As an advanced method of multivariate data analysis, structural equation modeling (SEM) is to obtain relationships among latent variables in structural models. One characteristic of SEM models is that the same substru...
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Onion URLs lead to the dark web, a mysterious and secretive internet space with many websites. This paper proposes a novel content-based classification of. onion URLs. Given the concerns surrounding the dark web's...
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Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks ...
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This dominant approach in contemporary science and industry-level applications is multilayer deep learning. In this article we compare convolutional neural networks (CNN) architecture with synergetic models. The CIFAR...
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The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating ...
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ISBN:
(纸本)9798350301212
The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating the orientation, an additional filter is required. Some of the newest Attitude and Heading Reference systems can provide a referenced estimation of the orientation of the device, allowing it to retrieve the orientation of a robotic system. However, magnetic field perturbations caused by ferromagnetic objects or induced magnetic fields might influence these systems and, consequently, lead to the accumulation of errors over time. In this paper, the performance of the Xsens fusion filter is compared with a stateof-the-art algorithm to estimate the orientation of the system under dynamic movements and in the presence of magnetic perturbations, with the goal of finding the most suitable for an Unmanned Aerial Vehicle. The results show that both filters are robust and perform well in the target scenario, with a root mean squared error between 2 and 5 degrees;however, the Xsens fusion filter does not require an extra computer to process the data.
Multi-modal machine translation requires a large amount of training data to improve performance. However, collecting and annotating labeled data typically requires a lot of time and human resources. For tasks such as ...
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Multi-modal machine translation requires a large amount of training data to improve performance. However, collecting and annotating labeled data typically requires a lot of time and human resources. For tasks such as natural language processing and computer vision, obtaining large-scale annotated data is a challenging problem that needs to be addressed. To address the issue of high cost in obtaining annotated data, this article proposes an effective method of integrating a pre-trained generative visual model for text-to-image generation into pure-text machine translation. To improve the modeling quality of semantic translation from text to image, we will perform conditional augmentation on the text used for image generation. The gate fusion mechanism is used to combine the generated image information with the conditionally augmented text information to assist in improving translation performance. To test the feasibility of the method, we conducted experiments on multiple different datasets. When generating text-to-image correspondence on the Multi30k English-to-German dataset, the Bleu score get a significant improvement in compared to pure-text translation. Experiments and visualization analysis show that the proposed method can effectively improve the quality of pure-text machine translation.
The following algorithms for constructing predictive models of key quality indicators of polymer film materials are considered and implemented: adaptive boosting of decision trees (AdaBoost), recurrent neural network ...
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In recent years, visual-based Brain-computer Interface (BCI) systems have gained significant attention due to their high Information Transfer Rate (ITR). In practical applications, there is a growing demand for large ...
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ISBN:
(纸本)9798400708343
In recent years, visual-based Brain-computer Interface (BCI) systems have gained significant attention due to their high Information Transfer Rate (ITR). In practical applications, there is a growing demand for large instruction set BCI systems to support more complex commands. However, users may experience fatigue-related issues during prolonged engagement in visual tasks, which negatively impacts the modeling accuracy of BCI systems. To address the issue of signal degradation caused by user subjective intentions that is difficult to detect and process, we propose a method which can monitor user attention and optimizes signal quality in offline data processing. Under the monitor of eye tracker, this method employs Exponentially Weighted Moving Average (EWMA) and Simple Moving Average (SMA) to calculate fixation points, aligns them with the target range, and filters the electroencephalogram (EEG) signals of the current trials. Offline results demonstrate that the average accuracy, after optimizing with EWMA and SMA, is 87.31% and 86.86% respectively, while the average accuracy is 80.81% for the raw signals. This paper demonstrates that monitoring user subjective intention decay can improve the accuracy of offline models and provides a new method for taking user performance into consideration in the development of BCI applications in the future.
This paper would provide a comprehensive review of the synergistic integration of measurement science and artificial intelligence (AI) in the realm of smart agriculture. As agriculture undergoes a transformative phase...
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Financial markets are considered the most chaotic and complex dynamic systems as factors responsible for the fluctuation of share prices are numerous and plentiful. Owing to the advancements in the field of machine le...
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