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Digital Triplet Paradigm Based Brain Like Intelligence for Augmenting the Resilience of Intelligent Mechatronics, Towards Mitigating the Complexity of Cognitive Computing in the Oil and Gas Industry 5.0 Context
No full textL'integrazione dell’intelligenza artificiale (AI) e della trasformazione digitale ha accelerato l’evoluzione delle architetture Digital Triplet (D3), allineandosi ai principi umanocentrici di Industria 5.0. Incorporando le funzioni cognitive umane e l'intelligenza percettiva nei domini fisici e virtuali, il paradigma del digital triplet stabilisce una sinergia adattiva tra esseri umani e macchine. Questa ricerca amplia il concetto di digital twin intelligente, superando le metodologie tradizionali basate sui dati e integrando ragionamento, modellazione predittiva e computazione cognitiva, consentendo un processo decisionale adattivo e in tempo reale nei sistemi industriali.
Questa tesi analizza sistematicamente l'evoluzione dei digital twin, introducendo un framework gerarchico del digital triplet, che integra cognizione umana, volizione e intelligenza adattiva per migliorare le interazioni cyber-fisiche. Definendo i livelli di maturità, dominazione e volizione all'interno della gerarchia del digital triplet, la ricerca dimostra la sua capacità di potenziare le capacità percettive e cognitive nello spazio cibernetico. Tre studi di caso illustrano il ruolo del digital triplet nello sviluppo di siti brownfield, retrofit intelligenti e automazione industriale resiliente, con particolare attenzione alle valvole di controllo del flusso pneumaticamente attuate nell'industria petrolifera e del gas. Il framework proposto migliora la maturità digitale e l’intelligenza automatizzata, affrontando la complessità di un modello matematico multifisico innovativo per il sistema valvola-serbatoio, utilizzando metodologie guidate dall’IA e basate sui dati, e operando entro ordini cibernetici avanzati.
Il paradigma digital triplet favorisce l’intelligenza e la resilienza del sistema, integrando soft sensing, analisi predittiva e intelligenza prescrittiva. Colmando il divario tra osservazioni sperimentali e modelli data-driven, la ricerca avanza le capacità cognitive e percettive dei sistemi meccatronici intelligenti. Questa ricerca impiega regressori random forest basati su machine learning per costruire soft sensor per le valvole di controllo del flusso, migliorando la precisione predittiva e i meccanismi di controllo adattivo. La Optimized R2 Score Matrix evidenzia elevati punteggi di predittività (R2 ≈ 1.00) per le relazioni chiave tra le caratteristiche, come la portata d'acqua della valvola solenoide che prevede il flusso interpolato e lo spostamento della valvola che prevede la pressione sul diaframma. Tuttavia, le correlazioni moderate (R2 ≈ 0.6-0.9) evidenziano la necessità di ulteriori perfezionamenti nel feature engineering e nella modellazione multivariata.
Inoltre, modelli di deep learning come multilayer perceptron (MLP), long short-term memory (LSTM), spiking neural networks (SNNs) e liquid state machines (LSMs) sono integrati con i principi dell'apprendimento hebbiano e non-hebbiano per predire il comportamento delle valvole e potenziare le capacità cognitive basate sulla memoria. Le tecniche di ottimizzazione, come la mini-batch processing e l'inizializzazione Xavier, migliorano la robustezza predittiva della pressione di attuazione e dello spostamento, con un modello MLP ottimizzato che ha raggiunto un R2 di 0.9901 per la predizione della pressione pneumatica e dello spostamento. Inoltre, un modello ibrido SNN-LSTM con sei strati ha migliorato significativamente l'accuratezza della previsione del livello del serbatoio (R2 = 0.9777), dimostrando l'efficacia della combinazione tra dinamiche temporali basate sui picchi neuronali e architetture di memoria a lungo termine. Il modello MLP a tre strati con un livello di codifica Leaky Integrate-and-Fire (LIF) ha mostrato miglioramenti moderati nella predizione dello spostamento (R2 = 0.9425) e della pressione pneumatica (R2 = 0.8841), validando il ruolo del calcolo bio-ispirato nelle applicazioni di IA industriale.
Questa tesi evidenzia il potenziale trasformativo delle architetture digital triplet, unificando intelligenza artificiale, computazione cognitiva, apprendimento hebbiano e non-hebbiano, e principi neuromorfici. Sottolinea il ruolo strategico dei modelli digitali gerarchici, dei meccanismi di apprendimento ispirati al cervello e dell'analisi avanzata nel raggiungimento di capacità resilienti, percettive ed euristiche per applicazioni critiche nell’automazione industriale e nella meccatronica. Sfruttando IA bio-ispirata e intelligenza adattiva, questa ricerca pone le basi per la prossima generazione di digital twin cognitivi, in grado di auto-ottimizzarsi, prevedere e rispondere autonomamente, avanzando così il paradigma dell'automazione cognitiva nell'Industria 5.0 e oltre.The integration of artificial intelligence (AI) and digital transformation has accelerated the evolution of digital triplet (D3) architectures, aligning with the human-centric imperatives of Industry 5.0. By embedding human cognitive functions and perceptual intelligence into both physical and virtual domains, the digital triplet paradigm establishes an adaptive synergy between humans and machines. This research advances intelligent digital twins beyond traditional data-driven methodologies by incorporating reasoning, predictive modeling, and cognitive computing, enabling real-time adaptive decision-making in industrial systems.
This thesis systematically explores the evolution of digital twins, introducing a hierarchical digital triplet framework that integrates human cognition, volition, and adaptive intelligence to enhance cyber-physical interactions. By defining maturity, domination, and volition levels within the digital triplet hierarchy, this research demonstrates its capability to enhance perceptual and cognitive capacities in cyberspace. Three case studies illustrate its role in brownfield development, intelligent retrofitting, and resilient smart industrial automation, with a primary focus on pneumatically actuated flow control valves in the oil and gas industry. The proposed framework enhances digital maturity and automation intelligence, addressing the complexity of novel multiphysics mathematical model in valve-tank system through AI-driven, data-driven methodologies and within increased cybernetic orders.
The digital triplet paradigm fosters system intelligence and resilience by integrating soft sensing, predictive analytics, and prescriptive intelligence. By bridging the gap between experimental observations and data-driven models, it advances the cognitive and perceptual capabilities of intelligent mechatronic systems. This research employs machine learning-based random forest regressors to construct soft sensors for flow control valves, improving their predictive accuracy and adaptive control mechanisms. The Optimized R2 Score Matrix reveals high predictability scores (R2 ≈ 1.00) for critical feature relationships, such as solenoid valve water flow predicting interpolated flow rate and valve displacement predicting diaphragm pressure, while moderate predictability (R2 ≈ 0.6-0.9) highlights areas for further feature engineering and multivariate modeling.
Furthermore, deep learning models including multilayer perceptrons (MLP), long short-term memory (LSTM), spiking neural networks (SNNs), and liquid state machines (LSMs) are integrated with Hebbian and non-Hebbian learning principles to predict valve behaviors and enable memory-augmented cognitive capacities. Optimization techniques such as mini-batch processing and Xavier initialization enhance predictive robustness for actuation pressure and displacement, with the optimized MLP model achieving an R2 of 0.9901 for pneumatic pressure and displacement prediction. Additionally, a hybrid SNN-LSTM model with six layers significantly improved tank level prediction accuracy (R2 = 0.9777), highlighting the effectiveness of combining spiking-based temporal dynamics with long-term memory architectures. The three-layer MLP model with an additional Leaky Integrate-and-Fire (LIF) encoding layer demonstrated moderate performance improvements in displacement prediction (R2 = 0.9425) and pneumatic pressure prediction (R2 = 0.8841), validating the role of biologically inspired computation in industrial AI applications.
This thesis underscores the transformative potential of digital triplet architectures by merging AI, cognitive computing, Hebbian and non-Hebbian learning, and neuromorphic principles. It highlights the strategic role of hierarchical digital models, brain-inspired learning mechanisms, and advanced analytics in achieving resilient, perceptive, and heuristic capabilities for critical mechatronics and industrial automation applications. By leveraging bio-inspired AI and adaptive intelligence, this research lays the foundation for next-generation cognitive digital twins that self-optimize, predict, and respond autonomously advancing the cognitive automation paradigm in Industry 5.0 and beyond
The resurrection of digital triplet: A cognitive pillar of human-machine integration at the dawn of industry 5.0
Get PDFThe integration of AI technology with digital transformation has profoundly shaped the evolution towards digital triplet architecture, grounded in human-centric methodologies. By infusing human intellectual activities into both physical and cyberspace, innovative links between humans and machines are established. Despite limitations in transitioning from tangible human presence to the digital realm in cyberspace, extensive efforts are underway to harness emotional, visual, and oral responses, thereby enhancing the reasoning and predictive capabilities of digital twins. These advancements aim to elevate real-time human interactions with physical and virtual systems by integrating intelligent AI algorithms and cognitive computing systems into digital twins. This paper meticulously analyses recent trends in digital twins, tracing their evolution from traditional concepts and applications to a nuanced digital triplet hierarchy that incorporates human intuition, knowledge, and creativity within cyberspace. we delve into the hierarchical framework of the digital triplet, resonating with maturity, domination, and volition levels, enhances cognitive and perceptual capabilities in cyberspace. The study provides a systematic overview of the development of ultra-realistic digital models, incorporating real-time data-driven artefacts that integrate intelligent activities with multidomain, multiphysics, and multiscale simulations. The research scope is focused on augmenting the perceptive and heuristic capabilities of the digital triplet framework by utilizing AI in data analytics, retrieving heterogeneous data from virtual entities using semantic artificial intelligence technologies, and amalgamating AI and machine learning with human insight and perceptual knowledge. The proposed digital triplet hierarchy aims to enhance cyberspace's capacity for learning, cognitive skills, and knowledge transfer. It can be a guideline for the researcher to promote cognitive augmentation of the human brain through brain-machine/computer interface, virtual, augmented, and extended reality, fostering a symbiotic relationship between humans and machines in the industrial metaverse and industry 5.0. The paper discusses future directions for research and the challenges involved in developing intelligent digital twins towards the digital triplet paradigm, aiming to embody intelligent activities and cognitive capabilities within the framework of human–machine symbiosi
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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