To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed i...
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Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before...
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With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse feature...
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ISBN:
(纸本)9781450396899
With the development of artificial intelligence, pulse diagnosis has been standardized and objectified. However, there is a lack of research on the extraction and dimensionality reduction of hypertensive pulse features. We propose two effective features for distinguishing pulses of disease samples and a fusion dimensionality reduction method that combines linear and nonlinear dimensionality reduction. The results show that the proposed features and dimensionality reduction method make the classification accuracy of hypertension pulse feature reach 94.23%, and the training time of the classifier is reduced by 47 seconds, which improves the performance in terms of both accuracy and time.
A robust feature extraction method based on feature transfer for heterogeneous remote sensing images is proposed to address the problem of insufficient generalization ability of traditional edge feature extraction met...
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Time series is the main form of sensor data. Time series prediction is conducive to reduce the energy consumption of sensor nodes and increase the service life. As time cost and computation cost of the existing hybrid...
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Visual and inertial navigation have obvious complementarity in navigation accuracy, and the combined navigation of the two has excellent anti-interference ability. In this paper, a visual-inertial-based satellite qual...
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The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and cl...
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In recent years, semantic segmentation methods based on deep learning have made remarkable developments. Despite achieving high segmentation accuracy, the performance of real-time segmentation methods cannot satisfy r...
In recent years, semantic segmentation methods based on deep learning have made remarkable developments. Despite achieving high segmentation accuracy, the performance of real-time segmentation methods cannot satisfy real-world applications. In order to achieve a balance between segmentation accuracy and speed, a real-time semantic segmentation algorithm based on Tversky loss function and mixed pooling is proposed in this paper. A Short-Term Dense Concatenate network (STDC network) is used to construct the encoder, and a mixed pooling module is used for the final part of the encoder, which uses strip pooling and average pooling to enhance the feature representation while expanding the receptive field. Additionally, a Tversky-based loss function is used for the detail guidance module of the backbone network in the encoder, and a joint loss function is used to supervise the whole network's training. We achieved 76.9% mIoU at 110.7 FPS on the Cityscapes dataset, a 2.4% improvement in accuracy over the benchmark algorithm STDCSeg, and 72.2% mIoU at 177.6 FPS on the Camvid dataset, satisfied the requirements of the real-time segmentation task.
As a major form of data, the collection and analysis of time series have been widely used in many fields. In practice, wireless sensor network is a popular mechanism for data collection and it is suitable for time ser...
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It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However,...
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