Network Elements Research Articles

Dimension Prediction and Microstructure Study of Wire Arc Additive Manufactured 316L Stainless Steel Based on Artificial Neural Network and Finite Element Simulation.

The dimensional accuracy and microstructure affect the service performance of parts fabricated by wire arc additive manufacturing (WAAM). Regulating the geometry and microstructure of such parts presents a challenge. The coupling method of an artificial neural network and finite element (FE) is proposed in this research for this purpose. Back-propagating neural networks (BPNN) based on optimization algorithms were established to predict the bead width (BW) and height (BH) of the deposited layers. Then, the bead geometry was modeled based on the predicted dimension, and 3D FE heat transfer simulation was performed to investigate the evolution of temperature and microstructure. The results showed that the errors in BW and BH were less than 6%, and the beetle antenna search BPNN model had the highest prediction accuracy compared to the other models. The simulated melt pool error was less than 5% with the experimental results. The decrease in the ratio of the temperature gradient and solidification rate induced the transition of solidified grains from cellular crystals to columnar dendrites and then to equiaxed dendrites. Accelerating the cooling rate increased the primary dendrite arm spacing and δ-ferrite content. These results indicate that the coupling model provides a pathway for regulating the dimensions and microstructures of manufactured parts.

Cannabidiol Alleviates Neurological Deficits After Traumatic Brain Injury by Improving Intracranial Lymphatic Drainage.

Traumatic brain injury (TBI) persists as a substantial clinical dilemma, largely because of the absence of effective treatments. This challenge is exacerbated by the hindered clearance of intracranial metabolic byproducts and the continual accrual of deleterious proteins. The glymphatic system (GS) and meningeal lymphatic vessels (MLVs), key elements of the intracranial lymphatic network, play critical roles in the clearance of harmful substances. Cannabidiol (CBD) has shown promise in reducing metabolite overload and bolstering cognitive performance in various neurodegenerative diseases. The precise mechanisms attributing to its beneficial effects in TBI scenarios, however, are yet to be distinctly understood. Utilizing a fluid percussion injury paradigm, our research adopted a multifaceted approach, encompassing behavioral testing, immunofluorescence and immunohistochemical analyses, laser speckle imaging, western blot techniques, and bilateral cervical efferent lymphatic ligation. This methodology aimed to discern the influence of CBD on both neurological outcomes and intracranial lymphatic clearance in a murine TBI model. We observed that CBD administration notably ameliorated motor, memory, and cognitive functions, concurrently with a significant reduction in the concentration of phosphorylated tau protein and amyloid-β. In addition, CBD expedited the turnover and elimination of intracranial tracers, increased cerebral blood flow, and enhanced the efficacy of fluorescent tracer migration from MLVs to deep cervical lymph nodes (dCLNs). Remarkably, CBD treatment also induced a reversion in aquaporin-4 (AQP-4) polarization and curtailed neuroinflammatory indices. A pivotal discovery was that the surgical interruption of efferent lymphatic conduits in the neck nullified CBD's positive contributions to intracranial waste disposal and cognitive improvement, yet the anti-neuroinflammatory actions remained unaffected. These insights suggest that CBD may enhance intracranial metabolite clearance, potentially via the regulation of the intracranial lymphatic system, thereby offering neurofunctional prognostic improvement in TBI models. Our findings underscore the potential therapeutic applicability of CBD in TBI interventions, necessitating further comprehensive investigations and clinical validations to substantiate these initial conclusions.

Network element methods for linear elasticity

Network element methods for linear elasticity

Approximation of One-Dimensional Darcy–Brinkman–Forchheimer Model by Physics Informed Deep Learning Feedforward Artificial Neural Network and Finite Element Methods: A Comparative Study

Approximation of One-Dimensional Darcy–Brinkman–Forchheimer Model by Physics Informed Deep Learning Feedforward Artificial Neural Network and Finite Element Methods: A Comparative Study

Does ecological network affect the construction land spatial development intensity? -An empirical study based on the main urban area of Jinan, China

The ecological network as the cornerstone of urban ecological security protection, understanding the interaction between ecological network and construction land spatial development intensity (CLSDI), which is crucial for guiding the orderly utilization of territorial space and promoting regional sustainable development. However, the information on the complex spatial and temporal interactions between ecological networks and CLSDI has not yet been adequately studied. This study takes the main urban area of Jinan City as an example, and explores the spatial and temporal coupling relationship and change rule of ecological network and CLSDI during 2006–2018 through spatial multiple buffer analysis tools, and proposes the optimization path of construction land spatial development under the perspective of ecological network. The results showed that: from 2006 to 2018, there is a significant spatial effect between CLSDI and ecological network elements; within the buffer zone of 300–3000 m for ecological sources and nodes, CLSDI rises with the increase of buffer zone distance; while the ecological corridor exhibits decreased CLSDI within the 500-m buffer zone, the 500–3000-m CLSDI tends to increase. Based on the empirical analysis, the optimization path of construction land spatial development is proposed from three aspects: ecological source, corridor and node. The 300 m control range of the ecological source is designated as a “specific protection zone”, and strictly control the increase of the CLSDI within the zone; the ecological corridor is set up with differential protection and utilization zoning, and CLSDI and activity types are controlled in a hierarchical and classified manner; the CLDSI around the ecological nodes is strictly controlled in order to prevent encroachment of construction activities on the nodes. This study provides a reference value for constructing a territorial spatial pattern coordinated with ecological protection and development of construction.

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works.

Social Networks and Communication Impairment: Racial Differences Among Stroke Survivors

Purpose: Recent evidence highlights the importance of social networks—the composition of social relationships—and their characteristics in stroke recovery. This study explores the relationship between social networks and poststroke communication outcomes. Method: A cohort of 30 non-Hispanic Black ( n = 12) and non-Hispanic White ( n = 18) adult stroke survivors completed the Stroke Impact Scale (SIS) Communication subtest and the Stroke Social Network Scale (SSNS). The SSNS captures elements of social networks including satisfaction and contact with friends, relatives, children, and groups. The relationship between the SIS Communication subtest scores and SSNS scores was explored using ordinal Bayesian regression estimation models adjusting for sex, age, time postonset, marital status, and stroke type. Results: Average SIS Communication scores differed ( t = 2.07, p = .335) between White ( M = 81.75, SD = 22.90) and Black ( M = 95.71, SD = 4.95) participants, but SSNS Comprehensive scores were similar between the two groups ( t = −0.89, p = 1.00). SSNS children subtest scores were higher among the Black than the White ( t = −1.73, p = .0473) participants. Similarly, satisfaction subtest scores were comparatively higher among Black than White participants ( t = 1.85, p = .451). SSNS Satisfaction, Friends, and Groups subtest scores were positively associated with SIS Communication, indicating strong associations with lower impairment. SSNS Children subtest scores were negatively related to SIS, indicating higher impairment. Although most subtest associations were similar for both groups, the associations with SSNS Children and Friends subtests differed between racial groups. Conclusions: Levels of communication impairment were correlated with child, friendship, and group attachments among participants. However, strong social ties to children and friends had different associations among racial groups.

A Comprehensive Flood Risk Inundation Mapping and Hybrid Model for Flood Forecasting in the Panam River Basin

The forecasting of flooding plays a pivotal role in the field of hydrology and serves as an essential measure for preventing any possible flood damage. This study presents an analysis of the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to prevent floods in river basins. ANFIS represents a hybrid approach that combines elements of neural networks and fuzzy logic methodologies for the purpose of analyzing and comparing input and output data. Performance evaluation of ANFIS is based on the ratio of Discrepancy Ratio (D) along with the determination coefficient (R2), Coefficient of correlation (R), and Root Mean Square Error (RMSE). Results: The best ANFIS model was the one that gave the best results during training. The RMSE was 622.43, with a correlation coefficient of 0.93 and an R2 of 0.86. Nevertheless, its validation performance demonstrated slightly elevated RMSE figures and lower correlation and R2 values, with RMSE at 2847.99, R at 0.91, and R2 at 0.83. An Artificial Neural Network (ANN) model with three transfer functions (SIGMOID, HYPERBOLIC TANGENT, and LINEAR) is developed and evaluated based on R = correlation and MSE = mean squared error. Furthermore, the study also entails the development of a flood model for the research area, consisting of 38 cross profiles for a detailed analysis of the river’s profile and flood inundation. This analysis aims to provide precise recommendations for flood protection measures.

Fabrication and investigation of a memristive crossbar array artificial synapses based on electrochemical titanium oxide for neuroelectronics

The formation of free resistive switching between the low-resistance state (LRS) 100.2 ± 9.1 Ω and the high-resistance state (HRS) 890.3 ± 81.7 Ω was observed during 1000 switching cycles, the ratio of the resistance value of HRS to LRS was 8.9 ± 1.6. The dependence of the increase in the response current during paired pulse facilitation on the number of pulses and the magnitude of the voltage pulses is shown. The results obtained can be used in the development of technological foundations for the formation of high-performance multilevel artificial synapses for elements of neuroelectronics and hardware neural networks.

IMPROVING THE EFFICIENCY OF TRAFFIC LIGHT CONTROL AT ROAD INTERSECTIONS

From the functional point of view, the intersection is the most complex element of the road network. It is here that the traffic flows in different directions cross, and various maneuvers take place. This indicates that the intersection is a place with an increased concentration of conflict situations and an increased risk of traffic accidents. At most high-flow intersections, traffic is controlled by traffic lights, and their inefficient operation can lead to unnecessarily long wait times and overall increase in traffic delays. The research analyzed a regulated intersection at the crossing of Stepana Bandery and Viacheslava Chornovola main streets in the city of Rivne. This intersection is characterized by constant significant traffic jams. To simulate traffic conditions at this intersection, the research used PTV Vissim software. Observations of traffic flows, their distribution by directions, and traffic light regulation parameters were used as initial data for modeling the intersection traffic scheme. The main problem that must be solved when considering isolated traffic light intersections is the calculation and optimization of the signal plan, which involves: determining the number of phases; determining cycle duration; distribution, i.e. determining parts of available green time for each phase; modeling of traffic situations that may arise due to the passage of priority vehicles, congestion of vehicles in intersection areas during peak periods or other situations. At the same time, it is necessary to achieve the best possible characteristics of the intersection functioning. According to the research results, the road conditions were modeled and the signal plan was optimized with the use of PTV Vissim software. Simulation modeling of the intersection included drawing a road network, installing traffic lights (signal controllers) with a description of their work (choosing the type of light signaling devices, creating signal groups and traffic light signals, parameters for coordinating signals), forming pedestrian zones and a node, performing calculations with subsequent analysis of the received data. In order to improve the efficiency of the intersection, two options for the operation of the traffic light controllers are offered: 1. Lengthening of the “green” phase, for the convenience of turning to the left (by reducing the phase of oncoming traffic in one direction by 5 seconds). The total duration of the cycle of 70 seconds will not change; 2. Implementation of the third phase – fully pedestrian in all directions, lasting 20 seconds. The total duration of the cycle will increase to 90 seconds. A more progressive measure can be the introduction of adaptive systems, which are based on new traffic monitoring technologies and allow obtaining accurate data on traffic flows in real time and performing adaptive control of traffic lights, that is, adapting the signal plan in real time to changes in traffic flows.

Исследование системы электропотребления угольной шахты

The article is devoted to the study of information sources of the power consumption system of a coal mine. All parameters and the state of structural elements of the power supply network can serve as sources of information. The process of transmitting information about these functional changes is an information system of the mine distribution network, which can serve as an information base for monitoring and controlling the process of mine power consumption. Other sources of information are daily schedules of electrical load for power supply centers, energy-intensive installations and the mine as a whole. Obtaining quantitative characteristics of information sources of the coal mine power consumption system is the main task of the conducted research

FORMALIZATION OF THE PROCEDURE FOR ASSESSING THE ELECTRIC SUPPLY EFFICIENCY IN WORKSHOP NETWORKS OF INDUSTRIAL ENTERPRISES

There is defined that the efficiency of workshop power supply networks largely determines the efficiency of the power supply system of the industrial enterprise as a whole and is determined by minimizing the reduced costs, which have two components: capital investments and the cost of electricity losses in workshop networks. Calculating the reduced costs during a technical and economic comparison of options is a labor-intensive task, since there is a large variability in the choice of circuit design, the choice of cross-sections of the conductors of the supply and distribution workshop network, as well as taking into account the design features of the workshop network. The analysis of the characteristics of the efficiency factors of workshop power supply was carried out. The basic components of the effective functioning of the workshoppower supply circuit are identified, namely: discreteness of electrical equipment; its placement in the workshop; operating modes of current-using equipment (CUE); loading schedule; ratio of the cost of electricity and the unit cost of workshop network elements. There has been determined that in any workshop power supply scheme, the duration of switching CUE on the electric power supply (CUE with a constant and variable operating mode), as well as the shape of the load schedule, largely determines the efficiency of its operation. Obtained results of a numerical experiment, when performing design of workshop networks in the DIPROPROM design institution (Zaporozhye), showed the effectiveness (3-7%) in determining the operational characteristics of workshop networks in comparison with existing engineering methods.

Creation of a Software Tool with Elements of an Artificial Neural Network for Data Forecasting

Currently, Russia is moving towards automating the collection of information from metering devices for heat, gas, water, electricity, etc. Technologies are being developed that allow transmitting data on resource consumption through various communication channels, both wired and on-air. There are already devices and devices that allow transmitting readings of electricity metering devices over existing wired communication networks, and this issue is being resolved at the legislative level, federal Law No. 522-FZ of 01.07.2020 has been adopted. The basis for the research is to save time and effort to obtain the necessary information to take into account the readings of electricity from substations, increase the speed of decision-making, to be able to predict the situation with big data to identify abnormal values and eliminate them (for example, identified data from electricity meters will reduce the time labor costs of accounting operators and economic losses of the energy supply company). The article proposes software methods for detecting data on electricity losses based on the use of artificial neural network algorithms and allowing to detect inconsistencies of commercial data readings of electric meters, which will reduce the commercial component of electricity losses. A verification calculation of an artificial neural network was performed for inconsistencies in the data on the transmitted electricity.

AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks

The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system’s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions.

Dependability of local power systems based on autonomous generation units

Aim. The paper aims to create and verify methods for predicting the dependability of autonomous power supply to local low-power consumers. Methods. The work uses simulation tools, i.e., computational experiment for predicting the dependability and safe service life of autonomous cluster distributed energy networks that include various power generation installations, power transmission lines and an automated self-regulation system for the purpose of diversifying the risks of failure. Results. Using methods of probabilistic analysis of technical system safety, the dependability of power supply is predicted in terms of possible energy network failures and threats to the life of consumers. The problem can be solved by simulating and predicting the moment of failure of a power network, i.e., critical violation of design conditions, accident, etc. This scenario allows – for long periods of operation – simulating failure situations due to degradation and destruction of elements, units, power lines, as well as failures under multidimensional uncertainty of prediction of dependability and quality of power supply. The key output of simulation is a set of recommendations to ensure energy security. The paper presents simulated dependability indicators, analysis of the effect of structural design solutions underlying a distributed energy network on the dependability and energy security. Conclusion. The use of computational experiments and results of power supply dependability simulation as part of predicting probabilistic indicators of failures of equipment and elements of cluster energy networks, identifying their consequences for the energy security of sparsely populated and remote locations lays the foundation for quality management of energy networks. Additionally, in the future, additional causes and initial events may be included in the model for the purpose of studying the facts of failures. For instance, such causes as uneven generation using renewable sources, finite fuel supply for conventional generation sources, reliability of the power grid personnel. The proposed approach allows verifying promising new energy network design solutions, for example, including energy storage systems into a distributed energy network that – at various points in time – may be either “generators” or “consumers”. Simulation of local energy networks is of practical interest and is important for improving their consumer quality, resistance to hazardous environmental effects.

A universal parallel simulation framework for energy pipeline networks on high-performance computers

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

PUBLIC POLICIES TO REDUCE SWITCHING COSTS LINKED TO MANDATORY ACCESS TO CERTAIN ELEMENTS OF THE POSTAL NETWORK IN THE EU AND THE EFFECTS OF THE RETENTION COSTS

Purpose: Our objective is to examine the switching costs that exist in the European postal service, where an ambitious process of opening up to competition has been carried out since 1997, as well as the different types of retention costs. Theoretical Framework: The theoretical framework is determined by the sectoral regulatory framework regarding the liberalization of services of general economic interest in the EU, the experience of postal operators in recent decades and the changes experienced in the demand for postal services in the current context of profound technological change in the field of communication. Design/Methodology/Approach: This research work is carried out through a systematic review of the relevant specialized literature, the study of the policy decisions adopted in the EU, the changes in the strategies of postal operators and the rigorous analysis of the evolution of the demand of the postal services in a context of rapid and profound change. Finding: Inadequate regulation of the access to some elements of postal infrastructure or services within the scope of the universal postal service, such as the postcode system, address database, post office boxes, delivery boxes, re-direction and retour to sender service, can generate switching costs and thus limiting competition, with negative effects on the consumer surplus and social welfare. Standardization and access to these elements may be regulated separately Research, Practical & Social Implications: This article proposes solutions to the technical functions of coordination between operators. Our proposals are in line with sectoral directives aimed at strengthening competition in the long term in the postal market. Originality/Value: A correct classification of the change costs applied to the liberalized postal service is made. Likewise, a classification of retention costs. We must underline the progress that our proposals represent to reduce the costs of switching to elements of the postal network whose access is mandatory.

Artificial neural network-based cooling capacity estimation of various radiator configurations for power transformers operated in ONAN mode

Power transformers submerged in oil are universally acknowledged as very useful elements in electrical power networks. A fraction of the electrical energy involved in the conversion from high to low voltages is lost as thermal energy, which is produced inside the transformer's windings and core. The effective dissipation of heat is of paramount significance and can be accomplished through the strategic installation of radiators on the tank. This study aims to examine the total cooling capacity of a radiator employed for cooling a power transformer operating in oil, natural air, and natural mode. The investigation is conducted by varying the design parameters, specifically the number of fins per radiator and the radiator length. The study also utilizes computational fluid dynamics results to achieve a substantial convergence with experimental findings. In the context of the verification study conducted for a specific study point, it was found that the simulation outcomes at a mass flow rate of 0.15 kg/s corresponded to about 7.4 % of the experimental results. Additionally, the cooling capacity value obtained was 7.2 %. In the context of machine learning, the performance of the numerical approach was assessed by employing the Bayesian regularization method. The evaluation revealed that the margin of deviation, mean squared error, and coefficient of determination (R2) metrics yielded values of −0.001 %, 1.32E-02, and 0.99930, respectively.

The effects of knowledge network characteristics on R&D alliance formation

ABSTRACT Firms’ knowledge characteristics shape their research and development (R&D alliance activities). When denoting the characteristics, the alliance literature has exclusively focused on firms’ internal knowledge characteristics, leaving structural properties of their knowledge relatively unexplored. Therefore, this study aims to investigate how firms’ knowledge network characteristics are associated with the propensity to ally. By using 235,024 unique alliance dyads between U.S. semiconductor firms and their potential partners, we examine the effects of two knowledge network characteristics – degree centrality and structural holes – on alliance formation. We further examine the joint effects of the knowledge network characteristics. Our findings reveal the curvilinear relationship between degree centrality and the likelihood of alliance formation. We also find support for the positive joint effects on the likelihood of alliance formation. These findings provide implications for the varying propensity to ally associated with the knowledge network characteristics, i.e. structural positions of a firm’s knowledge elements in the sector-level knowledge network.

BiVi-GAN: Bivariate Vibration GAN.

In the domain of prognosis and health management (PHM) for rotating machinery, the criticality of ensuring equipment reliability cannot be overstated. With developments in artificial intelligence (AI) and deep learning, there have been numerous attempts to use those methodologies in PHM. However, there are challenges to applying them in practice because they require huge amounts of data. This study explores a novel approach to augment vibration data-a primary component in traditional PHM methodologies-using a specialized generative model. Recognizing the limitations of deep learning models, which often fail to capture the intrinsic physical characteristics vital for vibration analysis, we introduce the bivariate vibration generative adversarial networks (BiVi-GAN) model. BiVi-GAN incorporates elements of a physics-informed neural network (PINN), emphasizing the specific vibration characteristics of rotating machinery. We integrate two types of physical information into our model: order analysis and cross-wavelet transform, which are crucial for dissecting the vibration characteristics of such machinery. Experimental findings show the effectiveness of our proposed model. With the incorporation of physics information (PI) input and PI loss, the BiVi-GAN showed a 70% performance improvement in terms of JS divergence compared with the baseline biwavelet-GAN model. This study maintains the potential and efficacy of complementary domain-specific insights with data-driven AI models for more robust and accurate outcomes in PHM.