A combination of three algorithms is proposed that gives a reasonable rate of success in image similarity searches. All use region based statistical measures using a combination of color features, linear features, and...
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A combination of three algorithms is proposed that gives a reasonable rate of success in image similarity searches. All use region based statistical measures using a combination of color features, linear features, and intensities combined with a collection of methods for subdividing the image into regions. All possible combinations were evaluated and the best (highest success rate) was selected to be used as a practical image similarity measure. Component algorithms are combined using a simple decision fusion voting scheme, giving success rates in the range of 50-60%. For a web search application this is quite reasonable.
A combination of three algorithms is proposed that gives a reasonable rate of success in image similarity searches. All use region based statistical measures using a combination of color features, linear features, and...
详细信息
ISBN:
(纸本)1891706179
A combination of three algorithms is proposed that gives a reasonable rate of success in image similarity searches. All use region based statistical measures using a combination of color features, linear features, and intensities combined with a collection of methods for subdividing the image into regions. All possible combinations were evaluated and the best (highest success rate) was selected to be used as a practical image similarity measure. Component algorithms are combined using a simple decision fusion voting scheme, giving success rates in the range of 50-60%. For a web search application this is quite reasonable.
Polarization diversity radar measurements such as reflectivity factor, differential reflectivity, and differential propagation phase are extensively used in rainfall estimation. algorithms to estimate rainfall from po...
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Polarization diversity radar measurements such as reflectivity factor, differential reflectivity, and differential propagation phase are extensively used in rainfall estimation. algorithms to estimate rainfall from polarimetric radar measurements are based on a model for the raindrop shape as a function of drop diameter. Most of the current algorithms use an equilibrium shape-size model for raindrops. Variation of the prevailing mean raindrop shapes from an assumed model has a direct impact on the accuracy of radar rainfall estimates. This paper develops composite algorithms to estimate rainfall from polarimetric radar data without an a priori assumption about the specific form of mean raindrop shape-size model such as equilibrium shape model. The accuracy of rainfall estimates is evaluated in the presence of random measurement errors as well as systematic bias errors. The composite algorithms, independent of a prespecified raindrop shape model, were applied to radar parameters simulated from disdrometer data collected over 3 months, and the corresponding rainfall estimates were found to be in good agreement with disdrometer estimates. The composite algorithms were also tested with Colorado State University CHILL radar observations of the 28 July 1997 Fort Collins (Colorado) flood event. The storm total precipitation estimates based on the composite algorithms developed in this paper were in much better agreement with rain gauge estimates in comparison with conventional algorithms.
The paper contains experimental analysis of efficiency of a new class of algorithms i.e. composite algorithms aimed at solving of knapsack problem providing global optimal solution. These algorithms combine features o...
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ISBN:
(纸本)9781538694688
The paper contains experimental analysis of efficiency of a new class of algorithms i.e. composite algorithms aimed at solving of knapsack problem providing global optimal solution. These algorithms combine features of branch and bound algorithms together with branch and cut procedure as latter is used dynamic programming: verified are modifications of branch and bound procedures, which cut off "bad" vectors of variables technique includes technologies used in dynamic programming, and vice versa - modifications of the algorithm, which implements dynamic programming, are involving bounds determination procedures used by branch and bound methods. It is shown experimentally that, in the case of the knapsack problem, the efficiency of these algorithms exceeds the effectiveness of the parent procedures, and this difference increases with the number of variables
The paper contains experimental analysis of efficiency of a new class of algorithms i.e. composite algorithms aimed at solving of knapsack problem providing global optimal solution. These algorithms combine features o...
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ISBN:
(数字)9781538694688
ISBN:
(纸本)9781538694695
The paper contains experimental analysis of efficiency of a new class of algorithms i.e. composite algorithms aimed at solving of knapsack problem providing global optimal solution. These algorithms combine features of branch and bound algorithms together with branch and cut procedure - as latter is used dynamic programming: verified are modifications of branch and bound procedures, which cut off "bad" vectors of variables technique includes technologies used in dynamic programming, and vice versa - modifications of the algorithm, which implements dynamic programming, are involving bounds determination procedures used by branch and bound methods. It is shown experimentally that, in the case of the knapsack problem, the efficiency of these algorithms exceeds the effectiveness of the parent procedures, and this difference increases with the number of variables.
This study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid ...
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This study focuses on the optimization of geopolymer composites, considering the parameters of composition and performance. The research explores the integration of algorithm-built hybrid implementations and a hybrid intelligent system to solve complex optimization problems in geopolymer composite materials. Firstly, an algorithm-built hybrid implementation is proposed, combining experimental results with various data processing methods. This approach enables the utilization of composite algorithms, offering several advantages, such as scalability and adaptability to different loads. The models developed in this study provide a flexible and extensible architecture, allowing for efficient problem-solving in optimization tasks. Secondly, a hybrid intelligent system is introduced, comprising statistical simulation models that combine different control and design problem-solving approaches. Markov chains are employed to address the quantitative aspects of loosely structured tasks and process performance evaluation. Criterion methods are utilized for quantitative conclusions, ensuring the optimal adaptation of the results from both applications. The research culminates in the identification of the optimal composition, denoted as G + FC + CFI, with specific weight content. This composition consists of cement, activator, fireclay, and carbon fiber I, with 100 g, 90 g, 100 g, and 2.5 g, respectively. The findings from this study provide valuable insights into the optimization of geopolymer composites, employing algorithm-built hybrid implementations and a hybrid intelligent system. The proposed approaches offer enhanced efficiency and accuracy in solving complex optimization problems in the field of geopolymer composite materials. The identified optimal composition demonstrates the potential for improving performance in composition and weight content.
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