The technology of virtual reality is a very new teaching technique. Virtual reality in the classroom can motivate students to learn. It follows the trend of faster, more engaging, and higher motivation. This research ...
详细信息
The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily...
详细信息
This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by ana...
详细信息
By analyzing the common problems in the teaching implementation of modern teaching system based on the background of network platform, combining the theory of deep learning theory and personalized learning theory, usi...
详细信息
The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how pa...
详细信息
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a pr...
详细信息
ISBN:
(纸本)9798331541378
The escalating impacts of climate change and the increasing demand for sustainable development and natural resource management necessitate innovative technological solutions. Quantum computing (QC) has emerged as a promising tool with the potential to revolutionize these critical areas. This review explores the application of quantum machine learning and optimization techniques for climate change prediction and enhancing sustainable development. Traditional computational methods often fall short in handling the scale and complexity of climate models and natural resource management. Quantum advancements, however, offer significant improvements in computational efficiency and problem -solving capabilities. By synthesizing the latest research and developments, this paper highlights how QC and quantum machine learning can optimize multi infrastructure systems towards climate neutrality. The paper also evaluates the performance of current quantum algorithms and hardware in practical applications and presents realistic cases, i.e., waste-to -energy in anaerobic digestion, disaster prevention in flooding prediction, and new material development for carbon capture. The integration of these quantum technologies promises to drive significant advancements in achieving climate resilience and sustainable development.
In order to enhance the practical innovation ability of new engineering students, this paper, based on the educational policy of innovation driven development, combines the current situation of artificial intelligence...
详细信息
This paper aims to revolutionize weather forecasting by exploring the potential of machine learning algorithms to achieve more accurate predictions. The focus is to leverage historical patterns and real-time meteorolo...
详细信息
Under specific environmental conditions such as dense fog or high dust levels, conventional RGB imaging technology faces significant challenges in capturing clear images. In contrast, infrared imaging technology, due ...
详细信息
ISBN:
(纸本)9798350391961;9798350391954
Under specific environmental conditions such as dense fog or high dust levels, conventional RGB imaging technology faces significant challenges in capturing clear images. In contrast, infrared imaging technology, due to its unique characteristics, can effectively acquire images under these adverse conditions. However, the high cost associated with improving image quality through hardware enhancements in infrared imaging makes software-based image quality improvement crucial. Recent studies have demonstrated that deep learning networks hold significant potential for enhancing the quality of super-resolution images. To address the issues of gradient vanishing, insufficient feature utilization, and feature redundancy present in deep learning networks, this paper proposes a dual-channel hybrid convolutional residual network based on CNN with super-resolution of infrared images, which combines dual-feature extraction and dense linking. The network employs channel splitting to effectively reduce feature redundancy and leverages residual and mixed convolution techniques to enhance feature extraction and utilization. This approach efficiently preserves image details while eliminating noise. Comparative analysis using qualitative and quantitative metrics demonstrates the effectiveness of the proposed network for infrared image super-resolution tasks. The effectiveness of the network proposed in this paper in the task of super-resolution of infrared images is demonstrated by comparing it with other methods in terms of qualitative and quantitative metrics.
This paper investigates the phenomenon of catastrophic forgetting in continuous learning, focusing on crack detection problems. We propose a specific configuration of the neural network involving selective layer freez...
详细信息
暂无评论