• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.7-14
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• 2015

To implement an automatic picking system (APS) in distribution center with high precision and high dynamics, this paper presents a high gain observer-based robust speed controller design for a linear induction motor (LIM) drive. The force disturbance as well as the mechanical parameter variations such as the mass and friction coefficient gives a direct influence on the speed control performance of APS. To guarantee a robust control performance, the system uncertainty caused by the force disturbance and mechanical parameter variations is estimated through a high gain disturbance observer and compensated by a feedforward manner. While a time-varying disturbance due to the mass variation can not be effectively compensated by using the conventional disturbance observer, the proposed scheme shows a robust performance in the presence of such uncertainty. A Simulink library has been developed for the LIM model from the state equation. Through comparative simulations based on Matlab - Simulink, it is proved that the proposed scheme has a robust control nature and is most suitable for APS.

• Journal of the Society of Naval Architects of Korea
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• v.29 no.4
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• pp.152-172
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• 1992

The ship sailing among waves are suffered the various wave loads that comes from its motion throughout its life. Because there are dynamic, the analysis of ship structure must be considered as the dynamic problem precisely. In the rationally-based design, the dynamic structural analysis is carried out using dynamic wave loads provided from the results of the ship mouton calculation as the rigid body. This method is based on the linear theory assumed low wave height and small amplitude of motion. But at the rough sea condition, high wave height, relatively ship's depth, is induced the large ship motion, so the ship section configulation below water line is rapidly changed at each time. This results in non-linear problem. Considering above situation in this paper, the strength analysis method is introduced for the hull glider among waves considering non-linear hydrodynamic forces. This paper considers that the overall or primary level of the ship structural dynamic loading and dynamic response provided from the non-linear wave forces, and bottom and bow flare impact forces estimated by momentum slamming theory, in which the ship is idealized as a hollow thin-walled box beam using thin-walled beam theory and the finite element method. This method is applied to 40,000 Ton Double-Skin Tanker and attention is paid to the influence of the response of ship speed, wave length and wave height compared with linear strip theory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.134-145
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• 2021

It is common to optimize the propulsion control system through a so-called tuning process that modifies the parameter values of the propulsion control software during a ship commissioning. However, during this process, if the error of the initial setting value is large, the tuning time may take too long, or the propulsion equipment can be seriously damaged. Therefore, we conducted research on the design of a propulsion controller that applied a Processor lever controller even for inexperienced people with relatively little experience in tuning propulsion control software to be able to reduce the tuning time while protecting the propulsion system. Through simulation, by comparing the execution result of propulsion control lever commands through the PI controller without applying the Processor lever controller. We analyzed the improvement of the Overshoot and propulsion performance. The simulation results showed that the safety of the propulsion system increased because Overshoot of approximately 9.74%, which occurred when the Processor lever function was not applied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2792-2799
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• 2015

In this paper, we utilize source mel-frequency cepstral coefficients (SMFCCs), skewness, and kurtosis extracted in glottal flow signals to improve speaker recognition performance. Generally, because the high band magnitude response of glottal flow signals is somewhat flat, the SMFCCs are extracted using the response below the predefined cutoff frequency. The extracted SMFCC, skewness, and kurtosis are concatenated with conventional feature parameters. Then, dimensional reduction by the principal component analysis (PCA) and the linear discriminat analysis (LDA) is followed to compare performances with conventional systems under equivalent conditions. The proposed recognition system outperformed the conventional system for large scale speaker recognition experiments. Especially, the performance improvement was more noticeable for small Gaussan mixtures.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.33-40
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• 2009

This paper presents the high performance control of interior permanent magnet synchronous motor(IPMSM) using space vector(SV) PWM method based on hybrid artificial intelligent(HAI) controller. The HAI controller combines the advantages between adaptive fuzzy control and neural network The SV PWM method is applied to a speed control system of motor in the industry field until now and is feasible to improve harmonic rate of output current, switching frequency and response characteristics. This HAI controller is used instead of conventional PI controller in order to solve problems happening when calculating a reference voltage. The HAI controller improves speed performance by hybrid combination of reference model-based adaptive mechanism method, fuzzy control and neural network. This paper analyzes response characteristics of parameter variation, steady-state and transient-state using proposed HAI controller and this controller compares with conventional fuzzy neural network(FNN) and PI controller. Also, this paper proves validity of HAI controller.

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.61-70
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• 2003

I-Business providers and customers have chosen the service availability as the most important technical parameter which should be included in their SLA to succeed in their business. This means that web Service availability management is crucial to the web-based service providers. Application availability is originally defined as a measure of the fraction of time during a defined period when the service provided is deemed to be better than user expectation of service performance. But, because most web service availability measurement tools simply monitor disconnected state, they do not satisfy user's expectation of extended availability concept. In this paper, We propose the web service availability measurement method which supports extended availability concept. It takes account of performance degradation of web service based on response time, and determines what is the cause of unavailability of the service. We developed a measurement tool, WebSerAvail.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.111-119
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• 2002

One of the advantages of SP methods is the possibility of getting a number of responses from each respondent. However, when the repeated observations from each respondent are analysed by applying the simple modeling method, a potential problem is created because of upbiased significance due to the repeated observation from each respondent. This study uses a variety of approaches to explore this issue and to test the robustness of the simple model estimates. Among several different approaches, the Jackknife method and Kocurs method were applied. The Jackknife method was implemented using a program JACKKNIFE. The model estimate results of Jackknife method and Kocurs method were compared with those of the uncorrected estimates in order to test whether there was repeated measurement problem or not and the extent to which this problem affected the model estimates. The standard errors between the uncorrected model estimates and Jackknife estimates were also compared. The results reveals that the t-ratios of Kocurs are much lower than those of the uncorrected method and Jackknife estimates, indicating that Kocurs method underestimates the significance of the coefficients. Jackknife method produced the almost same coefficients as those of the uncorrected model but the lower t-ratios. These results indicate that the coefficients of the uncorrected method are accurate but that their significance are somewhat overestimated. In this study. 1 concluded that the repeated measurement Problem did exist in our data, but that it did not affect the model estimation results significantly. It is recommended that such a test should become a standard procedure. If it turns out that the analysis based on the simple uncorrected method are influenced by the repeated measurement problem. it should be corrected.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.4
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• pp.271-277
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• 2020

There are increasing cases of monitoring the structural response of structures using multiple sensors. However, owing to cost and management problems, limited sensors are installed in the structure. Thus, few structural responses are collected, which hinders analyzing the behavior of the structure. Therefore, a technique to predict responses at a location where sensors are not installed to a reliable level using limited sensors is necessary. In this study, a numerical study is conducted to predict the seismic response of low-rise buildings using limited information. It is assumed that the available response information is only the acceleration responses of the first and top floors. Using both information, the first natural frequency of the structure can be obtained. The acceleration information on the first floor is used as the ground motion information. To minimize the error on the acceleration history response of the top floor and the first natural frequency error of the target structure, the method for predicting the mass and stiffness information of a structure using the genetic algorithm is presented. However, the constraints are not considered. To determine the range of design variables that mean the search space, the parameter prediction method based on artificial neural networks is proposed. To verify the proposed method, a five-story structure is used as an example.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1976-1985
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• 2015

The amount of packets in palyout buffer of video streaming receiver can be changed by network condition, and saturated and exhausted by the delay and jitter. Especially, if the amount of incoming video traffic exceeds the maximum allowed playout buffer, buffer overflow problem can be generated. It makes the deterioration of video image and the discontinuity of playout by skip phenomenon. Also, if the incoming packets are delayed by network confusion, the stop phenomenon of video image is made by buffering due to buffer underflow problem. To solve these problems, this paper proposes the video streaming receiver with token bucket scheme which automatically establishes the important parameters like token generation rate r and bucket maximum capacity c adapting to the pattern of video packets. The simulation results using network simulator-2 (NS-2) and joint scalable video model (JSVM) show that the proposed token bucket scheme with automatic establishment parameter provides better performance than the existing token bucket scheme with manual establishment parameter in terms of the generation number of overflow and underflow, packer loss rate, and peak signal to noise ratio (PSNR) in three test video sequences.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.