• Journal of Power Electronics
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• v.17 no.5
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• pp.1327-1337
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• 2017

Photovoltaic (PV) systems are influenced by disproportionate impacts on energy production caused by frequent mismatch cases. The occurrence of multiple maximum power points (MPPs) adds complexity to the tracking process in various PV systems. However, current maximum-power point tracking (MPPT) techniques exhibit limited performance. This paper introduces an enhanced simulated annealing (ESA)-based GMPPT technique against multiple MPP issues in P-V curve with different PV system structures. The proposed technique not only distinguishes global and local MPPs but also performs rapid convergence speed and high tracking accuracy of irradiance changing and restart capability detection. Moreover, the proposed global maximum power tracking algorithm can be applied in the central converter of DMPPT and hybrid PV system to meet various application scenarios. Its effectiveness is verified by simulation and test results.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.637-644
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• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.329-334
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• 1998

This work is concerned with the problem of tracking a maneuvering target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly maneuvering tar-get. Many researches have been performed in tracking a maneuvering target. The conventional Interacting Multiple Model (IMM) filter is well known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation scheme. The subfilters of IMM can be considered as fusing its initial value with new measurements. This approach is also shown in this paper. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a maneuvering target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly maneuvering target as well as it reduces maximum values of estimation errors when maneuvering starts and finishes. The effectiveness of the pro-posed method is validated through simulation by comparing it with the conventional IMM algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1082-1095
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• 2019

Camera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• Journal of Digestive Cancer Research
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• v.4 no.2
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• pp.92-98
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• 2016

The barcelona clinic liver cancer (BCLC) staging systemis regarded as the optimal staging system to predict prognosis and guide treatmentfor hepatocellular carcinoma (HCC) .According to the BCLC classification, only patients with BCLC A stage should undergo liver resection. In contrast, patients with intermediate-advanced HCC should be scheduled for palliative therapies,such as transcatheter arterial chemoembolization (TACE) and target therapy, even if the lesion is resectable. More and more studies report good short-term and long-term outcomes in patients with intermediate-advanced HCC treated by radical resection and many patients benefited from curative resection. The aim of this review was to evaluate the role of surgery beyond the BCLC recommendations. A revision of the BCLC algorithm should be proposed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• Structural Engineering and Mechanics
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• v.75 no.6
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• pp.747-769
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• 2020

This paper provides a semi-analytical approach to investigate the variations of 3D displacement components, electric potential, stresses, electric displacements and transverse vibration frequencies in laminated piezoelectric composite plates based on the scaled boundary finite element method (SBFEM) and the precise integration algorithm (PIA). The proposed approach can analyze the static and dynamic responses of multilayered piezoelectric plates with any number of laminae, various geometrical shapes, boundary conditions, thickness-to-length ratios and stacking sequences. Only a longitudinal surface of the plate is discretized into 2D elements, which helps to improve the computational efficiency. Comparing with plate theories and other numerical methods, only three displacement components and the electric potential are set as the basic unknown variables and can be represented analytically through the transverse direction. The whole derivation is built upon the three dimensional key equations of elasticity for the piezoelectric materials and no assumptions on the plate kinematics have been taken. By virtue of the equilibrium equations, the constitutive relations and the introduced set of scaled boundary coordinates, three-dimensional governing partial differential equations are converted into the second order ordinary differential matrix equation. Furthermore, aided by the introduced internal nodal force, a first order ordinary differential equation is obtained with its general solution in the form of a matrix exponent. To further improve the accuracy of the matrix exponent in the SBFEM, the PIA is employed to make sure any desired accuracy of the mechanical and electric variables. By virtue of the kinetic energy technique, the global mass matrix of the composite plates constituted by piezoelectric laminae is constructed for the first time based on the SBFEM. Finally, comparisons with the exact solutions and available results are made to confirm the accuracy and effectiveness of the developed methodology. What's more, the effect of boundary conditions, thickness-to-length ratios and stacking sequences of laminae on the distributions of natural frequencies, mechanical and electric fields in laminated piezoelectric composite plates is evaluated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.