• Journal of the Korean Society for Railway
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• v.17 no.3
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• pp.171-177
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• 2014

PWM converter trains exhibit excellent load characteristics in comparison with conventional phase-controlled trains with low power factors, as they can be operated at power factors which are close to unity by means of a voltage vector control method. However, in the case of a high track density or extended feeding, significant line losses and voltage drops can occur. Instead of operating these trains at a fixed unity power factor, this paper suggests a continuous optimal power factor control scheme for each train in an effort to minimize line losses and improve voltage drops according to varying load conditions. The proposed method utilizes the steepest descent algorithm targeting each car in the same feeding section to establish the optimized reactive power compensation levels that can minimize the reactive power loss of the feeder. The results from a simulation of a sample system show that voltage drops can be improved and line losses decreased.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.1
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• pp.10-18
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• 2000

This paper proposes a fuzzy logic cross-coupled controller using a new contouring modeling for a two-axis servo system. The general decoupled control approach may result in degraded contouring performance due to such factors as mismatch of axial dynamics and axial loop gains. In practice, such systems contain many uncertainties. The cross-coupled controller utilizes all axis position error information simultaneously to produce accurate contours. However, the conventional cross-coupled controllers cannot overcome friction, backlash, and parameter variations. Also since, it is difficult to obtain an accurate mathematical model of multi-axis system, here we investigate a fuzzy logic cross-coupled controller of servo system. In addition, new contouring error vector computation method is presented. The experimental results are presented to illustrate the performance of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1424-1435
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• 2000

A bit-rate controller that can achieve a constant bit rate when coding object-based video sequences is an important part to achieve an adaptation to bit-rate constraints, desired video quality, distribution of bits among objects, relationship between texture and shape coding, and determination of frame skip or not. Therefore we design content-based bit rate controller which will be used for relevant bit-rate control. The implementation is an extension of MPEG-4 rate control algorithm which employs a quadratic rate-quantizer model. The importance of different objects in a video is analyzed and segmented into a number of VOPs which are adaptively bit-allocated using the object-based modelling. Some test sequences are observed by a number of non-experts and interests in each object are analysed. The initial total target bit-rate for all objects is obtained by using the proposed technique. Then the total target bits are jointly analyzed for preventing from overflow or underflow of the buffer fullness. The target bits are distributed to each object in view of its importance, not only of statistical analysis such as motion vector magnitude, size of object shape, and coding distortion of previous frame. The scheme is compared with the rate controller adopted by the MPEG-4 VM8 video coder by representing their statistics and performance.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.411-420
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• 1999

In geology, lineament features have been used to identify geological events, and many of scientists have been developed the algorithm that can be applied with the computer to recognize the lineaments. We choose several edge detection filter, line detection filters and Hough transform to detect an edge, line, and to vectorize the extracted lineament features, respectively. firstly the edge detection filter using a first-order derivative is applied to the original image In this step, rough lineament image is created Secondly, line detection filter is used to refine the previous image for further processing, where the wrong detected lines are, to some extents, excluded by using the variance of the pixel values that is composed of each line Thirdly, the thinning process is carried out to control the thickness of the line. At last, we use the Hough transform to convert the raster image to the vector one. A Landsat image is selected to extract lineament features. The result shows the lineament well regardless of directions. However, the degree of extraction of linear feature depends on the values of parameters and patterns of filters, therefore the development of new filter and the reduction of the number of parameter are required for the further study.

• Tribology and Lubricants
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• v.28 no.3
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• pp.130-135
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• 2012

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.58-62
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• 2008

The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1049-1053
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• 1999

It is important to measuring and monitoring about state vectors of power system for precise operation control. All state vectors cannot be measured because it is economically disadvantageous, so that some state vectors are determined using state estimator. Determination of observability is a important precondition of power system state estimation because state estimation can be performed when given power system is observable. Recently as time-synchronization technique progress, using the PMU(Phasor Measurement Unit), state vector can be measured directly so that voltage phasor and current phasor measurements can be used for power system estimation. In this paper, observability algorithm is proposed to determinate the observability with real/reactive injection power measurements and real/reactive lineflow power measurements of existing measurement system and with phasor measurements of PMU. The jacobian matrix is newly composed for state estimation with measurements of added PMU, and state estimation is performed with least square estimatior. Comparison between state estimation result of existing measurement system and that of measurement system added PMU is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.639-650
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• 1999

In this paper, a chattering alleviation VSS controller for the sinusoidal type BLDC motor is designed. Dead Zone function is proposed to change the chattering occurring in the transient state from high frequency to low frequency and time-varying gains are applied for the control input to eliminate the steady state excessive chattering in the conventional ISM. The proposed Dead Zone function represents the sliding layer composed of two switching surfaces and if a state vector exists in this layer, the chattering don't occur. Simulation and experimental results confirm the useful effects of the above algorithm.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.