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Majority of taxi recommender systems mainly focused on satisfaction of passengers without considering fairness in assignment of taxi drivers. In this paper we propose a balanced assignment mechanism for online taxi recommendation (BAMOTR). BAMOTR provides a mechanism for fair assignment of drivers at some locations with specific routes to pick up passengers and ensures a short waiting time for passengers...
The paper that is based on the mesoscopic damage evolution of metal explores the mechanism of Kaiser effect. Unidirectional and multidirectional tensile experiments of Q345 material were conducted to reveal the relationship between the internal damage evolution of mesoscopic holes and Kaiser effect. The results show that the forcewill cause the damage corresponding to the direction of force. When...
Authentication header protocol was introduced to neighbor discovery protocol to ensure its security. A security neighbor discovery protocol model based authentication header was proposed. In this model, multicast Internet key exchange and management protocol was designed, star structure key management algorithm was presented, and IP addresses and link-layer address was bound in the AH authentication...
Dictionary learning is a challenging theme in computer vision. The basic goal is to learn a sparse representation from an overcomplete basis set. Most existing approaches employ a convex relaxation scheme to tackle this challenge due to the strong ability of convexity in computation and theoretical analysis. In this paper we propose a non-convex online approach for dictionary learning. To achieve...
Aiming at the features of the durability test of vacuum booster, this paper designed a multi-channel pneumatic loading system based on PROFIBUS-DP. The IPC (industrial personal computer) works as a master station to monitor the slave stations via roll-call polling mode. SPC200 works as a slave station to complete the task of multi-axis pneumatic servo control. At this point, the system realized the...
For the method of magnetic flux leakage testing, the evaluation of test results is based on the detected signals. The quality of evaluation is affected by the quality of the signals. The signals of magnetic flux leakage are often influenced by various factors, the signals of some small defects maybe drowned by noise. In this paper, a method is proposed based on wavelet denoising and empirical mode...
In this paper, basic principles of Hilbert-Huang transformation and the theoretical basis, based on which Hilbert-Huang transformation can be applied to analysis acoustic emission signal of fiber reinforced plastics acquired during tensile tests, are expatiated simply. Moreover, through several practical signal processing examples, it is observed that time-frequency characteristics of AE signals are...
Magnetic flux leakage testing method is a major direction of tank floor testing. In this paper, the spatial distribution of magnetic flux leakage field of tank floor corrosion defects is analyzed based on the features of magnetic flux leakage signals. BP neural network model is applied to the quantity analysis of tank floor corrosion defects. The results in network training and test reach the quantitative...
Stimulated by sensor technology, image matching techniques are greatly innovated towards multiple-image matching. In this paper, an image-space-based AMMGC multiple-image matching model is introduced. However, AMMGC model is quite complex and involves massive computing amount, especially for dense image grid point. So, two parallel computing methods are analyzed comprehensively from the point of average...
Short-term load forecasting is very important for decision making in power system operation and planning. During the last several years, kernel machines have been widely employed for short-term forecasting. Owing to the inherent limitations, the corresponding forecasting accuracy can be impaired. To overcome the limitations, this paper develops a novel kernel machine, hereafter called learnable kernel...
The ability to accurately forecast the load plays an important role in electric power system planning and operating. In this paper, a novel approach was proposed for the electricity load forecasting by applying the manifold regularization learning methodology. Unlike traditional methods for load forecasting, the prediction method based on manifold regularization allows us to effectively exploit the...
As one of the popular and advanced statistical learning algorithms, support vector machine (SVM) has been the new hot study area of pattern recognition and machine learning in recent years. SVM has such advantages as suitableness to high dimensional data, requirement of few samples and robustness to uncertainty, so it can be used to hyperspectral remote sensing image classification effectively. Based...
We present a subspace learning method, called local discriminant embedding with tensor representation (LDET), that addresses simultaneously the generalization and data representation problems in subspace learning. LDET learns multiple interrelated subspaces for obtaining a lower-dimensional embedding by incorporating both class label information and neighborhood information. By encoding each object...
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