This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offeringaninnovativealternativetotraditionalmetamodel-basedsimulations. We unde...
Construction teams are responsible for building complex projects within the allotted budget, time and expected quality. Thus, team members must be able to work with each other to avoid any complications during the con...
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This research evaluates the effectiveness of Mean-Variance (MV) and Safety-First (SF) portfolio selection strategies for high-volume/high-value traded stocks on the Philippine Stock Exchange (PSE). Analyzing data from...
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The potential for magnesium storage batteries to become the next generation of rechargeable batteries is high. This is largely due to the abundance of magnesium metal resources and their high safety and electric capac...
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The Fourth industrial Revolution and its disruptive technologies are emerging massively. With various motivations for its implementation such as elevation of speed, reducing costs, mitigating errors, and other differe...
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Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrit...
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In this paper, we propose a general distributionally robust framework for performative optimization, where the selected decision can influence the probabilistic distribution of uncertain parameters. Our framework faci...
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Risk profile has been utilized in a variety of knowledge fields beyond disaster management or risk management it also plays as the tools in assessment process before the planning both in operation and *** public healt...
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This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. ...
ISBN:
(纸本)9798350369663
This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.
Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (R...
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