• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.4
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• pp.36-45
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• 2014

Model based control methods are widely used to improve the control performance of diesel engine air systems because the control results of the air system significantly affect the emission level and drivability. However, the model based control algorithm requires a lot of unmeasurable states which are hard to be measured in a mass production engine. In this study, an air system model of the diesel engine is proposed to estimate 11 unmeasurable states using only sensors equipped in a mass production engine. In order to improve the estimation performance in the transient condition, dynamic characteristics of the air system are analyzed and implemented as discrete filters. Turbine and compressor efficiency models are also proposed to overcome a limitation of the constant or look-up table based efficiency values. The proposed air system model was validated in steady state and transient conditions by real-time engine experiments. The maximum error of the estimation for 11 physical states was 11.7%.

• Journal of Power Electronics
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• v.16 no.1
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• pp.351-361
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• 2016

Power quality is a critical issue in distribution systems, where a dynamic voltage restorer (DVR) is commonly used to mitigate the voltage disturbances for loads. This paper deals with a nonlinear control for the three-phase four-wire (3P-4W) DVR under a grid voltage unbalance and nonlinear loads in the distribution system, where a novel control scheme based on the feedback linearization technique is proposed. Through feedback linearization, a nonlinear model of a DVR with a PWM voltage-source inverter (VSI) and LC filters is linearized. Then, the controller design of the linearized model is performed by applying the linear control theory, where the load voltages are kept constant by controlling the d-q-0 axis components of the DVR output voltages. To keep the load voltage unchanged, an in-phase compensation strategy is employed, where the load voltages are recovered to be the same as the previous voltage without a change in the magnitude. With this strategy, the performance of the DVR becomes faster and more stable even under unbalanced source voltages and nonlinear loads. The validity of the proposed control strategy has been verified by simulation and experimental results.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2339-2345
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• 2011

This paper proposes a new multi-filtered inertial navigation system to estimate the attitude and position of moving objects. This system has two states, the one is attitude state and the other is position/velocity state. For compensating IMU sensor errors, each of the two states uses a different filter: the attitude state uses the EKF and the position state uses the UPF. The fast and precise characteristics of the EKF have been properly utilized for the attitude estimation, while superior dynamic characteristics of the UPF have been fully adopted for the position estimation. The combination of these two filters in an inertial navigation system improves the system performance to be faster and more accurate. Experimental results demonstrate the superiority of this approach comparing to the conventional ones.

• Journal of the Korean Society for Precision Engineering
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• v.5 no.3
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• pp.81-90
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• 1988

In recent, the requirement of remote control of hychaulic system is in- creasing. The actuator unit whose output position is proportional to input electrical signal needs a pressure reducer and a dump valve. The pressure reducer provides a constant regulated pressure and filters contaminants. The dump valve supplies proper pressure to the pressure reducer and unloads when the system is not operated. In this thesis, dump valve and pressure reducer with auxiliary function are studied. The choke in the pressure reducer prevents actuator from supplying higher pressure than necessary pressure at beginning, and the spring constant affects on the dynamic characterisics. In dump valve, it is proved that diameter of servo-slide hold and choke diameter of dump plunger affects on damping response.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.1
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• pp.31-38
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• 1991

A direct digital control technique of a current source using the phase-controlled rectifier is presented. A digital firing technique without sensing the line voltage is proposed. This scheme generates firing pulses directly from error signal between command and output voltage. Thus the phase detection transformers filters and zero-crossing detector are unnecessary. The synchronism is modeled and analized. Also a software synchronization algorithm is presented without a look up table and controls the system in real time with fast dynamic characteristics. Using the single-chip microprocessor 8097BH, the direct digital control is implemented with minimal hardware structure. Using the time-weighted performance index, the optimal discrete IPM control technique is also proposed to control the current of the PCR.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.803-807
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• 2001

This paper presents a new control scheme for a DVR (Dynamic Voltage Restorer) system consisting of series voltage source PWM converters. To control the negative sequence components of the source, it is necessary to detect the negative sequence components. Generally, a filtering process is used which has some undesirable effects. This paper suggests a new method for separating positive and negative sequences components. This control system is designed using differential controllers and digital filters. The positive and negative sequences are extracted and controlled individually. The performance of the presented controller and scheme are confirmed through simulation and actual experiment with a 2.5kVA prototype DVR system.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.792-799
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• 2008

Motion estimation is crucial in a remote control for its convenience or accuracy. Time delays, however, can occur in the problem because data communication is required through a network. In this paper, state estimation problem with uncertain time delayed measurements is addressed. In dynamic system with noise, after taking measurements, it often requires some time until that is available in the filter algorithm. Standard filters not considering this time delays cannot be used since the current measurement is related with a past state. These delayed measurements are solved with augmented extended Kalman filter, and the uncertainty of delayed time is also resolved based on an explicit formulation. The proposed method is analyzed and verified by simulations.