User identification is explored through EEG-based biometric analysis using different auditory stimuli, using the distinctiveness and security of EEG signals for reliable biometric authentication. The impact on system ...
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Deep learning was a machine learning method based on artificial neural networks that enabled the learning and abstraction of features through multi-level non-linear models, thus facilitating the resolution of complex ...
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Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed,...
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ISBN:
(纸本)9798350386783;9798350386776
Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. This paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. This method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.
Forging is a traditional and important manufacturing technology to produce various high strength products and is widely used in engineering fields such as automotive, aerospace and heavy industry. To produce highly ac...
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Forging is a traditional and important manufacturing technology to produce various high strength products and is widely used in engineering fields such as automotive, aerospace and heavy industry. To produce highly accurate product, underfill that the material is not filled into the cavity should strongly avoided. For material saving and near-net product, flash should be minimized. To make the tool life long, it is preferable to produce product with low forging load. It is also preferable to uniformly deform the billet as much as possible for high strength product. Crack is a crucial defect and should strongly be avoided. Therefore, many requirements are taken into account in order to produce the forged product. To meet the requirements, design optimization in forging coupled with computer aided engineering (CAE) is an effective approach. This paper systematically reviews the related papers from the design optimization point of view. For the billet or die shape optimization, the papers are classified into four approaches. The process parameters optimization such as the billet temperature, the die temperature, the stroke length and the friction coefficient is conducted, and the related papers are also classified into four categories. The design variables and the objective function(s) used in the papers are clarified with the design optimization technique. The multi-stage forging including the hammer forging for producing complex product shape is also briefly reviewed. Finally, major performance indexes and the future outlook are summarized for the further development of design optimization in forging.
In this study, we propose a multi-task learning framework using a modified VoVNet-OSA Block Enhanced UNet, named VovUnet_Var, for image segmentation and classification on the Med++ MNIST dataset. VovUnet_Var features ...
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This paper tackles the challenges inherent in existing multi-View Stereo (MVS) methods, which often struggle with scenes that have repetitive textures and complex scenarios, leading to reconstructions that lack qualit...
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The most commonly used method for denoising SAR images involves applying kernel filters across the image, with filter weights typically calculated based on pixel differences within the filter kernel across multiple di...
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We propose a novel parallel computing that allows processors to access data in predictable time without the need to access it from different locations in memory using addresses. It uses orbital data that is mapped to ...
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ISBN:
(纸本)9783031762727;9783031762734
We propose a novel parallel computing that allows processors to access data in predictable time without the need to access it from different locations in memory using addresses. It uses orbital data that is mapped to time and is made available to multiple processors at the same time in multiple different orbits and at a specific predictable time in each orbit. This allows processors in different orbits to share the same data, eliminating the problem of sharing data at the same time among multiple processors. It provides processors with the ability to hide the waiting time when accessing shared data by overlapping it with useful work on another data while allowing other processors to work on the shared data in another orbit. The performance of this novel method shows significant improvements in scalability compared to that of conventional parallel computing.
Image-to-image translation has become a crucial research area, aiming to enable one image to absorb the style of another while retaining its own content. In this research the CycleGAN has made significant strides but ...
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Many real-world continuous control problems inherently involve multiple conflicting objectives, especially in the case of robotic control problems. In recent years, with the development of deep multi-objective reinfor...
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