In this paper, attention is paid to audio containers and the possibilities of using them as carriers of steganographic information. One of the most proven methods for increasing the reliability of stegoalgorithms is d...
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To receive synchronous observations data, that transmitted by the XCTD automatic delivery acquisition device, and to realize intensive and gridded ocean observation, A multi-XCTDs measurement information receiving and...
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Deep neural networks are usually trained on a closed set of classes, which makes them distrustful when handling previously-unseen out-of-domain (OOD) objects. In safetycritical applications such as perception for auto...
ISBN:
(纸本)9798350307443
Deep neural networks are usually trained on a closed set of classes, which makes them distrustful when handling previously-unseen out-of-domain (OOD) objects. In safetycritical applications such as perception for automated driving, detecting and localizing OOD objects is crucial, especially if they are positioned in the driving path. In the context of this contribution, OOD objects refer to objects that were not represented in the training dataset. We propose a Dirichlet deep neural network for instance segmentation with inherent uncertainty modeling based on Dirichlet distributions and the Intermediate Layer Variational Inference (ILVI). A thorough analysis shows that our method delivers reliable uncertainty estimates to its predictions whilst identifying OOD instances. The model-agnostic approach can be applied to different instance segmentation models as demonstrated for two different state-of-the-art deep neural networks. Superior results can be shown on the out-of-domain Lost and Found dataset compared to state-of-the-art approaches, whilst also achieving improvements on the in-domain Cityscapes dataset.
The article offers an economical and mathematical modeling toolkit to determine the enterprise's knowledge and project management maturity level. In particular, gray relational analysis and the hierarchy analysis ...
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Travel time estimation is an integral component of emergency medical services (EMS) simulations due to the need to calculate ambulance transport times for patients. We present a study where we integrated a machine lea...
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As the scale of the power system continues to expand, the quality of the power source is crucial to the stable operation of the electric network, making the automation of feeders an inevitable trend in power system de...
This paper explores several ways to drive a music-oriented computer system by push-button controls, with a particular focus on music education for young children and individuals with disabilities. The research investi...
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The training of 3D modeling professionals from the complexity of the software itself or the trends of the professional sector, often focuses on the mastery of a single main tool, perhaps complementing it with other mo...
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ISBN:
(纸本)9789811963469;9789811963476
The training of 3D modeling professionals from the complexity of the software itself or the trends of the professional sector, often focuses on the mastery of a single main tool, perhaps complementing it with other more specific tools for texturing or digital sculpting. Students, when entering the labor market, even if they master the techniques in a software, often need to improve or expand their professional profile by learning other tools, adapting their previous knowledge transversely, almost having to start from scratch. In addition to this, there is a certain lack of unity, from the nomenclature, information architecture, or interaction, to make equivalent operations between these softwares. The creation of didactic materials that allow the acquisition of knowledge from the transversal mapping would help to reduce or optimize this adaptation process, while reinforcing the professional profile of 3D modeling students by providing them with complementary skills.
Following the ideas put forth by industry 4.0, flexible manufacturing systems that make use of robots, sensors and artificial intelligence are gaining more and more relevance. While vision systems are fundamental for ...
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