(Retracted) Temporary Petri-nets-based method for synthesizing network models

Industrial Internet of Things (IIoT) is becoming ubiquitous across different industries. However, there are several challenges that needs to be overcome especially in a manufacturing environment which predominantly has legacy hardware and software systems. Further, finding a cost-effective solution becomes crucial when manufacturing facilities are geographically distributed. In this paper, we provide some overview of our complexities across data integration, data analytics, and business impact. Through this knowledge sharing, we address some of the key roadblocks to transform a legacy manufacturing facility into an Industry 4.0 system. All of the findings presented here are based on a two-year pilot project executed at the Boeing Everett site. We believe that digital transformation is must for our future success, but to accomplish this goal at a reasonable cost and effort will be a continued challenge.

We explore the relationship between a yield stress fluid flow in a Hele-Shaw cell with irregular walls, and a non-Darcy 2D porous medium flow with limiting pressure gradient. The continuum (Hele-Shaw) flow is a much-studied simplification of a cementing displacement flow model, solved via a variational formulation that leads to a convex streamfunction minimization problem. Our interest is with the analogy between the discretization used for the numerical solution and network flows that model the associated porous media flow, i.e. via the pore-throat approach. A staggered mesh, with pressure nodes at the cell corners and streamfunction nodes at the cell center is used for the continuum problem, which naturally separates into a network representation comprising primal and dual graphs, linking streamfunction and pressure nodes, respectively. We show explicitly how the continuum model defines a network model and vice versa. We develop the variational form of the network flow, including an appropriate (discrete) streamfunction minimization and a discrete version of the principle of virtual work. Two network models are explored, based on different interpretations of the minimization problem. The network flow results are compared with analogous computed continuum flow results in 3 specific geometries. We find that our network model I, which is the most natural interpretation of the continuum model as a network flow using our discretization, generally under-predicts flow rates. This is problematic from the perspective of considering the network flow as an approximation to the porous media or Hele-Shaw flow. Network model II rectifies this situation, via a pressure interpolation method. In our examples we find that the network II flow converges to the continuum flow as the mesh and network are refined. This is not the usual comparison made, as in many pore-throat models the network is fixed according to the underlying pore-space geometry. Despite the differences, both network models have their own advantages and disadvantages. Network model I offers a more natural way of modelling the flow and is easier to apply, for example to complex meshes, e.g. unstructured triangular. Network model II gives the more accurate physical representation of the Hele-Shaw flow. Lastly, we have developed approximate algebraic relationships for the total flow rate as a function of the total applied pressure gradient, both close to the critical onset pressure and for large pressure gradients. These correlations align well with previous findings for Bingham fluid flows in porous media. • Hele-Shaw cell – porous media analogy derived for Herschel-Bulkley fluids. • Discrete pore-throat network models derived. • Discrete principle of virtual work. • Convergence as network is refined. • Improved network model that gives convergence.

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

The paper presents an image-based network model of retinal vasculature taking account of the 3D vascular distribution of the retina. Mouse retinas were prepared using flat-mount technique and vascular images were obtained using confocal microscopy. The vascular morphometric information obtained from confocal images was used for the model development. The network model developed directly represents the vascular geometry of all the large vessels of the arteriolar and venular trees and models the capillaries using uniformly distributed meshes. The vasculatures in different layers of the retina, namely the superficial, intermediate and deep layer, were modelled separately in the network and were linked through connecting vessels. The branching data of the vasculatures was recorded using the method of connectivity matrix of network (the graph theory). Such an approach is able to take into account the detailed vasculature of individual retinas concerned. Using such network model, circulation analyses to predict the spatial distribution of the pressure, flow, hematocrit, apparent viscosity and wall shear stress in the entire retinal vasculature can be carried out.KeywordsMurine retinanetwork topologymorphological datanetwork modelspatial pressureflowratevelocitywall shear stress

Ideally, quality is designed into software, just as quality is designed into hardware. However, when dealing with legacy systems, demonstrating that the software meets required quality standards may be difficult to achieve. Evolving customer needs, expressed by new operational requirements, resulted in the need to develop a legacy software quality assurance program at Los Alamos National Laboratory (LANL). This need led to the development of a reverse engineering approach referred to as software archaeology. This paper documents the software archaeology approaches used at LANL to demonstrate the software quality in legacy software systems. A case study for the Robotic Integrated Packaging System (RIPS) software is included to describe our approach.

The modernization of legacy software is a growing priority for U.S. enterprises that want to remain political or business competitive in a data-driven economy. Although legacy systems often serve a central role in an organization or department, they come with various burdens including high maintenance costs, low elasticity scalability, and a lack of interface capacity with modern technologies. Therefore, like many aspects of innovation and digital technology, Artificial Intelligence (AI) has the potential to transform legacy systems by allowing automated code refactoring, in system optimization, and process decision management. The authors present a comprehensive review of AI-enabled approaches to successfully and cost-effectively modernize legacy systems for enterprise applications. They highlight the need to provide organizations and enterprises with the ability to be adaptive or flexible in a sustained and cost-effective manner for a wide scope of legacy system modernization. Based on new literature in legacy systems using AI, the authors provide examples of applications in enterprise resource planning, smart manufacturing systems, cloud integration and migration, and multi-cloud optimization and cost savings activities. Also presented are frameworks and best practices for successful implementation for each of these new areas, and the opportunistic challenges of analysis complexity of integrated systems, integration to newer technological spaces, and systems knowledge or skills gaps. The data shows that while AI extends current functional capacity of legacy systems, it also calibrates legacy systems within current governance expectations, strategic outcomes for digital transformation, flexible scaling and strategies for secure cloud capabilities, and is the safest and most cost-efficient approach to modernizing legacy systems to enhance or to reduce enterprise risk.

In order to solve the problem of excessive parameters and slow computing speed of classical neural networks, this paper uses the powerful parallel computing power of quantum computing to improve the computing speed of classical neural network models, and proposes a hybrid continuous variational quantum neural network (HCVQNN) model that can be used for network intrusion detection. The continuous variable layer of the model is realized through Gaussian gate and non-Gaussian gate, in which Gaussian gate performs operations such as quantum state weight addition and offset term setting, and non-Gaussian gate performs nonlinear operations, thereby improving the overall expression ability of the network model. In addition, aiming at the unbalanced problem of UNSW-NB15 in the network intrusion dataset, this paper proposes to design the algorithm from the feature level and the algorithm level, using the ReliefF algorithm for feature selection at the feature level, and oversampling and undersampling processing at the algorithm level by combining the Borderline-SMOTE algorithm and GMM algorithm. The classification experimental results on the UNSW-NB15 network intrusion dataset show that compared with the other two network models, the HCVQNN network model obtains higher classification accuracy and lower loss function value in both binary classification and multi-classification tasks, and the classification accuracy for minority categories is also improved.

Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium)

As the metaverse continues to expand, it becomes increasingly critical to have human-centric networks that are both efficient and high-performing to optimize the user experience. Network modeling plays a fundamental role in optimizing and allocating resources efficiently, and configuring networks to satisfy the demands of diverse applications and users. Recently, traditional queuing theory-based approaches to network modeling have given way to machine learning-based methods. These methods rely on vast amounts of data for building precise models. Although high-precision simulators are ubiquitous, data collection is still an expensive and time-consuming process, resulting in a data bottleneck. In this paper, we propose a weakly supervised learning approach to modeling networks for human-centric networking in the metaverse. Specifically, we identify that queuing theory-based labels can be used to design the supervision signal at a very low cost. Therefore, we propose an approach that combines the inaccurate network modeling obtained from queuing theory-based approaches with an efficient and precise network model through only a small amount of simulation data. To make it a reality, we propose a novel neural network model that combines the powerful graph neural network and transformers. Additionally, we propose several additional supervision signals and a training algorithm to build a better network model. Experimental results demonstrate that our approach reduces the burden of data collection while achieving prediction accuracy comparable to results from large amounts of expensive simulation data. Furthermore, our approach exhibits superior generalization ability.

Non-intrusive load disaggregation is designed to estimate the power consumption of each appliance based on the total power of the appliance in the household. Conventional machine learning algorithms cannot accurately extract semantic information from time series data, which motivates us to implement nonintrusive load disaggregation using residual gated recurrent neural networks model (Res-GRU). First, the networks model use multi-scale convolution kernels networks model extract time series data features, and will get multiple map fusions. Secondly, the networks model use residual learning to deepen the network to extract deep load features. Finally, the networks model use the gated recurrent unit to reset and update high level features. In this way, we can get the output power value of the target appliance. The experimental results show that the proposed network model has a good disaggregation effect.

In principle, network models can replicate exactly the microstructure of porous media. In practice, however, network models have been constructed using various assumptions concerning pore structure. This paper presents a network model of a real, disordered porous medium that invokes no assumptions regarding pore structure. The calculated permeability of the model agrees well with measured permeabilities, providing a new and more rigorous confirmation of the validity of the network approach. Several assumptions commonly used in constructing network models are found to be invalid for a random packing of equal spheres. In addition, the model permits quantification of the effect of pore-scale correlation (departure from randomness) upon permeability. The effect is comparable to reported discrepancies between measured permeabilities and predictions of other network models. The implications of this finding are twofold. First, a key assumption of several theories of transport in porous media, namely that pore dimensions are randomly distributed upon a network, may be invalid for real porous systems. Second, efforts both to model and to measure pore-scale correlations could yield more accurate predictions of permeability.

Traditional bridge maintenance approaches often lack the capability to capture interactions among structural elements or assess the vulnerability of freight networks to disruptions such as bridge closures. To overcome these limitations, this study introduces a data-driven bridge maintenance framework that integrates artificial intelligence, geographic information systems, and graph-theory-based network modeling. A comprehensive graph-based network representing Georgia’s National Highway Freight Network was developed using geospatial coordinates and roadway intersection data to evaluate structural and topological criticality at the network level. Structural condition forecasting of individual bridges was conducted using advanced recurrent neural network (RNN) models, including long short-term memory and Gated Recurrent Unit architectures. These models predict future deck condition ratings based on historical element data, average daily truck traffic, age, maximum span length, and the number of main-unit spans. The integrated RNN-driven, graph-theory-based framework uncovers key patterns influencing bridge performance and identifies topological weaknesses that may compromise freight mobility. This analysis enables risk-informed prioritization of maintenance, repair, and replacement strategies, supporting a shift from reactive to predictive decision-making and enhancing the resilience of critical transportation infrastructure. Findings highlight the framework’s utility in evaluating the impacts of disruptive events on bridge closures and network accessibility.

This paper describes the development of an integrated production network model for a major producing field located in the Western Offshore Region of India, the second most prolific oil field in India. The purpose is threefold: a) presentation of a case history describing successful implementation of optimisation methodology in a major gas lifted field, b) outlining the structure and methodology for constructing and calibrating a large integrated production model, and c) illustrating the potential applications in running the completed model in optimisation mode. The Heera Field, currently producing from 119 strings (all on gas lift except one) has been on production since 1984 and with recovery in the region of 20% considerable potential still exists for improving production and recovery. One of the areas and challenges for improvement involves the optimisation of the gas lift performance. The Heera network model described in this paper includes the multi-phase well production, gas lift injection, production processing and gas lift compression facilities that comprise the asset. The development of the network model comprised the following elements: Well models were constructed for all the producing strings using nodal analysis. Outflow performance was calibrated to existing flowing gradient survey (FGS) data and recent production test data. Fluid properties were characterized based on Black Oil correlations using four independent PVT datasets, and tuned to a single correlation across the entire field. Approximately 450 independent pipe/flowline objects were included in the model to represent major pipelines, risers and downcomers. Three major processing platforms, comprising 5 HP separators, MP and LP separators, and 5 gas lift compressors operating in parallel were modelled. Production from neighbouring complexes (Neelam and ICP) was included in the form of "object sources" with overall delivery point at the Uran terminal located onshore. The network model was built, history matched and calibrated to actual field data, within a tolerance of <1% for the measured liquids production. The calibrated model was then used to run a number of optimisation scenarios, in an effort to find ways of improving production and operating efficiency. Optimisation investigations conducted within the framework of this project indicate potential oil uplift of between 6-8% from a combination of well interventions (based on individual well modelling and gas lift diagnostics) and network optimisation (lift gas reallocation), as well as a reduction in total lift gas requirement and compressor horsepower. Besides the scope for production gains and operational improvements, the project also highlights a number of higher level issues relevant to the development and employment of integrated production network models: The successful implementation of the Heera network model is the first of its kind in ONGC's India operations. The technology utilised in this project is capable of modelling large scale complex production networks in a robust and reliable manner. Complexity and size need no longer be barriers to successful implementation of network modelling and optimisation. The technology is easily transferable for implementation by production engineering and operations staff in the field, provided standardised workflows are identified and new work practices implemented to allow improved data management and model maintenance. The maintained integrated production network model provides a platform for analysing and diagnosing a wide variety of production and equipment related issues in the field. Recommendations from the model are currently being implemented in the field. Based on the modelling recommendations, well interventions and lift gas reallocation have so far produced oil production increases in the region of 1700 BOPD.

The modularization of large legacy software systems has attracted a great deal of attention in recent years. Such modularization improves the maintenance and reuse of smaller, more manageable pieces of program source codes, and also provides insight into the overall structure of the software system. This paper develops a methodology to determine the modularization programming of a legacy software system. This methodology breaks programs into modules, or groups of related functions based on the observation that a module can be defined as a group of functions having high cohesion and low coupling. Furthermore, the determination of good alternatives to perform the desired attributes, with some fuzzy constraints, is crucial in building software systems. With known modules, a rule-based fuzzy representation of the module development problem is presented, while the tradeoff of the attributes of performance among modules is analyzed, using a fuzzy neural network approach. The approach taken to reach this solution is illustrated with simulation software.

The construction of large-scale engineering project usually relies on the development of many new software systems, whereas it would be very expensive and time consuming if these new software systems are completely developed anew. In order to solve the problem, a model of legacy software reuse is proposed in this paper. The model is based on code clone detection. Firstly, code clone in source code of legacy software is detected by means of code clone detection tool. Secondly, abstract syntax trees of the functions which contain code clone are created. Thirdly, the degree of variation between the functions which contain the code clone belongs to the same clone set is calculated according to their abstract syntax trees, and then some functions whose similarities of abstract syntax trees are in the allowed range are combined. Finally, the combined functions and other frequently invoked functions are refactored into new functions or encapsulated into new classes, and all of these functions or classes can be reused as components in the development of new software systems. The test result shows that the reuse method based on this model can shrink the scope for searching the reusable component in legacy software systems, and thus improve the efficiency of legacy software reuse.