• Journal of Power Electronics
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• v.16 no.1
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• pp.277-286
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• 2016

This paper offers two Maximum Power Point Tracking (MPPT) systems for Photovoltaic (PV) applications. The first MPPT method is based on a fixed frequency Model Predictive Control (MPC). The second MPPT technique is based on the Predictive Hysteresis Control (PHC). An experimental demonstration shows that the proposed techniques are fast, accurate and robust in tracking the maximum power under different environmental conditions. A DC/DC converter with a high voltage gain is obligatory to track PV applications at the maximum power and to boost a low voltage to a higher voltage level. For this purpose, a high gain Switched Inductor Quadratic Boost Converter (SIQBC) for PV applications is presented in this paper. The proposed converter has a higher gain than the other transformerless topologies in the literature. It is shown that at a high gain the proposed SIQBC has moderate efficiency.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.42-43
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• 2020

Recently as construction projects have been complicated, it has been wearing thin to draw on their personal experiences for management. Although the advance of IT, using the technology for construction is insufficient in contrast to other industries. BIM has been steadily used in construction projects, but it is not easy to find the case of successful use. This research considers ways in which BIM technology can be applied to useful management on a construction site and derives a method of application of using BIM for a Mega-complex Building Project especially.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.523-536
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• 2013

A recent air defense missile system is required to have a capability to intercept short-range super-high speed targets such as tactical ballistic missile(TBMs) by performing engagement control efficiently. Since flight time and distance of TBM are very short, the missile defense system should be ready to engage a TBM as soon as it takes an indication of the TBM launch. As a result, it has to predict TBM trajectory accurately with cueing information received from an early warning system, and designate search direction and volume for own radar to detect/track TBM as fast as it can, and also generate necessary engagement information. In addition, it is needed to engage TBM accurately via transmitting tracked TBM position and velocity data to the corresponding intercept missiles. In this paper, we proposed a method to estimate TBM trajectory based on the Kepler's law for the missile system to detect and track TBM using the cueing information received before the TBM arrives the apogee of the ballistic trajectory, and analyzed the bias of prediction error in terms of the transmission period of cueing data between the missile system and the early warning system.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.2
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• pp.89-95
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• 2009

Tracking the Maximum Power Point(MPP) of a photovoltaic(PV) array is usually an essential part of a PV system. The problem considered by MPPT techniques is to find the voltage $V_{MPP}$ or current $I_{MPP}$ at which a PV array should operate to generate the maximum power output PMPP under a given temperature and irradiance. The MPPT control methods, such as the perturb and observe method and the incremental conductance method require microprocessor or DSP to determine if the duty cycle should be increased or not. This paper proposes a simple and fast analog MPPT method. The proposed control scheme will track the MPP very fast and its hardware implementation is so simple, compared with the conventional techniques. The new algorithm has successfully tracked the MPP, even in case of rapidly changing atmospheric conditions, and Has higher efficiency than ordinary algorithms.

• Journal of Integrative Natural Science
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• v.15 no.3
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• pp.99-110
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• 2022

Solar photovoltaic (PV) system shows a non-linear current (I) -voltage (V) characteristics, which depends on the surrounding environment factors, such as irradiance, temperature, and the wind. Solar PV system, with current (I) - voltage (V) and power (P) - Voltage (V) characteristics, specifies a unique operating point at where the possible maximum power point (MPP) is delivered. At the MPP, the PV array operates at maximum power efficiency. In order to continuously harvest maximum power at any point of time from solar PV modules, a good MPPT algorithms need to be employed. Currently, due to its simplicity and easy implementation, Perturb and Observe (P&O) algorithms are the most commonly used MPPT control method in the PV systems but it has a drawback at suddenly varying environment situations, due to constant step size. In this paper, to overcome the difficulties of the fast changing environment and suddenly changing the power of PV array due to constant step size in the P&O algorithm, least mean Square (LMS) methods is proposed together with P&O MPPT algorithm which is superior to traditional P&O MPPT. PV output power is predicted using LMS method to improve the tracking speed and deduce the possibility of misjudgment of increasing and decreasing the PV output. Simulation results shows that the proposed MPPT technique can track the MPP accurately as well as its dynamic response is very fast in response to the change of environmental parameters in comparison with the conventional P&O MPPT algorithm, and improves system performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.123-128
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• 2018

In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.