摘要 :
The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Util...
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The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of shortfall in renewable production. It then becomes important to be able to target the right customers among the large population, since each enrollment has a cost. The availability of high resolution information about each consumers demand consumption can significantly change how such targeting is done. This paper develops a methodology for large scale targeting that combines data analytics and a scalable selection procedure. We propose an efficient customer selection method via stochastic knapsack problem solving and a simple response modeling in one example DR program. To cope with computation issues coming from the large size of data set, we design a novel approximate algorithm.
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摘要 :
The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Util...
展开
The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of shortfall in renewable production. It then becomes important to be able to target the right customers among the large population, since each enrollment has a cost. The availability of high resolution information about each consumers demand consumption can significantly change how such targeting is done. This paper develops a methodology for large scale targeting that combines data analytics and a scalable selection procedure. We propose an efficient customer selection method via stochastic knapsack problem solving and a simple response modeling in one example DR program. To cope with computation issues coming from the large size of data set, we design a novel approximate algorithm.
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摘要 :
With the development of Big data, cloud storage and data mining, targeting advertising technology has aroused wide attention. So many companies have invested in the target mobile advertising market in this area. Targeting advertis...
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With the development of Big data, cloud storage and data mining, targeting advertising technology has aroused wide attention. So many companies have invested in the target mobile advertising market in this area. Targeting advertising can effectively save resources, reduce capital investment. Aiming at this problem, this paper proposed a new method for targeting advertising based on C4.5 algorithm and cloud storage. The mess data about users, such as user information, user browsing behavior and so on, is stored in the cloud. With C4.5 algorithm, we do mess data processing for data classification. Then, with the results of classification, the server-push blocks push targeted advertising for users. This paper introduces the structure of the system and the detailed analysis of the classification algorithm. And the classification results obtained in this paper are compared with those by other algorithms. It is believed that this paper will be helpful in the development of targeting advertising.
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In view of the complex problem of the frame difference algorithm in the target tracking of small targets in space, considering the practical application of the camera array system in target tracking, the time when the camera array...
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In view of the complex problem of the frame difference algorithm in the target tracking of small targets in space, considering the practical application of the camera array system in target tracking, the time when the camera array system uses the target trajectory tracking function is to track the small target in space, so this paper proposes a target point position prediction trajectory tracking algorithm improves the efficiency of target trajectory tracking by predicting and tracking the centroid of the observation point. Compared with the frame difference method for target tracking, the efficiency of using this algorithm for tracking is much higher.
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摘要 :
In view of the complex problem of the frame difference algorithm in the target tracking of small targets in space, considering the practical application of the camera array system in target tracking, the time when the camera array...
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In view of the complex problem of the frame difference algorithm in the target tracking of small targets in space, considering the practical application of the camera array system in target tracking, the time when the camera array system uses the target trajectory tracking function is to track the small target in space, so this paper proposes a target point position prediction trajectory tracking algorithm improves the efficiency of target trajectory tracking by predicting and tracking the centroid of the observation point. Compared with the frame difference method for target tracking, the efficiency of using this algorithm for tracking is much higher.
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摘要 :
With the rapid development of the Internet, the application of big data is more and more extensive. Compared with traditional marketing, big data marketing has significant advantages in market information acquisition, data process...
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With the rapid development of the Internet, the application of big data is more and more extensive. Compared with traditional marketing, big data marketing has significant advantages in market information acquisition, data processing and analysis, and data application. For the automotive industry, the traditional marketing mode is facing severe challenges due to the development of big data technology. In view of this form, a new marketing mode based on big data marketing, which is called precision marketing, came into being. The new automobile precision marketing mode based on big data can be implemented from the following aspects: precise positioning based on big data analysis, accurate push based on big data mining, and fine management based on big data application.
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摘要 :
With the rapid development of the Internet, the application of big data is more and more extensive. Compared with traditional marketing, big data marketing has significant advantages in market information acquisition, data process...
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With the rapid development of the Internet, the application of big data is more and more extensive. Compared with traditional marketing, big data marketing has significant advantages in market information acquisition, data processing and analysis, and data application. For the automotive industry, the traditional marketing mode is facing severe challenges due to the development of big data technology. In view of this form, a new marketing mode based on big data marketing, which is called precision marketing, came into being. The new automobile precision marketing mode based on big data can be implemented from the following aspects: precise positioning based on big data analysis, accurate push based on big data mining, and fine management based on big data application.
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Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Multitarget regression has arisen in several interesting industrial and environmental ap...
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Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Multitarget regression has arisen in several interesting industrial and environmental application domains, such as ecological modeling and energy forecasting. However, standard multi-target regressors are not designed to perform well with such amounts of data. This paper proposes a scalable implementation for a multi-target linear regression algorithm with output dependence estimation for Big Data analytics in Apache Spark. Our experiments on large-scale datasets show an accurate analysis compared to standard implementation and order of training time reduction as the available number of working nodes in the processing cluster increases.
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摘要 :
Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Multitarget regression has arisen in several interesting industrial and environmental ap...
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Nowadays the phenomenon of Big Data is overwhelming our capacity to extract relevant knowledge through classical machine learning techniques. Multitarget regression has arisen in several interesting industrial and environmental application domains, such as ecological modeling and energy forecasting. However, standard multi-target regressors are not designed to perform well with such amounts of data. This paper proposes a scalable implementation for a multi-target linear regression algorithm with output dependence estimation for Big Data analytics in Apache Spark. Our experiments on large-scale datasets show an accurate analysis compared to standard implementation and order of training time reduction as the available number of working nodes in the processing cluster increases.
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In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective method based on big template is proposed. Big template is a set of small templates which contains a great amount of...
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In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective method based on big template is proposed. Big template is a set of small templates which contains a great amount of information of surface features. Its information content cannot be matched by any small template and it has advantages in conquering noise interference or incompleteness and avoiding erroneous judgments. Firstly, digital surface model (DSM) was utilized to make big template, distance transformation was operated on the big template, and region of interest (ROI) was extracted by the way of template matching between the big template and contour of real-time image. Secondly, corners were detected from the big template, response function was defined by utilizing gradients and phases of corners and their neighborhoods, a kind of similarity measure was designed based on the response function and overlap ratio, then the template and real-time image were matched accurately. Finally, a large number of image data was used to test the performance of the algorithm, and optimal parameters selection criterion was designed. Test results indicate that the target matching ratio of the algorithm can reach 95%, it has effectively solved the problem of building recognition under the conditions of noise disturbance, incompleteness or the target is not in view.
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