• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.65-70
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• 1991

A hybrid model which uses a probabilistic model and a MLP(multi layer perceptron) model for pattern recognition of EMG(electromyogram) signals is proposed in this paper. MLP model has problems which do not guarantee global minima of error due to learning method and have different approximation grade to bayesian probabilities due to different amounts and quality of training data, the number of hidden layers and hidden nodes, etc. Especially in the case of new test data which exclude design samples, the latter problem produces quite different results. The error probability of probabilistic model is closely related to the estimation error of the parameters used in the model and fidelity of assumtion. Generally, it is impossible to introduce the bayesian classifier to the probabilistic model of EMG signals because of unknown priori probabilities and is estimated by MLE(maximum likelihood estimate). In this paper we propose the method which get the MAP(maximum a posteriori probability) in the probabilistic model by estimating the priori probability distribution which minimize the error probability using the MLP. This method minimize the error probability of the probabilistic model as long as the realization of the MLP is optimal and approximate the minimum of error probability of each class of both models selectively. Alocating the reference coordinate of EMG signal to the outside of the body make it easy to suit to the applications which it is difficult to define and seperate using internal body coordinate. Simulation results show the benefit of the proposed model compared to use the MLP and the probabilistic model seperately.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.10
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• pp.829-841
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• 1990

On assuming that the transmission speed of the original information is the fifth-order transmission speed of the Korea digital multiplex hierarchy (564.992Mb/s), this paper proposes a new structure of the transmission line frame at the terminal repeater system, in order to not only maintain and conserve 565Mb/s optical fiber transmission system but also make the B.S.I. of digital communication network for the optical transmission. And the structure uses the mBIZ transmission line code, which is considered the optimal transmission line code of conventional transmission line codes. System hardware of the transmission line frame structure proposed in this paper is consisted by a method of pulse stuffing after converting the speed of the original information signal sequence at the terminal repeater system for 565Mb/s optical transmission. As a result of this, we can prevent the optical transmission system from a domino phenomenon, the phenomenon of the continuous error multiplication of systems by the transmission error, and suppress timing jitter and the identical consecutive digit number. And also we can improve SNR of the optical transmission system about 2dB because of raising total BER at the optical terminal system up to 10.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.139-150
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• 2014

This paper suggests an algorithm to improve QoS by using DTR bit employed in the MIL-STD-188-220 protocol, when there is a multi-path between source and destination in the environment of KVMF tactical network supporting NCW. The MIL-STD-188-220 protocol can evaluate the link quality relatively but it cannot support optimal path selection for QoS. In order to solve this problem, we design an algorithm for selecting path using topology table which reflects measured DTR of path after the completion of transmission. The performance of the proposed algorithm has been evaluated by OPNET simulator. As a result of the simulation, it is found that QoS of proposed algorithm is enhanced higher than that of the MIL-STD-188-220 in the aggravated communication environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.80-83
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• 1999

An efficient modeling and management of a large amount of surface data for a wide rage of geographic information play an important role in determining the functionality of 3D geographic information system. It has been put many efforts to design and manage an effective way to enhence the manipulation of the data by considering geometry type and data structures. Recently, DEM(Data Elevation Model) and TIN(Triangulated Irregular Network) are used for representing surface data. In this paper, we propose a 3D data processing method. The method utilizes the major properties of DEM and TIN, respectively. Furthermore, by approximating DEM with a TIN of an appropriate resolution, we can support a fast and realistic surface modeling. We implement the structure with the following 4 level stages. The first is an optimal resolution of DEM which represent all of wide range of geographic data. The second is the full resolution DEM which is a subarea of original data generated by user's selection in our implemeatation. The third is the TIN approximation of this data with a proper resolution determined by the relative position with the camera. And the last step is multi-resolution TIN data whose resolution is dynamically decided by considering which direction user take notice currently. Specially, the TIN of the last step is designed for realtime camera navigation. By using the structure we implemented realtime surface clipping, efficient approximation of height field and the locally detailed surface LOD(Level of Detail). We used the initial 10-meter sampling DEM data of Seoul, KOREA and implement the structure to the 3D Virtual GIS based on the Internet.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.129-136
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• 2011

Variable Message Signs have been used for providing information on the current traffic conditions. However, it is considered more important to achieve optimal traffic allocation among the alternative routes by strategic VMS information provision. Fuzzy control is very effective and efficient to deal with such systems that are too complex and uncertain to build mathematical models. In this paper, a fuzzy feedback controller for VMS is proposed, whose goal is to achieve the travel time equilibrium between the two alternative routes. The performance of the suggested controller is implemented and examined using MATLAB/Simulink. More robust controller applicable to a real highway network is suggested for the further research.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.191-200
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• 2010

The "intelligent internet power outlets" realized in this study use Internet environment to connect electrical and electronic products to the Net as well as to enable remote monitoring and control. They also have temperature and light sensors, and even offer scheduling options by means of a universal calendar run by an inner real-time timer. Furthermore, they control 4 outlets handling 4 external input conditions and infrared remote control with easy-to-use functions for home automation control. The user interface is equipped with an embedded Web server and UDP protocol handler, and which also allows special control programs be used with the Web browser. Thus, installing this real-time power control function with optimal scheduling and various communication functions will provide you with energy-efficient power outlets outfitted with the increasingly popular "standby power" electric power efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1204-1227
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• 2020

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.