Fig 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Aiming to provide a novel paradigm of oil fields, metaverses-based parallel oil fields are proposed in this article. Compared with the existing smart/intelligent oil fields in cyber–physical systems (CPS), parallel oil fields can take human factors into full consideration and expand the operation space to cyber–physical–social systems (CPSS), which...
Contexts in source publication
Context 1
... human factors are not fully considered in CPS, which are also an essential part of actual industries. By introducing cyberphysical-social systems (CPSS) [8] to industries, Industry 5.0, proposed by Wang et al. [9], can fully consider human factors and make up for Industry 4.0's shortcomings, thus ushering in a new industrial era (as shown in Fig. 1). It is believed that Industry 5.0 will bring more technological changes and provide more opportunities. Inside industries, oil is an indispensable element and is crucial to almost everyone's daily life. Given the fact that oil is a nonrenewable energy resource, more and more researchers begin to focus on how to increase oil ...
Context 2
... Among these faults, some of the most common ones can be: standing valve leakage, traveling valve leakage, gas influence, insufficient liquid supply, plunger moving out of the barrel, tubing leakage, pump bumping, pump sticking, rod parting, and abnormal properties of well fluid [101]. Part of DCs with abnormal working conditions are listed in Fig. ...
Citations
... judge overlapping of vehicle trajectories and vehicle rectangles, which indicates vehicle collisions. We will also check the changing rate of the acceleration and orientation to ensure the vehicle dynamics constraints [68], [69], [70]. ...
Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of autonomous driving systems, driving scenario data are important for the training and testing of critical modules. However, the corner scenario cases are usually rare and necessary to be extended. Existing methods cannot handle the interpretation and reasoning of the generation process well, which reduces the reliability and usability of the generated scenarios. With the rapid development of Foundation Models, especially the large language model (LLM), we can conduct scenario generation via more powerful tools. In this article, we propose LLMScenario, a novel LLM-driven scenario generation framework, which is composed of scenario prompt engineering, LLM scenario generation, and evaluation feedback tuning. The minimum scenario description specific to LLM is given by scenario analysis and ablation studies. We also appropriately design the score functions in terms of reality and rarity to evaluate the generated scenarios. The model performance is further enhanced through chain-of-thoughts and experiences. Different LLMs are also compared with our framework. Experimental results on naturalistic datasets demonstrate the effectiveness of LLMScenario, which can provide solid support for scenario engineering in Industry 5.0.
... This approach allows for the resolution of modeling, analysis, and experimentation issues within a unified theoretical framework and has found wide application across various domains. For instance, in the field of complex engineering, it has been applied to real-time safety monitoring of visual Intelligence [20], transportation systems [21], oil fields [22], metaverse [23], and so on [24][25] [26] [27]. In agricultural engineering, it has been utilized for monitoring and managing crop cultivation, precision control of crop manufacturing, and fine control of high-value plant species [28]. ...
In the context of the rapid advancements in space technology and the increasing complexity of space missions, there is a growing need for efficient and effective approaches to tackle the multifaceted challenges faced by space systems. Traditional methods often fall short in providing comprehensive support throughout the entire lifecycle of space systems. To address these challenges, this paper presents a novel parallel space systems architecture based on ACP (Artificial systems, Computational experiments, Parallel execution) and explores its applications in the design, development, and operation of space systems. The proposed architecture integrates artificial systems with actual space systems and employs computational experiments to generate extensive sample data. This approach enhances the accuracy of the artificial systems' model and optimizes the performance of the real systems, facilitating parallel advancements between the two. The design, development, and operation process of Q-Sat, implemented using the ACP framework, serves as a case study to illustrate the advantages of parallel space systems. Following adjustments made to the discrepancies between parallel systems under the ACP-based space systems framework, the accuracy of missing orbit compensation improved by 86.5%, and the 24-hour forecast positional error was reduced by approximately 65 m. Furthermore, this paper discusses future trends, emphasizing the increasing efficiency and reliability of digitized, integrated, and adaptive space systems. The findings contribute to the understanding of parallel space systems and provide valuable insights for further advancements in the field.
... Another research work on metaverse adoption in supply chains is presented by Chen et al [16] who analysed the adoption of metaverse taking into account the possibility of increasing efficiency and performance, especially when integrating metaverse with Artificial Intelligence (AI) and blockchain technology. Wang et al [17] suggested a metaverse-based parallel oil fields framework which is meant to achieve the 6s goal that includes sustainability. The digital layer of the framework has a variety of apps such as 'energy management' and 'production optimisation & prediction'. ...
Newly introduced technologies often require time for adoption and integration into manufacturing environments, for several reasons including technological maturity, adoption costs, and skills gaps. The inclusion of sustainability as a new requirement for both customers and producers adds further complexity to the equation. As metaverse technology became available, it became logical to establish a set of requirements to harness its new potential and create a sustainability-oriented framework for seamless integration into modern smart manufacturing environments. Against this background, the current work introduces a framework aimed at harnessing the potential of the metaverse to enhance manufacturing sustainability. As a case study, an industrial workshop was analysed and evaluated using the proposed framework. The findings help create a future plan for leveraging the use of the metaverse and prioritising its requirements. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 34th CIRP Design Conference.
... By leveraging large-scale trainable parameters and training on vast amounts of text data, LLMs have shown promising ability in understanding, reasoning [19], and interaction [20]. LLMs have also brought inspiration to the foundation models for traffic control [21,22], smart factories [23][24][25], smart agriculture [26,27], management [28,29], intelligent manufacturing [30][31][32], sensing [33][34][35], and cyber-physical-social systems in metaverse [36][37][38]. ...
Intelligent systems and human-machine interactions have consistently provided convenience in both work and daily life. Artificial Intelligence Generated Content (AIGC) can assist humans in artistic creation by generating painting images based on textual descriptions. However, the quality of generated painting images depends heavily on well-designed prompts, which are labor-intensive and time-consuming in painting creation. Large Language Models (LLMs) like ChatGPT have shown impressive performance in linguistic tasks such as question answering and logical inference, demonstrating strong linguistic intelligence. This paper proposes an assistant painting creation approach to provide precise content control for painting generation by combining LLMs with text-to-image generative models and evaluates the performance of the proposed approach on painting content generation and painting element arrangement. The experimental results show that our approach can provide clear guidance on rich painting content and reasonable arrangements of painting elements, demonstrating its ability of text-based painting scene imagination. In painting generation tasks, LLMs like ChatGPT can help the text-to-image models with precise control over the painting content and improve the overall painting results.
... Wang believes that digital workers will make up about 80% of the future workforce in Industries 5.0, while robotic workers and biological workers will account for about 15% and 5%, respectively [26]. The cooperation of the three kinds of humans enables various activities in CPSS, such as art creation [27], manufacturing [24], driving [22], and management [28] while ensuring the realization of the 6S goals: safety, security, sustainability, sensitivity, service, and smartness [23]. ...
The construction of transportation 5.0 or the so-called society-centered intelligent transportation systems (ITS) has aroused higher requirements for the intelligent sensing capability to seamlessly integrate Cyber-Physical-Social Systems (CPSS). Crowd Sensing Intelligence (CSI), as a promising paradigm, leverages the collective intelligence of heterogeneous sensing resources to gather data and information from CPSS. Our first Distributed/Decentralized Hybrid Workshop on Crowd Sensing Intelligence (DHW-CSI) has been focused on principles and high-level processes of organizing and operating CSI. This letter reports the discussion results of the second DHW-CSI addressing the participants, methods, and stages of CSI for ITS. We categorized sensing participants into three kinds, i.e., biological , digital, and robotic. Then we summarized three methods to enable sensing intelligence, i.e., foundation models, scenarios engineering, and human-oriented operating systems. Finally, we anticipated that the progression of CSI will experience three stages, from algorithmic intelligence to linguistic intelligence, and eventually to imaginative intelligence.
Replacing manual inspection, automated optical inspection (AOI) equipment is widely used in printed circuit board (PCB) factories for automatic PCB defect segmentation. However, parameter refinement of AOI devices has gradually become an efficiency bottleneck in AOI usage, posing a highly challenging task. Since a large number of AOI parameters and different types of inspected objects make timely proper parameter refinement for clear images quite difficult. Considering this, we propose the concept of parallel PCB inspection in cyber–physical–social systems (CPSSs). Based on artificial systems, computational experiments, and parallel execution (ACP) theory with automatic parameter identification and refinement, we perform descriptive intelligence to build an artificial imaging system, obtain knowledge about the mapping relationships of parameter settings and imaging results, and realize automatic parameter identification given image input; conduct predictive intelligence to obtain image quality assessment results and maximize quality score for refinement strategies; and carry out prescriptive intelligence to guide parameter refinement for better imaging. This system could guide engineers proactively with constructive suggestions on parameter refinement when imaging failures occur, greatly reducing the training cost of engineers while improving work efficiency and work quality. To validate that our parallel PCB inspection could perform automatic AOI results evaluation without human participation, we evaluate it on distortion-free and different distortion images and confirm image quality score is positively associated with segmentation accuracy.
Beam pumping units (BPUs) are key equipment in oilfield production. Currently, many fault diagnosis methods for BPUs have been developed, and most of them are based on feature or image classification of indicator diagrams. However, low-quality monitoring data and the limited proportion of effective pixels in indicator diagram greatly restrict the performances of these methods. This article proposes an efficient two-step fault diagnosis method for BPUs. In the first step, to overcome the impact of low-quality monitoring data, a dynamic time warping-based matching method is proposed to extract the period of the data, and then a physical model driven method optimized by Bayesian gradient descent is proposed to reconstruct the data. In the second step, to overcome the impact of the limited proportion of effective pixels in indicator diagram, a parallel deep network is proposed which directly takes the time series of the displacement and the load of BPUs as the inputs. Extensive experiments on dataset from 45 real oil wells have shown that, the proposed method can achieve the best performance compared with the state-of-the-art methods, meanwhile the computational load is only 5% of other deep learning-based methods.
Very recently, intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers, entrepreneurs, and policymakers from various sectors around the world. However, there is no consensus on why and what is Industry 5.0 yet. In this paper, we define Industry 5.0 from its philosophical and historical origin and evolution, emphasize its new thinking on virtual-real duality and human-machine interaction, and introduce its new theory and technology based on parallel intelligence (PI), artificial societies, computational experiments, and parallel execution (the ACP method), and cyber-physical-social systems (CPSS). Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development. We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies. Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces, Secure in cyberspaces, Sustainable in ecology, Sensitive in indi-vidual privacy and rights, Service for all, and Smartness of all.
The current ChatGPT phenomenon has signaled a new era of Artificial Intelligence moving from Algorithmic Intelligence to Linguistic Intelligence where interactive activities between actual and artificial, real and virtual, human and machine play an active and important role online and in real-time. At IEEE/CAA JAS, we are interested in investigating the impact and significance of this new era on industrial development, especially control and automation for manufacturing and production.
