A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Sats...
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A novel technique,named auxiliary equation method,is applied in this research work for obtaining new traveling wave solutions for two interesting proposed systems:the Kaup-Boussinesq system and generalized Hirota-Satsuma coupled KdV system with beta time fractional *** solutions were obtained using MAPLE *** technique shows a great potential to be applied in solving various nonlinear fractional differential equations arising from mathematical physics and ocean *** a standard equation has not been used as an auxiliary equation for this technique,different and novel solutions are obtained via this technique.
Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to ...
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Dry joints in prefabricated concrete construction, especially in precast segmental bridges, are a matter of controversial discussion within the professional community. While dry joints have been successfully applied i...
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In this paper, we present a single-shot X-ray speckle tracking (XST) phase-contrast imaging (PCI) method for a non-coherent polychromatic laboratory source. XST typically requires a coherent X-ray source, such as a sy...
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Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling ...
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Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.
Neural networks (NNs) are increasingly often employed in safety critical systems. It is therefore necessary to ensure that these NNs are robust against malicious interference in the form of adversarial attacks, which ...
Neural networks (NNs) are increasingly often employed in safety critical systems. It is therefore necessary to ensure that these NNs are robust against malicious interference in the form of adversarial attacks, which cause an NN to misclassify inputs. Many proposed defenses against such attacks incorporate randomness in order to make it harder for an attacker to find small input modifications that result in misclassification. Stochastic computing (SC) is a type of approximate computing based on pseudo-random bit-streams that has been successfully used to implement convolutional neural networks (CNNs). Some results have previously suggested that such stochastic CNNs (SCNNs) are partially robust against adversarial attacks. In this work, we will demonstrate that SCNNs do indeed possess inherent protection against some powerful adversarial attacks. Our results show that the white-box C&W attack is up to 16x less successful compared to an equivalent binary NN, and Boundary Attack even fails to generate adversarial inputs in many cases.
The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this chal...
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ISBN:
(数字)9798350349320
ISBN:
(纸本)9798350349337
The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this challenge. SLT is in charge of exercising system-level interactions between hardware components and software. Non-functional properties, e.g., temperature, play a major role in *** work focuses on the automatic generation of assembly test programs for SLT that aim to indirectly maximize a particular non-functional property, for example, the temperature. It is based on two-step generation with genetic algorithms. First, a fast architectural simulation is used with the genetic algorithm to provide a structure for the test programs. Afterward, an additional generation is done on the hardware to optimize the initial register contents of the *** case study for gathering experimental results is a super-scalar out-of-order RISC-V processor, the Berkeley Out-of-Order Machine (BOOM). Experimental results show that the two-step generation is more effective in converging to a better power-hungry test program than only using the power consumption as a fitness function for the genetic algorithm.
The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent log...
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The mobility of the future is not only relevant for passenger and goods traffic in large cities and on the highway. For an attractive and vital campus with intelligent infrastructure modern mobility is also an importa...
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Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improvi...
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