The 2011 flood in Thailand exposed significant vulnerabilities in industrial areas, highlighting the necessity for enhanced disaster risk management through Area-Business Continuity Management (Area-BCM). This prelimi...
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The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kin...
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The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.
The global agribusiness context faces at the same time challenges of feeding a growing global population that is used to safe and nutritious food, opportunities based on innovation, high technology and efficiency in t...
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Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, d...
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Multi-objective optimization techniques are practical techniques for controller tuning purposes. A wide variety of papers use such an approach or propose new algorithms to approximate the Pareto front. Nevertheless, despite the expressive volume of works dealing with it, there is no standard benchmark testbed that could be used as a baseline comparison among different techniques. This work addresses such a gap, with a first step proposing a linear-SISO testbed with guidelines and rules and providing a basic example for a PI controller. Such a benchmark is expected to promote further research on the topic.
In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specifi...
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Intelligent systems focused on traffic management have been in evidence in recent years, and applications related to vehicle detection and tracking, speed estimation, and traffic flow identification have become an int...
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ISBN:
(数字)9798350360868
ISBN:
(纸本)9798350360875
Intelligent systems focused on traffic management have been in evidence in recent years, and applications related to vehicle detection and tracking, speed estimation, and traffic flow identification have become an interesting research topic. For the previously mentioned tasks, a large number of data has to be gathered to train deep learning algorithms, but collecting that data can be a time and resource-consuming task. Therefore, the use of synthetic data has become a viable option that helps to minimize data acquisition problems, but when misused, it can negatively impact the model's quality. This paper presents a systematic literature review based on the use of synthetic images to train object detection models in urban scenarios, aiming at identifying the ideal ratio between real and synthetic images that can benefit those models and the best methods to produce synthetic images. This study identified that there is no consensus on the number of synthetic images that can help to generate a more accurate model, due to the low number of papers addressing this relationship, however, it was noted that the use of generative adversarial networks (GANs) can create synthetic images that are more similar to real images, bringing benefits for training detection models, although without identifying how the use of images generated by this method can help in the relationship between synthetic and real.
One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and t...
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The evolution of manufacturing has led to Industry 5.0, where human creativity is integrated with advanced technology to achieve efficient and sustainable production. Given the high volume and diversity of assembly ta...
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The evolution of manufacturing has led to Industry 5.0, where human creativity is integrated with advanced technology to achieve efficient and sustainable production. Given the high volume and diversity of assembly tasks, operator assistance systems have become essential tools in the industry, leveraging augmented reality (AR) and other new technologies. This literature review specifically examines 19 papers on AR applications within the context of industrial assembly tasks. The review focuses on identifying the types of AR technologies utilized, the methodologies for implementing AR systems in assembly, and the primary limitations and challenges encountered. The findings indicate a growing field with promising applications in assembly, training, and maintenance, predominantly using AR glasses. Furthermore, a gap is identified indicating the absence of a standardized framework for AR-based assembly assistance systems. Future research should aim at creating a foundational framework for AR system implementation, thereby accelerating the adoption of innovative technologies in the industry.
This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler...
This article implements the multi-objective grey wolf optimizer (MOGWO) in the tuning process of the gains of the Fractional-Order Proportional-Integral-Derivative (FOPID) controller applied in the control of a boiler system. For a sequence of 100 runs, this application had compared and analyzed with other implementation that uses MOGWO to optimize the gains of the classical Proportional-Integral-Derivative (PID) controller. In the computational simulation, the value hypervolume metric had used to analyze the performance of the controllers. In the results, the implementation of the FOPID showed superior to PID, where the comparison had validated by a hypothesis test. Despite the higher computational cost concerning the tuning process of the PID controller, this study proved that the FOPID controller can be advantageous for industrial applications.
One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and t...
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