The experiment was carried out by growing BaTiO3 (Undoped or Li-doped) on p-type Si(1 0 0) substrates using the Chemical Solution Deposition (CSD) method and spin coating at a rotational speed of 3000 rpm for 60 s, fo...
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Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and ...
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This paper presents a hybrid BRKGA (Q-HBRKGA) that combines BRKGA with Q-learning and a Local Branching technique to solve the Knapsack Problem with Forfeits(KPF). The aim is to tackle this problem, a vari-ant of the ...
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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...
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The aim of this paper is to provide an advanced WSN solution for power-sustainable energy management and efficient communication, integrating the latest developments from the SEMS. This work will focus on the research...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
The aim of this paper is to provide an advanced WSN solution for power-sustainable energy management and efficient communication, integrating the latest developments from the SEMS. This work will focus on the research effort directed at improving M2M communication and integrating WSNs with wireless mobile networks, as well as addressing PAPR, energy consumption, and scalability. It now applies to real-time monitoring of power usage, room temperature, and lighting by integrating renewable resources of energy with sophisticated algorithms and sensor networks. Key results include a 16.6% saving in energy consumptions, 78% in system delay improvements, and the implementation of solar energy systems that have realized significant cost savings while assuring environmental sustainability. This work proves the ability of the approach through wide simulations and real-world deployments for scalable and eco-friendly energy management solutions. Besides, this work is closely related to worldwide goals for reduction of carbon footprints and efficient resource utilization.
Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this conte...
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Continuous progress in memory semiconductor manufacturing technology has significantly increased capacities, densities, and operating frequencies. However, these developments have also increased the probability of mem...
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A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect ...
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Lauded as the world's most popular fruit, bananas are one of the main exports of the Philippines. It contains essential nutrients that can have a protective impact on health. Commonly, farmers can distinguish the ...
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ISBN:
(数字)9798350374186
ISBN:
(纸本)9798350374193
Lauded as the world's most popular fruit, bananas are one of the main exports of the Philippines. It contains essential nutrients that can have a protective impact on health. Commonly, farmers can distinguish the different types of bananas and potentially predict their shelf-life. Nevertheless, human judgment is not always reliable. It needs to have support and evidence to minimize the margin of error. Hence, this study focuses on developing an android application to determine the Philippine bananas' ripeness, variety, and shelf-life. This application identifies different bananas sold in the local market, specifically Cardava, Lakatan, and Latundan. Also, the application determines the shelf-life of bananas, such as the remaining days for unripe bananas to become ripe and ripe bananas to overripe. A Convolution Neural Network-based (CNN) model classifier is used as an architecture categorizing the Philippine local market bananas. The proponents used real-life image data to increase the accuracy of the CNN model. The application was proven effective and has reached 90% accuracy.
Imitation learning, in which learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods...
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Imitation learning, in which learning is performed by demonstration, has been studied and advanced for sequential decision-making tasks in which a reward function is not predefined. However, imitation learning methods still require numerous expert demonstration samples to successfully imitate an expert's behavior. To improve sample efficiency, we utilize self-supervised representation learning, which can generate vast training signals from the given data. In this study, we propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations that are robust to diverse distortions and temporally predictive, on non-image control tasks. In particular, in comparison with existing self-supervised learning methods for tabular data, we propose a different corruption method for state and action representations that is robust to diverse distortions. We theoretically and empirically observe that making an informative feature manifold with less sample complexity significantly improves the performance of imitation learning. The proposed method shows a 39% relative improvement over existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs. Moreover, we conduct comprehensive ablations and additional experiments using demonstrations with varying optimality to provide insights into a range of factors.
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