• Journal of information and communication convergence engineering
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• v.13 no.1
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• pp.42-49
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• 2015

In laser pointer interaction systems, laser spot detection is one of the most important technologies, and most of the challenges in this area are related to the varying backgrounds, and the real-time performance of the interaction system. In this paper, we present a robust dictionary construction and update algorithm based on a sparse model of background subtraction. In order to control dynamic backgrounds, first, we determine whether there is a change in the backgrounds; if this is true, the new background can be directly added to the dictionary configurations; otherwise, we run an online cumulative average on the backgrounds to update the dictionary. The proposed dictionary construction and update algorithm for laser spot detection, is robust to the varying backgrounds and noises, and can be implemented in real time. A large number of experimental results have confirmed the superior performance of the proposed method in terms of the detection error and real-time implementation.

• Journal of the Optical Society of Korea
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• v.13 no.1
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• pp.116-122
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• 2009

In this paper, a robust method is developed to locate the irises of both eyes. The method doesn't put any restrictions on the background. The method is based on the AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in the centers of the irises. Mean crossing function and convolution template are proposed to filter out candidate points and select the iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select the iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1101-1107
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• 2017

In this paper, a robust and fast grid synchronization method of a three-phase power converter is proposed. The amplitude and phase information of grid voltages are essential for power converters to be properly connected into the utility. The phase-lock-loop in synchronous reference frame has been widely adopted for the three-phase converter system since it shows a satisfactory performance under balanced grid voltages. However, power converters often operate under abnormal grid conditions, i.e. unbalanced by grid faults and frequency variations, and thus a proper active and reactive power control cannot be guaranteed. The proposed method adopts a second order generalized integrator in synchronous reference frame to detect positive sequence components under unbalanced grid voltages. The proposed method has a fast and robust performance due to its higher gain and frequency adaptive capability. Simulation and experimental results show the verification of the proposed synchronization algorithm and the effectiveness to detect positive sequence voltage.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.93-100
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• 2024

In this paper, we propose a novel robust blind crypto-watermarking method for medical images security based on hiding of DICOM patient information (patient name, age...) in the medical imaging. The DICOM patient information is encrypted using the AES standard algorithm before its insertion in the medical image. The cover image is divided in blocks of 8x8, in each we insert 1-bit of the encrypted watermark in the hybrid transform domain by applying respectively the 2D-LWT (Lifting wavelet transforms), the 2D-DCT (discrete cosine transforms), and the SVD (singular value decomposition). The scheme is tested by applying various attacks such as noise, filtering and compression. Experimental results show that no visible difference between the watermarked images and the original images and the test against attack shows the good robustness of the proposed algorithm.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.315-322
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• 2008

Searching a location of the copied video in video database, signatures should be robust to video reediting, channel noise, time variation of frame rate. Several kinds of signatures has been proposed. Ordinal signature, one of them, is difficult to describe the spatial characteristics of frame due to the site of fixed window, $N{\times}N$, which is compute the average gray value. In this paper, I studied an algorithm of sequence matching in video copy detection for the copyright protection, employing the R-tree index method for retrieval and suggesting a robust ordinal signatures for the original video clips and the same signatures of the pirated video. Robust ordinal has a 2-dimensional vector structures that has a strong to the noise and the variation of the frame rate. Also, it express as MBR form in search space of R-tree. Moreover, I focus on building a video copy detection method into which content publishers register their valuable digital content. The video copy detection algorithms compares the web content to the registered content and notifies the content owners of illegal copies. Experimental results show the proposed method is improve the video matching rate and it has a characteristics of signature suitable to the large video databases.

• Journal of Broadcast Engineering
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• v.12 no.5
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• pp.503-515
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• 2007

A new fast motion estimation algorithm is presented in this paper. The algorithm, named Enhanced Cross-Diamond Search (ECDS), is based on the Diamond Search (DS) algorithm. The DS algorithm, even though faster than the most well-known algorithms, was found not to be very robust in terms of objective and subjective qualities for several sequences and the algorithm searches unnecessary candidate blocks. We propose a novel ECDS algorithm using a small cross search as the initial step, and large/small DS patterns as subsequent steps for fast block motion estimation. Experimental results show that the ECDS is much more robust, provides a faster searching speed, and smaller distortions than other popular fast block-matching algorithms.

• Journal of Power Electronics
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• v.11 no.3
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• pp.360-368
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• 2011

This paper proposes an active frequency with a positive feedback in the d-q frame anti-islanding method suitable for a robust phase-locked loop (PLL) algorithm using the FFT concept. In general, PLL algorithms for grid-connected PV PCS use d-q transformation and controllers to make zero an imaginary part of the transformed voltage vector. In a real grid system, the grid voltage is not ideal. It may be unbalanced, noisy and have many harmonics. For these reasons, the d-q transformed components do not have a pure DC component. The controller tuning of a PLL algorithm is difficult. The proposed PLL algorithm using the FFT concept can use the strong noise cancelation characteristics of a FFT algorithm without a PI controller. Therefore, the proposed PLL algorithm has no gain-tuning of a PI controller, and it is hardly influenced by voltage drops, phase step changes and harmonics. Islanding prediction is a necessary feature of inverter-based photovoltaic (PV) systems in order to meet the stringent standard requirements for interconnection with an electrical grid. Both passive and active anti-islanding methods exist. Typically, active methods modify a given parameter, which also affects the shape and quality of the grid injected current. In this paper, the active anti-islanding algorithm for a grid-connected PV PCS uses positive feedback control in the d-q frame. The proposed PLL and anti-islanding algorithm are implemented for a 250kW PV PCS. This system has four DC/DC converters each with a 25kW power rating. This is only one-third of the total system power. The experimental results show that the proposed PLL, anti-islanding method and topology demonstrate good performance in a 250kW PV PCS.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.80-91
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• 1987

Minimum $L_i$ norm estimation is a robust procedure ins the sense that it leads to an estimator which has greater statistical eficiency than the least squares estimator in the presence of outliers. And the $L_1$ norm estimator has some desirable statistical properties. In this paper a new computational procedure for $L_1$ norm estimation is proposed which combines the idea of reweighted least squares method and the linear programming approach. A modification of the projective transformation method is employed to solve the linear programming problem instead of the simplex method. It is proved that the proposed algorithm terminates in a finite number of iterations.