonomous Underwater Vehicles and Unmanned Underwater Vehicles. Underwater communication is studying very actively by advanced country like United States. But differ from wireless communication in the air, underwater acoustic communication has some difficult problems, ISI(Inter Symbol Interference) due to multipath and limit of transmission bandwidth due to slow propagation of sound wave. In this paper, SC-FDE and OFDM transmission system for the cancellation of ISI in conjunction with underwater acoustic channel modeling are applied to the underwater simulation of communication. The performance of these methods in the simulation guide to possibility of adopting in underwater acoustic communication algorithm. For this purpose, we compare SER performance of SC-FDE with that of OFDM for modelled underwater channel. Underwater channel is generated by Bellhop model. Simulation results show above 5dB SNR gain at 10-3 SER. And it demonstrate SC-FDE is efficient method for underwater acoustic communication.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.349-356
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• 2018

This paper attempts to develop a connection model based on FE analysis that can be applied to the evaluation of earthquake fragility of Steel Storage Racks lacking research in Korea. In order to accomplish this goal, shaking table tests, modal tests, and various member tests (8 case, push-over test) for structural members have been conducted to understand the behavior of steel storage racks. Based on the experimental results, detailed modeling of the joints was conducted using the NX-Nastran program in order to develop a connection model for Steel storage racks to be applied to the seismic vulnerability assessment. Especially, surface to surface contact element and spring element are applied to simulate the connection between the column member and the beam member connected by the simple latch method. Spring element model developed and applied ARX (Auto Regressive eXogenous) based mathematical model. The simulation results based on the FE model showed excellent reliability with a mutual error rate of less than 8% when compared with the member test results. As a result, it was confirmed that the FE model based connection model developed in the study can be applied to the analytical model for the seismic vulnerability assessment of Steel storage racks.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.109-119
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• 2007

Increasingly, the emphasis in regional Passenger rail Planning is finding ways to more efficiently use existing facilities, with particular attention being Paid to Policies designed to spread Peak-Period travel demand more evenly throughout the week with consideration of train classification. In this context the individual's choice of time to travel is of crucial significance. This paper investigates the use of multinomial logit analysis to model ridership by rail classification using data collected for travel from Seoul to Busan during the one week in October 2004. The Particular model form that was successfully calibrated was the multinomial logit (MNL) model : it describes the choice mechanism that will Permit rail systems and operations to be planned on a more reliable basis. The assumption of independently and identically distributed(IID) error terms in the MNL model leads to its infamous independence from irrelevant alternatives (IIA) property. Relaxation of the IID assumption has been undertaken along a number or isolated dimensions leading to the development of the MNL model. For business and related rail travel patterns, the most important variables of choice were time and frequency to the chosen destination. The calibrated model showed high agreement between observed and Predicted market shares. The model is expected to be of use to railroad authorities in Planning and determining business strategies in the Increasingly competitive environment or regional rail transport.