• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1945-1955
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• 2018

High frequency signal injection (HFI) is an alternative method for estimating rotor position of interior permanent magnet synchronous motor (IPMSM). The general method of frequency and amplitude selection is based on error tolerance and experiments, and is usually set with only one group of HF parameters, which is not efficient for different working modes. This paper proposes a novel rotor position estimation scheme by HFI with optimized frequency and amplitude, based on the mathematic model of IPMSM. The requirements for standstill and low-speed operational modes are met by applying this novel scheme. Additionally, the effects of the frequency and amplitude of the injected HF signal on the position estimation results under different operating conditions are analyzed. Furthermore, an optimization method for HF parameter selection is proposed to make the estimation process more efficient under different working conditions according to error tolerance. The effectiveness of the propose scheme is verified by the experiments on an IPMSM motor prototype.

• 전기의세계
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• v.29 no.9
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• pp.584-593
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• 1980

Conventional radar antenna systems are susceptible to performance degradation caused by unwanted signals received via the antenna sidelobes and/or mainlobes. Adaptive array systems offer possible solution to this interference problem by automatically steering nulls to unwanted signals providing significant system performance improvement. Another important andvantage of the adaptive array is its self-optimization capability which uses the collective incoming noise data for the nulling purposes. This paper provides a tutorial introduction to adaptive arrays as well as some new development of recent research in this area. Optimum link between the antenna theory and signal processing has been sought by illustrating the gain patterns and output signal-to-noise ratio. Signal acqusition methods are shown including a new attempt of the use of spread-spectrum techniques in conjuction with array systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.3
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• pp.59-71
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• 2018

This study was initiated to develop an optimal signal control algorithm for coordinated signalized intersections using individual vehicle's information which can be collected in a format of prove vehicle data (PVD) via V2I (Vehicle to Infrastructure) communication environment. For developing this signal optimization algorithm, three modules were developed for phase group length computation, split distribution, and phase sequence assignment. The simulation analysis using the microscopic simulation model, Vissim, was conducted for evaluating the effectiveness of the developed algorithm. The analysis result represented that the performance of the developed algorithm is far superior to that of the fixed coordinated signal control method which is the most common signal control method for coordinated signalized intersections in Korea.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.35-41
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• 2002

To solve the large scale optimization problem approximately, various approaches have been introduced. They are mainly based on recent research advancement of simulations for evolutions, flocking, annealing, and interactions among organisms on artificial environments. The typical ones are simulated annealing(SA), artificial neural network(ANN), genetic algorithms(GA), tabu search(TS), etc. Recently the particle swarm optimization(PSO) has been introduced. The PSO simulates the process of birds flocking or fish schooling for food, as with the information of each agent Is share by other agents. The PSO technique has been applied to various optimization problems of which variables are continuous. However, there are seldom trials for visualization of searching process. This paper proposes a new visualization tool for searching process particle swarm optimization(PSO) algorithm. The proposed tool is effective for understanding the searching process of PSO method and educational for students.

• Structural Engineering and Mechanics
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• v.51 no.2
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• pp.229-248
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• 2014

In this paper, the development of a new optimization software for finite element model updating of engineering structures titled as FemUP is described. The program is used for computational FEM model updating of structures depending on modal testing results. This paper deals with the FE model updating procedure carried out in FemUP. The theoretical exposition on FE model updating and optimization techniques is presented. The related issues including the objective function, constraint function, different residuals and possible parameters for FE model updating are investigated. The issues of updating process adopted in FemUP are discussed. The ideas of optimization to be used in FE model updating application are explained. The algorithm of Sequential Quadratic Programming (SQP) is explored which will be used to solve the optimization problem. The possibilities of the program are demonstrated with a three dimensional steel frame model. As a result of this study, it can be said that SQP algorithm is very effective in model updating procedure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.414-421
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• 2015

This paper presents a new method to decompose a duplicated ECG signal, which is measured from two people, to two individual ECG signals. In paper, it is shown that the duplicated ECG signal can be decomposed, provided that their SAECG signals are known. As the SAECG signal is the average of a ECG signal, it is a feature to identify individual ECG signals from the duplicated signal. Since the ECG signal is nearly periodic, so-called heart-rate, the period of each ECG signal can be found by using the autocorrelation of the duplicated signal, That is, the autocorrelation has high peaks at the multiple instants of heart-rate of each person. With the heart-rate of each person obtained by some processing, all R-peaks are identified by the SAECG signals. To be concrete, the SAECG signal of each person is repeatedly placed at the R-peak instants with his heart-rate, and the weight of each SAECG signal is computed by LMSE optimization. Finally, as adding the error signal in the LMSE optimization processing to the weighted SAECG signal, each individual ECG signal is obtained. In experimental results, we demonstrate that the duplicated ECG signal is successfully decomposed into two ECG signals.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.89-100
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• 2008

In an urban network controlled by traffic signals, there is an interaction between the signal timing and the routes chosen by individual road users. This study develops a bi level programming model for traffic signal optimization in networks with path based traffic assignment. In the bi level programming model, genetic algorithm approach has been proposed to solve upper level problem for a signalized road network. Path based traffic assignment using column generation technique which is proposed by M.H. Xu, is applied at the lower-level. Genetic Algorithm provieds a feasible set of signal timings within specified lower and upper bounds signal timing variables and feeds into lower level problem. The performance of this model is investigated in numerical experiment in a sample network. In result, optimal signal settings and user equilibrium flows are made.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.84-92
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• 2006

Freeway Corridors consist of urban freeways and parallel arterials that drivers can use alternatively. Ramp metering in freeways and signal control in arterials are contemporary traffic control methods that have been developed and applied in order to improve traffic conditions of freeway corridors. However, most of the existing studies have focused on either optimal ramp metering in freeways, or progression signal strategies between arterial intersections. There have been no traffic control systems in Korea that integrates the freeway ramp metering and arterial signal control. The effective control strategies for freeway operations may cause negative effects on arterial traffic. On the other hand, traffic congestion and bottleneck phenomenon of arterials due to the increasing peak-hour travel demand and ineffective signal operation may generate an accessibility problem to freeway ramps. Thus, the main function of the freeway which is the through-traffic process has not been successful. The purpose of this study is to develop an integrated control model that connects freeway ramp metering systems and signal control systems in arterial intersections. And Optimization of integrated control model which consists of ramp metering and signal control is another purpose. Optimization results are verified by comparison with the results from MATDYMO.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.