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In this paper, a novel hybrid maximum power point tracking (MPPT) method under partially shaded conditions (PSC) is proposed. The duty cycle of the dc-dc converter is controlled from small to large with fixed intervals in order to scan the P-V characteristics curve under partially shaded conditions, and then find out the maximum power value, which is called duty sweep (DS) algorithm in this thesis...
To solve the parallelism algorithm of Petri network system with parallel feature and implementing paralleling control and execution of Petri network, we propose the analysis and algorithm design of Petri network process. Based on employing P-invariants to partition the function of Petri network system, we firstly analyzed the situations of parallelism between processes, checked the effects of the...
Hidden Markov Random Field (HMRF) model and Finite Mixture Model (FMM) parameter estimation algorithm provides an interesting framework for image segmentation task, hence a technique that capitalizes on the benefits of both algorithms would achieve better performance. In this regard, we propose a new segmentation algorithm which combines with HMRF model and FMM parameter estimation algorithm. Firstly,...
This paper studies two online scheduling problems on parallel machines that process computing jobs arriving stochastically in a batch pattern. With the objective function as minimizing total completion time of weighted jobs or minimizing total latency of job completion, we propose two solutions for above scheduling problems, respectively. With the assumption that the processing time of a batch is...
CDHL algorithm is a new hybrid range-free localization algorithm, which combines the Centroid and DV-Hop algorithms. The basic theory of the algorithm was introduced in this paper, and the average localization error rate of this hybrid algorithm is discussed and the algorithm energy consumption is analyzed. Simulation experiments show that the proposed algorithm is effective on different network situations,...
It is important to integrate contextual information in order to improve the performance of automatic image annotation. Graph based representations allow incorporation of such information. In this paper, we propose a graph-based approach to automatic image annotation which models both feature similarities and semantic relations in a single graph. The annotation quality is enhanced by introducing graph...
Image semantic has the characters of vague and abstractive, therefore, only low-level feature is not enough to describe image semantics. It requires to combine with image related content in order to improve the accuracy of the image annotation In this paper, An image annotation algorithm based on mutual K adjacency graph is proposed which describes the relationship between the low-level features,...
In order to improve the computing speed of automatic image annotation. We propose a fast solution for this problem in this paper. First, the proposed approach describes the relationship between the low-level features, annotated words and image by a multi-modal graph which is linear correlation, block-wise and community-like structure. Second, we, to achieve fast solution of the problem, exploit the...
In insurance industry, frequent business variation brings new problems to Insurance database design: serious complexity, difficulties in database maintenance and updating, low efficiency in the inquiry and reconstruction of components. Formal Concept Analysis (FCA) is a technique that bases on the order theory, which can show concepts and their hierarchical structure and well solve problems faced...
This paper presents a novel address assignment algorithm (Adaptive Dynamic Address Redistribution ADAR) and tree-based routing protocol for large-scale uneven Wireless sensor networks (WSNs). The ADAR algorithm can make 16-bit address space used effectively and it also can solve the problem that nodes which want to access the WSNs can not be assigned addresses because of the father node running out...
In this paper we present a new continuous-time recurrent neurofuzzy network structure for modeling and identification of a class of nonlinear systems, using a training algorithm motivated from previous works in adaptive observers. Using only output measurements and the knowledge of an excitation input signal, the proposed network is trained by generating estimates of an ideal network and jointly identifying...
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