Pulsed eddy current (PEC) technology has become a burgeoning method for detection and analysis of multi-layer conductive structures owing to rich time and frequency domain information presented by PEC signals. In this...
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Pulsed eddy current (PEC) technology has become a burgeoning method for detection and analysis of multi-layer conductive structures owing to rich time and frequency domain information presented by PEC signals. In this study, PEC technique is applied to characterise hidden-defect parameters while nondestructively inspecting multi-layer structures. A projectionpursuit (PP) feature extraction method based on the information divergence index is investigated to effectively analyse PEC signals. An improved accelerating genetic algorithm is adopted to find the optimal projection direction. The signal's dimension is reduced with minimal information loss while the data's structure is preserved to the greatest degree. The features extracted on the basis of PP are simultaneously employed in crack localisation and crack length quantitative evaluation combined with a SVM classifier. The theoretical analysis and experimental results demonstrate that compared with the principal component analysis method, the features extracted by the presented PP algorithm work better for simultaneously characterising crack's depth and size information and it reflect the inherent laws of the data, which make the features more physically interpretation meanwhile. Inversion accuracy for smaller and deeper cracks is enhanced obviously which will be helpful for crack localisation and quantitative identification of crack parameters in difficult situations.
Karst water is one of the main drinking water sources in North China. The single factor method and projection pursuit algorithm (PPA) are employed to assess the karst water quality of the Baotu spring area in Jinan. T...
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Karst water is one of the main drinking water sources in North China. The single factor method and projection pursuit algorithm (PPA) are employed to assess the karst water quality of the Baotu spring area in Jinan. The water quality distribution pattern, its causes, and the main groundwater pollution sources are analyzed. The water quality evaluation results of the PPA model are more reliable than those of the single factor method because the PPA model comprehensively considers the weight and correlation of various factors. The water quality of the study area is generally excellent, but the NO3− index content is high. In recent years, the water quality grades have been mainly class II ∼ class IV. The driving factors of water quality evolution are not only human activities, including artificial recharge, but also natural factors, such as carbonate mineral dissolution. These factors control both the distribution and evolution trend of water quality. Urban nonpoint sources have a significant impact on groundwater quality. Based on the current water quality situation, it is urgent to strengthen protection of the ecological environment in the southern recharge area of the spring region and the water quality control in the western region.
Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great diffi...
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ISBN:
(纸本)9783037857519
Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great difficult to the operation of the power system. Most of the existing small-scale hydropower forecasting is considered as the load forecasting factors, and there is not effective forecasting method. This paper establishes an output power forecasting model of the small-scale hydropower based on projectionpursuit. The simulation results show that the new algorithm has a strong practical application in the small-scale hydropower output power forecasting and the forecast accuracy meets the scheduling requirements.
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved projectionpursuit based on Ant Colony Optimization algorithm ...
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ISBN:
(纸本)9783642163968
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method-an improved projectionpursuit based on Ant Colony Optimization algorithm was introduced. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result. The study provides a novel algorithm for the high-dimension data clustering.
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