Although evidence theory has been applied in sensor data fusion, it will have unreasonable results when handling highly conflicting sensor reports. To address the issue, an improved fusing method with evidence distance and belief entropy is proposed. Generally, the goal is to obtain the appropriate weights assigning to different reports. Specifically, the distribution difference between two sensor reports is measured by belief entropy. The diversity degree is presented by the combination of evidence distance and the distribution difference. Then, the weight of each sensor report is determined based on the proposed diversity degree. Finally, we can use Dempster combination rule to make the decision. A real application in fault diagnosis and an example show the efficiency of the proposed method. Compared with the existing methods, the method not only has a better performance of convergence, but also less uncertainty.
To cope with the facility location problem, a method based on simulated annealing and "ZKW" algorithm is proposed in this article. The method is applied to some real cases, which aims to deploy video content server at appropriate nodes in an undirected graph to satisfy the requirements of the consumption nodes with the least cost. Simulated annealing can easily find the optimum with less reliance on the initial solution. "ZKW" algorithm can find the shortest path and calculate the least cost from the server node to consumption node quickly. The results of three kinds of cases illustrate the efficiency of our method, which can obtain the optimum within 90 s. A comparison with Dijkstra and Floyd algorithms shows that, by using "ZKW" algorithm, the method can have large iteration with limited time. Therefore, the proposed method is able to solve this video content server location problem.
Gas injection and
water injection are common and effective methods
to improve oil recovery. To ensure its production effect, it is necessary
to simulate the oilfield production process. However, traditional
composition simulation runs a large number of calculations and takes
a long time. Through the analysis of relevant data, we found that
production is affected by many factors and has a strong sequential
character. Therefore, this paper proposes a deep learning model for
reservoir production prediction based on stacked long short-term memory
network (LSTM). It is applied to other well patterns with a short
production time and a few samples in the same oilfield block by transfer
learning. The model achieves an effective combination with the actual
reservoir production process. At the same time, it uses the knowledge
learned from the well pattern with sufficient historical data to assist
in the establishment of the model of the well pattern with limited
data. This can obtain accurate prediction results and save the model
training time, thus getting more effective application effects than
composition simulation. This paper verifies the effectiveness of the
proposed method through the data and multiple different injection
combinations of the Tarim oilfield.
Anthropometric dimensions can be acquired in 2D images by landmarks. Body shape variance causes low accuracy and bad robustness of landmarks extracted, and it is difficult to determine the position of axis division point when dimensions are calculated by the ellipse model. In this paper, landmarks are extracted from images by convolutional neural network instead of the gradient of body outline. A general multi-ellipse model is proposed, the anthropometric dimensions are obtained from the length of different elliptical segments and the position of axis division point is determined by thickness–width ratio of body parts. Finally, an evaluation is completed based on 87 subjects, in which it turns out that the average accuracy of our method for identifying landmarks is 96.6%, when the number of rotation angles is 2, the three main dimensional errors calculated by our model are smaller than existing method, and the errors of other dimensions are also within the margin of error for garment measuring.
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