• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.3
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• pp.150-157
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• 2002

This paper presents preliminarily an implementation of digital high-performance motion control system of Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The system consist of stator flux observer, torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320F240 DSP controller made by Texas Instruments. The stator fluff observer is based on the combined voltage and current model with stator flux feedback adaptive control, and the input of the observer are the stator voltage and current of motor terminal for wide speed range. The rotor position and speed sensor used 6000 pulse/rev encoder. In order to prove rightness of the suggested control algorithm, we have some simulation and actual experimental system at $\pm$20 and $\pm$2000 rpm. The developed digitally high-performance motion control system+ are shown a good response characteristic of control results and high performance features using 1.0kW RSM which has 2.57 Ld/Lq salient ratio.

• Journal of the Korean Society of Combustion
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• v.3 no.2
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• pp.13-27
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• 1998

The optimization of frontal device including fuel nozzle and swirler is required to secure the mixing of fuel and air and the combustion stability leading the reduction of pollutant emissions and the increase of combustion efficiency in gas turbine combustor. The effects of injection nozzle and swirler on the flow field, spray characteristics and consequently the combustion stability, were experimentally investigated by measuring the velocity field, droplet sizes of fuel spray, lean combustion limit and the temperature field in the main combustion region. Flow fields and spray characteristics were measured with APV(Adaptive Phase Doppler Velocimetry) under atmospheric condition using kerosine fuel. Temperatures were measured by Pt-Pt13%Rh, R-type thermocouple which was 0.2mm thick. Spray and flame was visualized by ICCD(Intensified Charge Coupled Device) camera. It was found that the dual swirler resulted in the biggest recirculation zone with the highest reverse flow velocity at the central region, which lead the most stable combustion. The various combustion characteristics were observed as a function of the geometries of injector and swirler, that gave a tip for the better design of gas turbine combustor.

• Journal of Power System Engineering
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• v.20 no.2
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• pp.58-65
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• 2016

Lately, tugboats are widely used to maneuver vessels by pushing or towing them where tugboats use rope. In order to correctly control the motion of tugboat and towed vessel, the dynamics of the towline would be well identified. In real application environment, the towing rope length changes and the towing load is not constant due to the various sizes of towed vessel. And there are many ropes made by many types of materials. It means that it is not easy to obtain rope dynamics, such that it is too difficult to satisfy the given control purpose by designing control system. Thus real time identification or adaptive control system design method may be a solution. However it is necessary to secure sufficient information about rope dynamics to obtain desirable control performance. In this paper, the authors try to have several rope dynamic models by changing the rope length to consider real application conditions. Among them, a representative model is selected and the others are considered as uncertain models which are considered in control system design. The authors design a robust control to cope with strong uncertain and nonlinear property included in the real plant. The designed control system based on robust control framework is evaluated by simulation.

• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.670-686
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• 2017

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6243-6259
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• 2019

As a foundation of next-generation air transportation systems, automatic dependent surveillance-broadcast (ADS-B) helps pilots and air traffic controllers create a safer and more efficient national airspace system. Owing to the open communication environment, it is easy to insert fake aircraft into the system via spoofing or the insertion of false messages. Efforts have thus been made in academic research and practice in the aviation industry to ensure the security of transmission of messages of the ADS-B system. An identity-based batch verification (IBV) scheme was recently proposed to enhance the security and efficiency of the ADS-B system, but current IBV schemes are often too resource intensive because of the application of complex hash-to-point operations or bilinear pairing operations. In this paper, we propose a lightweight IBV signature scheme for the ADS-B system that is robust against adaptive chosen message attacks in the random oracle model, and ensures the security of batch message verification and against the replaying attack. The proposed IBV scheme needs only a small and constant number of point multiplication and point addition computations instead of hash-to-point or pairing operations. Detailed performance analyses were conducted to show that the proposed IBV scheme has clear advantages over prevalent schemes in terms of computational cost and transmission overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2762-2777
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• 2017

A fuzzy identity based signature (FIBS) scheme allows a signer with identity ${\omega}$ to generate a signature which could be verified under identity ${\omega}^{\prime}$ if and only if ${\omega}$ and ${\omega}^{\prime}$ are within a certain distance of each other as judged by some metric. In this paper, we propose an efficient FIBS scheme from lattice assumption, which can resist quantum-computer attacks. Without using the Bonsai Tree technique, we utilize the lattice basis delegation technique to generate the private key, which has the advantage of keeping the lattice dimension invariant. We also prove that our proposed scheme is existentially unforgeable under an adaptive chosen message and identity attack in the random oracle model. Compared with existing scheme, our proposed scheme is much more efficient, especially in terms of communication overhead. Since our FIBS scheme possesses similar error-tolerance property, it can be well applied in post-quantum communication biometric authentication environments, where biometric identifiers such as fingerprints, voice, iris and gait are used in human identification.

• Journal of Korea Game Society
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• v.8 no.1
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• pp.79-90
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• 2008

This paper describes a motivation-based hierarchical behavior planning framework to allow autonomous agents to select adaptive actions in simulation game environments. The combined behavior planning system is formed by four levels of specification, which are motivation extraction, goal list generation, action list determination and optimization. Our model increases the complexity of virtual human behavior planning by adding motivation with sudden and cumulative attributes. The motivation selection by probability distribution allows NPC to make multiple decisions in certain situations in order to embody realistic virtual humans. Hierarchical goal tree enhances the effective reactivity. Optimizing for potential actions provides NPC with safe and satisfying actions to adapt to the virtual environment. A restaurant simulation game was used to elucidate the mechanism of the framework.

• KIEAE Journal
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• v.14 no.3
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• pp.71-77
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• 2014

This study aimed at developing and evaluating performance of the two logics for respectively operating two-position- and variable-heating systems. Both logics control the heating system and openings of the double skin facade buildings in an integrated manner. Artificial neural network models were applied for the predictive and adaptive controls in order to optimally condition the indoor thermal environment. Numerical computer simulation methods using the MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation) were employed for the performance tests of the logics in the test module. Analysis on the test results revealed that the variable control logic provided more comfortable and stable temperature conditions with the increased comfortable period and the decreased standard deviation from the center of the comfortable range. In addition, the amount of heat supply to the indoor space was significantly reduced by the variable control logic. Thus, it can be concluded that the optimal control method using the artificial neural network model can work more effectively when it is applied to the variable heating systems.