Reliability engineering implemented early in the development process has a significant impact on improving software *** can assist in the design of architecture and guide later testing,which is beyond the scope of tra...
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Reliability engineering implemented early in the development process has a significant impact on improving software *** can assist in the design of architecture and guide later testing,which is beyond the scope of traditional reliability analysis *** reliability models work for this,but most of them remain tested in only simulation case studies due to lack of actual *** we use software metrics for reliability modeling which are collected from source codes of post *** the proposed strategy,redundant metric elements are filtered out and the rest are aggregated to represent the module *** further propose a framework to automatically apply the module value and calculate overall reliability by introducing formal *** experimental results from an actual project show that reliability analysis at the design and development stage can be close to the validity of analysis at the test stage through reasonable application of metric *** study also demonstrates that the proposed methods have good applicability.
Recently, the number of Alzheimer’s disease patients has increased, and the disease seriously affects their daily lives. Hence, more and more researchers have paid attention to this disease, and the diagnostic techno...
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Fake news and its detection are now a critical issue in the world that needs to be solved as its propagation has many consequences. Because of social media's low cost and simple accessibility, as well as the quick...
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The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the opera...
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Deep learning techniques have the potential to significantly improve target detection speed and detection accuracy in autonomous driving. Most existing target detection algorithms have poor real-time performance and p...
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ISBN:
(纸本)9798350361643
Deep learning techniques have the potential to significantly improve target detection speed and detection accuracy in autonomous driving. Most existing target detection algorithms have poor real-time performance and poor accuracy. Moreover, the algorithm is difficult to be deployed. Based on analysis of the existing problems of target detection in autonomous driving, this paper puts forward an improved YOLOv5 algorithm. First, the extract feature network of YOLOv5 is replaced with the FasterNet-T0 model to reduce model parameters. Next, in the fusion part (Neck) of the network, the attention mechanism CBAM is introduced to improve the accuracy of the target detection. Then, the Slim-Neck framework is presented to improve the computational efficiency of the model. Finally, considering that CIoU may be replaced by Inner-IoU to improve the accuracy and generalization of the model, in order to verify the effect of the improved model, this paper uses the improved YOLOv5 algorithm model to extract the specific categories from VOC2007 dataset and DOTA dataset. After experimentation. The results show that the model we made, the improved YOLOv5 algorithm model is capable of getting a 0.848 and 0.748mAP @ 0.5 of the VOC2007 dataset and DOTA dataset for the specific category extraction, which verifies its effectiveness. Compared with other algorithms, the improved YOLOv5s algorithm is more competitive. On the VOC2007 data set, mAP live/voc, 0.5 increased by 5.1% for a specific category acquisition, and mAP live/voc, 0.5:0.95 increased by 6.4% relative to the original YOLOv5s algorithm, and the number of parameters reduced by 39.2% from 7035811 to 4276490. On the DOTA dataset, mAP@0.5 increased by 0.9% for a specific category acquisition, and mAP@ 0.5:0.95 increased by 0.4%, which demonstrates that this method improves the accuracy and efficiency of vehicle detection to some extent. In this study, DOTA dataset was used as an experimental dataset to verify the performance of YOL
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label ...
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Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Con...
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Data in the real world is often not static but generated and processed in streams, such as real-time adjustment of device setting parameters and real-time GPS positioning data. Feature streams means the number of samp...
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This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuni...
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The Transformer-based architecture achieves state-of-the-art results in image captioning. Due to its non-recurrent nature, additional positional information needs to be provided. However, existing advanced methods att...
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