Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

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The proceedings series Applied and Computational Engineering (ACE) is an international peer-reviewed open access series that publishes conference proceedings from various methodological and disciplinary perspectives concerning engineering and technology. The series contributes to the development of computing sectors by providing an open platform for sharing and discussion. The series publishes articles that are research-oriented and welcomes theoretical and applicational studies. Proceedings that are suitable for publication in the ACE cover domains on various perspectives of computing and engineering.

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December 21, 2022

Applied and Computational Engineering - Gender and Diversity pledge

We pledge to our series community:

  • We're committed: we put diversity and inclusion at the heart of our activities
  • We champion change: we're working to increase the percentage of women, early career ...

December 6, 2021

Applied and Computational Engineering - Disclaimer

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  • February 22, 2023, Good News! Welcome Dr. Marwan Omar from Illinois Institute of Technology to give a speech at CONF-CDS 2023!
  • February 21, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-CDS 2023!
  • January 18, 2023: Prof. Festus Adedoyin from Bournemouth University was invited to deliver a keynote speech at CONF-MSS 2023.
  • February 15, 2023: Good News! Welcome Dr. Ioannis Spanopoulos from University of South Florida to give a speech at CONF-MCEE 2023!
  • February 14, 2023, Good News! Welcome Dr. Roman Bauer from University of Surrey to give a speech at CONF-SPML 2024!
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    Latest articles

    Open Access | Article

    This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

    Open Access | Article

    With the maturity of various technologies and the vigorous development of advanced materials, the design and construction of suspension bridges require larger spans and lighter structures, but at the same time, the problems arising from the dynamic and static responses of the system under wind loads have become more and more prominent. The phenomenon of wind-induced static response occurring before the emotional response has been found in wind tunnel tests at home and abroad. Static instability is also a vital issue in the wind-resistant design of large-span suspension bridges, so studying the static behavior and instability law of large-span suspension bridges under strong winds is of high practical value for the construction of large-span bridges in the future. In this paper, relevant research results of previous researchers based on static wind stability of suspension bridges are sorted out and summarized. Their corresponding fundamental theories are introduced in detail. In addition, the related analysis program is prepared by using the APDL language of ANSYS.

    Open Access | Article

    With the widespread use of smart devices such as smartphones, facial recognition applications have experienced rapid growth. Additionally, breakthroughs in deep learning algorithms have led to the development of facial recognition technology based on deep convolutional networks, greatly improving recognition speed and accuracy. Therefore, this study proposes a real-time face recognition method based on the MTCNN-Inception-ResNet-v2-SVM model. In the facial detection phase, the MTCNN algorithm, which offers better overall performance compared to face detection algorithms such as OpenCV and Dlib, is utilized. Data augmentation and other methods are employed in the image preprocessing phase to enhance data diversity. The Inception-ResNet-v2 deep convolutional neural network is used as the backbone network for feature extraction in the facial recognition backbone network section. In the final classification stage, an SVM classifier is employed for the ultimate facial classification. Comparative analyses with models such as Inception-ResNet-v1, Inception-v3, and Inception-v4 are conducted in the backbone network section, determining that the Inception-ResNet-v2 model exhibits the best overall performance. The final result is a real-time face recognition model, MTCNN-Inception-ResNet-v2-SVM, with a high accuracy of 98.79% and fast processing speed.

    Open Access | Article

    Face recognition technology has made significant progress in recent years, driven by deep learning and other technologies, and is widely used in public safety, financial payment, intelligent access control, and other fields. Deep learning can effectively extract discriminative features from face images by constructing neural network structures and automatically learning feature mapping relationships, to improve the recognition accuracy. Deep learning shows excellent robustness when dealing with complex scenarios, and scientific metrology and visual analysis tools such as Citespace play an important role in analyzing the current status and development trend of applied research in the field of face recognition. Deep learning methods such as data enhancement techniques and generative adversarial networks have shown strong performance in face recognition tasks. In the future, the further integration and development of deep learning and face recognition technology will promote technological innovation and application expansion. Face recognition technology has important application potential in the digital society and will have wider application prospects in the future.

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