This study proposes a maintaining a quadrotor’s attitude system using a ‘moment disturbance observer’ aimed at enhancing stability. In recent years, quadrotors have become increasingly prevalent in sectors such as ...
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Fatigue is a prevalent issue that disrupts the overall well-being of individuals, leading to impaired cognitive functions such as learning, thinking, reasoning, remembering, and problem-solving. Chronic fatigue signif...
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The world wide web consists of web pages and databases from which web pages can be generated on demand. It is presumed that the information stored in the databases is of better quality than the one published onto alre...
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Kubernetes, an open-source platform for automating deployment, scaling, and management of containerized applications, has become a cornerstone in modern IT infrastructure. Alongside its widespread adoption, Kubernetes...
Kubernetes, an open-source platform for automating deployment, scaling, and management of containerized applications, has become a cornerstone in modern IT infrastructure. Alongside its widespread adoption, Kubernetes faces a series of sophisticated security challenges, especially in managing numerous containers. This research uniquely focuses on multi-class threat detection, classifying various types of security threats within Kubernetes environments. Machine learning is emerging as a powerful tool in cybersecurity, offering new ways to detect and mitigate threats. However, there is a shortage of comprehensive research on its application within Kubernetes, especially for detecting multiple types of security threats. This research aims to bridge this gap by introducing a machine learning-based technique for improved threat detection in Kubernetes environments. We propose an advanced detection method using the Naive Bayes algorithm, complemented by comprehensive feature engineering and dimensionality reduction using neural networks. The most effective model, which combines Principal Component Analysis (PCA) and Autoencoder with the Naive Bayes classifier, achieved an F1 Score of 0.95 and an accuracy of 91%.
The rapid growth of urban transportation necessitates innovative strategies for efficient parking management. Fog-based smart parking systems help reduce latency and improve resource use, but challenges remain in achi...
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The data path and memory elements are integral hardware components to evaluate the performance of a complex ASIC low power architecture. In this study, a FinFET model was evaluated via 7 nm FinFET process using ASAP P...
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Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physica...
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Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce ...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms’ trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers’ monetary incentives, profit, and the platforms’ trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms’ monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms’ reputation. Simulation results demonstrate TOPMG’s operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies.
Shock acceleration has been receiving attention to assess the impact resistance of products. Evaluation of the shock characteristics can be seen ranging from small size consumer electronics to large military equipment...
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Numerous environmental elements, including sun irradiation, temperature, shade, wind speed, and others, can significantly influence the output of photovoltaic (PV) systems. Solar generation forecasts across various ti...
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