Car production volumes have increased dramatically over the past decade, reaching 92 million vehicles produced by 2019. This increase has particularly boosted the used car market, emerging as a growing industry thanks...
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We introduce a safety-aware adaptive reinforcement learning (RL) approach for autonomous robots operating in dynamic environments, with a focus on assistive-care applications. To that end, we combine online planning, ...
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The maj ority of people in the world presently suffer from mental illness, and many of them are not even aware of it. Mental health awareness is important, but fear and ignorance often prevent people from talking abou...
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In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being dep...
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In this paper, a Halocode-based Bluetooth gamepad is developed for use as a physical computing device for students. Hardware for the gamepad includes a power and signal managing circuit and a 3D printed case. Software...
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ISBN:
(数字)9781665498210
ISBN:
(纸本)9781665498210
In this paper, a Halocode-based Bluetooth gamepad is developed for use as a physical computing device for students. Hardware for the gamepad includes a power and signal managing circuit and a 3D printed case. Software made to communicate with the gamepad comprises of a device package for the mBlock programming software and a responsive gamepad driver. Experiments reveal the mean data rate to be 7.65Hz.
With the integration of renewable energy sources and new power electronic devices, power grid complexity has increased, leading to frequent power quality disturbances. Existing classification models often require retr...
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Among clustering algorithms, fuzzy clustering stands out for its ability to offer a nuanced representation of the data by assigning degrees of membership to clusters, providing a more flexible and adaptive approach th...
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ISBN:
(纸本)9798331541378
Among clustering algorithms, fuzzy clustering stands out for its ability to offer a nuanced representation of the data by assigning degrees of membership to clusters, providing a more flexible and adaptive approach than the rigid partitioning of hard clustering algorithms. This has proved highly advantageous, particularly for image segmentation problems. Numerous approaches have been proposed to improve the Fuzzy C -means (FCM) algorithm using quantum computing, some are quantum inspired and others can be run on quantum simulators. In this paper, a study was conducted on Quantum Fuzzy Means (QFCM) approaches. Then, a novel QFCM algorithm is introduced to address the challenges associated with these current algorithms, particularly in handling large datasets and incorporating genuine fuzzy system principles. Using concepts from quantum computing, our approach aims to improve distance calculations between data points by using a quantum distance measure. This method enables significant acceleration of the clustering process especially when dealing with extensive datasets. Moreover, our proposed algorithm integrates a structured fuzzy system framework into the membership matrix calculation, enhancing the precision and interpretability of the clustering results. Furthermore, unlike other FCM algorithms, which often lack explicit representation of fuzzy logic principles, our approach incorporates a well-defined fuzzy system to capture the inherent uncertainty and ambiguity in real -world data.
Compared to conventional methods of collaborative filtering for recommendations, algorithms that employ matrix factorization are adept at tackling the challenge of sparse data and enhancing the performance of recommen...
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In recent years, educational-support-robots have attracted considerable attention. In conventional collaborative learning with robots, the number of problems to be answered by the learner (the number of solved problem...
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The importance of deep learning is growing with artificial intelligence. It operates quickly. Entangled nerve fibres (CNNs), a well-known deep learning method, have shown impressive results in painting, classification...
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