Context Modelling annual shoot development processes is a key step towards functional-structural modelling of trees. Various patterns of meristem activity can be distinguished in tree shoots, with active periods of ph...
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Context Modelling annual shoot development processes is a key step towards functional-structural modelling of trees. Various patterns of meristem activity can be distinguished in tree shoots, with active periods of phytomer production followed by rest periods. This approach has seldom been integrated in functional-structural tree models. Aims This paper presents theoretical research work on modelling and computation of the dynamics of tree annual shoots using stochastic processes with various development patterns: continuous or rhythmic, monocyclic or polycyclic, "seasonal" or "a-seasonal", with preformation or neoformation produced from meristem functioning. Methods The renewal theory is used to compute stochastic aspects of phytomer production, resulting from meristem extension or rest periods and meristem mortality. Results Continuous development can be modelled with a Bernoulli process, while rhythmic development is modelled by alternation between extension and rest periods, the duration of each period following specific distributions. Conclusion The application of such stochastic modelling is the estimation of organ production during tree development as a component of the demand in functional-architectural tree models, used for computing biomass production and partitioning.
With mobile agent technology, multi-agent traffic management system is one effective approach to realize the demand-based control. Traffic signal controller is the base of traffic management system. Hence, the realiza...
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Quality score (QS) plays a critical role in sponsored search auctions and in practice is closely related to the historical click-through rate (CTR) of an advertisement. Existing research, however, has not explicitly c...
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Quality score (QS) plays a critical role in sponsored search auctions and in practice is closely related to the historical click-through rate (CTR) of an advertisement. Existing research, however, has not explicitly considered this correlation between QS and historical CTR. In this paper, we strive to bridge this gap. Based on a discrete time-dependent optimal control model, which explicitly captures the CTR-QS correlation, we analyze the optimal positioning strategy and the widely-observed greedy positioning strategy for advertisers. We find that both strategies lead advertisers to monotonically increase or decrease their ranks over time, and thus may result in a polarization trend in sponsored search markets. Our findings can help characterize advertisers' behavior dynamics and also offer valuable insights and suggestions to search engines.
The main purpose of this paper is to make table tennis robots complete the hit table tennis ball action by imitating human's behavior. The main strategy is to record a video of action which people played the table...
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ISBN:
(纸本)9781457720727
The main purpose of this paper is to make table tennis robots complete the hit table tennis ball action by imitating human's behavior. The main strategy is to record a video of action which people played the table tennis, then analysis the video of the racket trajectory. The racket in the image is extracted by image processing when the each frame is captured in the video. Then three-dimensional coordinates of the center of the racket and racket posture are obtained via PnP positioning approach based on the intrinsic parameters of the camera. The table tennis robot will off-line learn to complete the imitation of basic actions of the racket trajectory and postures. A large number of experimental data is used to establish basic actions of table tennis robot.
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a no...
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ISBN:
(纸本)9781467322164
In traditional bag-of-words method, each local feature is treated evenly for representation. One disadvantage of this method is that it is not robust to noise, which makes the performance impaired. In this paper, a novel human action recognition approach which learns weights for features is proposed, where each feature is assigned a weight for human action representation. These weights are learned jointly with discriminative model. There are two advantages of our model. First, small weights are assigned to noise, which can help to reduce the effect of noise on representation of human action. Second, discriminative features, which are critical for human action recognition, are assigned large weights. Experimental results demonstrate the advantages of the proposed method.
Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have ...
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Movie recommendation is a very popular service in internet based movie related websites such as NetFlix, MovieLens. The performance of the recommendation plays the key role in the user experience. Existing works have shown that combining content based and collaborative filtering based algorithms is the best way for movie recommendation. Nevertheless, the performance of this hybrid algorithm is strongly depended on the strategy how to combine the basic pure algorithms. Existing works usually use a static combination strategy which may generate even worse performance for some users. To solve this problems, in this paper we propose a new item based hybrid algorithm that uses a dynamic user adaptive combination strategy. Besides, we also exploit the external open resources IMDB as the movie content data. Experiments on real datasets show that the dynamic user adaptive combination strategy can significantly enhance the performance of the recommendation and the external open resource IMDB is a very good information resource for recommendation.
With the acceleration of China's urbanization, more and more unexpected disasters in big cities make a severe challenge to city emergency traffic management. Under this background, we present a heuristic implement...
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ISBN:
(纸本)9781479905300;9781479905294
With the acceleration of China's urbanization, more and more unexpected disasters in big cities make a severe challenge to city emergency traffic management. Under this background, we present a heuristic implementation of urban emergency traffic evacuation in this paper. Firstly, we refer to a popular evacuation demand generation model to generate the evacuation demand. When solving the path selection problem, the heuristic search method is used. We take Dijkstra shortest path and current road condition as two parts of the evaluation function to evaluate different choices and choose the best one. To simulate the dynamic process of evacuation, we developed a position update algorithm to update the positions of traffic participants. The mathematical analysis method and computer simulation are combined to determine the final evacuation route of a traffic participant. This combination is effective since it takes advantages of both methods and avoids the shortcomings at the same time.
We introduce a model considering the single-leg revenue management problem from the perspective of online algorithms and competitive analysis. In this model, the price and limitation of bookings are both decision vari...
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The urban traffic coordination controls (UTCCs) can make full use of the mutual advantages of intersections, which makes it can improve the traffic access capacity and decrease the possibility of traffic congestion in...
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Traditional methods based on bag-of-word representation are easily affected by noise, and they also cannot handle the problem when a test distribution differs from the training distribution. In this paper, we propose ...
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ISBN:
(纸本)9781467322164
Traditional methods based on bag-of-word representation are easily affected by noise, and they also cannot handle the problem when a test distribution differs from the training distribution. In this paper, we propose a novel method for human action recognition by bagging data dependent representation. Different with traditional methods, the proposed method represents each video by several histograms. These histograms are obtained by bagging according to an estimated prior several times in both training and testing. The data dependent property of our method depends on the prior which reflects the training distribution. There are two advantages of the proposed method. First, it alleviates the distribution difference between training set and test set. Second, the bagging operation reduces noise and improves the performance significantly. Experimental results show the effectiveness of the proposed method.
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