About ACEThe 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. |
Aims & scope of ACE are: ·Computing ·Machine Learning ·Electrical Engineering & Signal Processing ·Applied Physics & Mechanical Engineering ·Chemical & Environmental Engineering ·Materials Science and Engineering |
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A one-time Article Processing Charge (APC) of 450 USD (US Dollars) applies to papers accepted after peer review. excluding taxes.
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This is an open access journal which means that all content is freely available without charge to the user or his/her institution. (CC BY 4.0 license).
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Our blind and multi-reviewer process ensures that all articles are rigorously evaluated based on their intellectual merit and contribution to the field.
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Latest articles View all articles
Born from the machine learning (ML) subfield of neural networks (NN), deep learning (DL) has many advantages over other ML algorithms and has become more significant today. As one of the most essential model architectures of DL, the convolutional neural network (CNN) has attracted the attention of many researchers, especially in recent years. Meanwhile, sentiment analysis has become more renowned since the rapid development of various online platforms like blogs, social networks, etc. To study these two heated topics together, this article selects a particular CNN model designed for sentiment analysis and explores its width’s potential influence on the result. During the experiment, four CNN models are created based on the same structure but with increasing width. By forwarding the pre-processed datasets to the four models and comparing their performances from different perspectives using different metrics, it’s concluded that the more expansive the model's width, the better it performs in the training, validation, and testing sections.
With the progress of science and technology, more and more cars have the function of automatic driving. After reviewing a series of articles, it is found that there are still many problems in the research on automatic driving. These include problems with sensors, cameras, navigation systems, signals and so on. The primary focus of this article is the automobile's sensor system, which includes body-sensing sensors, radar sensors, vision sensors, and GPS systems. However, there will be numerous issues if these sensors are used alone. It can be seen that many researchers have done the application of multiple sensors and achieved good results. Thus, in order to enable the widespread adoption of automated driving in the future, it is advised to integrate two or three sensors and conduct testing through real-world applications. This can not only reduce the occurrence of accidents but also promote the broader development of autonomous driving.
This paper compares three Simultaneous Localization and Mapping (SLAM) algorithms. SLAM algorithms are the core technology for autonomous navigation and environmental perception of mobile robots. SLAM algorithms are used by mobile robots to perceive the surrounding environment, build up an environment map and position themselves in real-time in an unknown environment. This article first systematically reviews the basic principles of each algorithm based on experiments and studies that have been completed by previous researchers and illustrates their respective unique mechanisms for processing sensor data, map construction, and localization. Subsequently, this paper analyzes the performance differences and characteristics of the three algorithms in practical applications in terms of robustness in complex environments, consumption of computing resources, and accuracy for generated maps. Finally, based on the advantages and disadvantages of each analyzed algorithm, this article summarizes the most suitable and unsuitable usage scenarios of different algorithms in specific situations. Moreover, this article puts forward specific algorithm selection suggestions for different scenarios to help engineers and researchers make more appropriate decisions in actual projects.
This paper examines the perception systems used in industrial Automated Guided Vehicles (AGVs), focusing on traditional and advanced sensor solutions. Traditional perception methods, such as track-based and magnetic tape guidance, offer reliability but are limited in flexibility. In contrast, radar, vision, and LiDAR sensors provide enhanced perception capabilities, enabling AGVs to navigate safely and efficiently in complex industrial environments. The study explores various sensors, including visible light, infrared, ultrasonic, LiDAR, magnetic strip sensors, Inertial Measurement Units (IMUs), tactile sensors, Ultrawideband (UWB) sensors, thermal sensors, and millimeter-wave (mmWave) sensors, highlighting their principles, advantages, and limitations. The integration of these sensors supports robust navigation and operational efficiency in diverse settings. The methodology involves reviewing existing literature and analyzing current technologies used in industrial AGVs. Results indicate that while traditional solutions are reliable, advanced sensor technologies significantly enhance AGV performance. The paper concludes that the future of AGV perception systems lies in the integration of advanced sensors with artificial intelligence and machine learning algorithms, promoting intelligent and adaptive industrial automation. Additionally, it underscores the necessity of developing robust sensor fusion techniques to harness the full potential of these advanced sensors.
Volumes View all volumes
Volume 94September 2024
Find articlesProceedings of Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning - CONFMLA 2024
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
ISBN: 978-1-83558-633-4(Print)/978-1-83558-634-1(Online)
Editor: Mustafa ISTANBULLU
Volume 93September 2024
Find articlesProceedings of Machine Learning assisted Automation Sensing System - CONFMLA 2024
Conference website: https://2024.confmla.org/
Conference date: 21 November 2024
ISBN: 978-1-83558-627-3(Print)/978-1-83558-628-0(Online)
Editor: Mustafa ISTANBULLU
Volume 92September 2024
Find articlesProceedings of the 6th International Conference on Computing and Data Science
Conference website: https://2024.confcds.org/
Conference date: 12 September 2024
ISBN: 978-1-83558-595-5(Print)/978-1-83558-596-2(Online)
Editor: Roman Bauer, Alan Wang
Volume 91September 2024
Find articlesProceedings of the 2nd International Conference on Functional Materials and Civil Engineering
Conference website: https://www.conffmce.org/
Conference date: 23 August 2024
ISBN: 978-1-83558-619-8(Print)/978-1-83558-620-4(Online)
Editor: Ömer Burak İSTANBULLU
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Applied and Computational Engineering
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