Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also m...
Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation.
The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production ...
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A microwave kiln, made of silicon carbide and ceramic fiber, commonly employs in a household glassware production process. In this process, when the kiln was in a microwave oven, a microwave transmitted to the kiln ge...
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Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteris...
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This study presents a novel approach to a Sensor Allocation Problem (SAP) in Wireless Sensor Networks (WSNs), a combinatorial optimization problem focused on optimizing network topology to minimize energy consumption ...
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ISBN:
(数字)9798331534318
ISBN:
(纸本)9798331534325
This study presents a novel approach to a Sensor Allocation Problem (SAP) in Wireless Sensor Networks (WSNs), a combinatorial optimization problem focused on optimizing network topology to minimize energy consumption while ensuring connectivity. We propose a hybrid methodology that combines the Biased Random-Key Genetic Algorithm (BRKGA), with reinforcement learning-based refinement incorporated into its decoder, and Local Branching techniques for efficient sensor placement. The integration of these techniques offers a scalable and adaptable solution to complex network configurations, achieving superior performance compared to traditional exact methods. Extensive computational experiments demonstrate the robustness and effectiveness of this approach across various network topologies, including regular, semi-regular networks. Our results highlight the ability of the proposed methodology to efficiently allocate sensors in large-scale, sparse, and dynamically changing networks, addressing the challenges of energy efficiency, connectivity, and coverage.
Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Rec...
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Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire m...
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Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shado...
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Supercapacitors are known for longer cycle life and faster charging rate compared to batteries. However, the energy density of supercapacitors requires improvement to expand their application space. To raise the energ...
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Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neur...
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Making inference with spatial extremal dependence models can be computationally burdensome since they involve intractable and/or censored likelihoods. Building on recent advances in likelihood-free inference with neural Bayes estimators, that is, neural networks that approximate Bayes estimators, we develop highly efficient estimators for censored peaks-over-threshold models that use augmented data to encode censoring information in the neural network input. Our new method provides a paradigm shift that challenges traditional censored likelihood-based inference methods for spatial extremal dependence models. Our simulation studies highlight significant gains in both computational and statistical efficiency, relative to competing likelihood-based approaches, when applying our novel estimators to make inference with popular extremal dependence models, such as max-stable, r-Pareto, and random scale mixture process models. We also illustrate that it is possible to train a single neural Bayes estimator for a general censoring level, precluding the need to retrain the network when the censoring level is changed. We illustrate the efficacy of our estimators by making fast inference on hundreds-of-thousands of high-dimensional spatial extremal dependence models to assess extreme particulate matter 2.5 microns or less in diameter (PM2:5) concentration over the whole of Saudi Arabia.
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