Network Elements Research Articles

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.

Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records.

The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond.

SCADvanceXP—an intelligent Polish system for threat detection and monitoring of industrial networks

SCADvanceXP is an industrial network intrusion detection system that scans and monitors data exchange between engineering stations, field divides, controllers, supervisory control and data acquisition (SCADA), and other elements of the operational technology network in detail. SCADvanceXP has the potential to detect advanced attacks on industrial infrastructures with the use of rulebased, signature-based, and behavioural detection methods, which are supported by sophisticated machine and deep learning models. As a system developed in Poland, it addresses the needs of industry in that region of Europe. The goal of this work was to assess SCADvanceXP’s potential to detect common industrial threats. In order to check SCADvanceXP’s potential, an effort was undertaken to evaluate its functionality on major industrial threats. For that purpose, twelve malware strains interfering with industrial systems were described. Later, the SCADvanceXP functionality was overlapped on malware behavioural and detection markers, pointing out exact mechanisms in SCADvanceXP that would detect analysed threats. The results show that SCADvanceXP is able to detect a wide range of attacks on industrial networks. SCADvanceXP’s rich functionality is able to provide a high standard of security. However, if a threat is affecting systems not directly connected with industrial networks, SCADvanceXP will not be able to detect it. SCADvanceXP only monitors industrial systems; hence, corporate networks must be protected by a different solution to provide the required level of security. Nonetheless, SCADvanceXP is dedicated to operating within industrial networks and does not have access to regular IT networks. It can be concluded that SCADvanceXP is a specialist tool providing desired security for industrial networks.

Analysis of Artificial Neural Networks on Predicting Dementia

Dementia, a chronic neurological disorder characterized by memory loss and impaired cognitive ability, has affected tens of millions of people globally and projections estimate this number to rise in the coming decade. Despite its widespread impact, dementia remains one of many diseases with no true cure or definitive method of diagnosis. This study aims to explore the question of how artificial neural networks can be used to predict dementia. Analyzing current research on neural networks for disease diagnosis highlights a notable gap, known as the "black box conundrum," indicating the challenge of tracing steps leading to a given output. Another gap in the literature is the absence of comparative data on factors influencing successful neural network algorithms. Conducting a comprehensive cross-continental comparative analysis across North America, Asia, and Europe, and utilizing an experimental design with the UCI ML Repository’s Parkinson’s Database, this study specifically aims to fill the identified gaps. The findings reveal key elements for successful neural networks: a data sample exceeding 100, clinically intelligible data values, and a backpropagation model. In the experimental phase, a comparison between a backpropagation model and logistic regression, paired with Shapley value analysis, elucidated a 94.87% accuracy for the former, surpassing the latter's 92.3%. These findings extend beyond dementia, providing valuable insights for neural network applications in all healthcare settings. Future research endeavors should focus on refining and optimizing neural networks with the ultimate goal of achieving 100% accuracy and maximized efficiency.

A Literature Review on Access Control in Networking Employing Blockchain

Access Control (AC) in networking attempts to make sure that only authorized devices perform actions formed upon privileges defined for them with a view to prevent malicious users' entry and interaction in the communication grid. Blockchain solutions contain an arrangement of related blocks that naturally safeguards the trustworthiness, defending the incontestability, defend masked-identity of its transactions/transfers due to scattered consensus strategies and cryptographic solutions. Our survey comprehensively reviews BC-formed AC in broad scope of networking considering AC techniques while breaking down into 4 propositions and assessing them in terms of blockchain roles, AC technique and approach, network elements, and rest. We stockpiled a primary sample of 79 bibliographic references by weeding out them for screening criteria sought from scientific information reservoirs exploiting a qualitative and extensive strategy. Formed upon this survey, in blockchain-formed AC, blockchain can be exploited as an AC manager to administrate network devices and access information, implement automatic AC by means of smart contracts, secure storage of AC related data to reinforce overall AC security, and for safe data exchanging in the operation of AC. Minute assessment highlights that from blockchain-formed AC, 52.5% provide AC using blockchain itself or using smart contracts, 92.5% exploit sequential blockchain, 35% exploit PBFT consensus, provide 100% fine-grained and host-formed AC, 85% decentralized AC, 87.5% have single-factor authentication, 92.5% provide dynamic AC, and 45% have opted for IoT. Finally, we evaluate the chances and difficulties of the principle of blockchain-formed AC and then giving recommended actions to beat them.

Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

Recent Advances in CFD-based Bridge Fire Evaluations: A Brief Review

Bridges, as the main elements of transportation networks, demand significant attention due to their unique structural characteristics and high consequences of fire incidents. So, it is crucial to enhance the fire resilience of these structures. Various studies have focused on the integration of advanced fire modeling techniques, including computational fluid dynamics (CFD), the finite element method (FEM), and artificial intelligence (AI) to improve the fire responses of the bridges. This article provided an overview of recent advancements in bridge fire modeling and addressing methodologies, challenges, and future directions for enhancing the fire resilience of bridge structures. Natural language processing (NLP) was employed for an in-depth exploration of bridge fire engineering topics and integrated CFD, FEM, and AI techniques. It has highlighted the importance of AI in enhancing real-time fire prediction and reducing related computational demands, emphasizing the importance of developing AI integrated approaches for the accelerated and effective post-fire assessment of bridge structures, considering their unique structural challenges and high impact of fires.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

Layer‐specific imprints of traits within a plant–herbivore–predator network – complementary insights from complementary methods

Who interacts with whom is a key question in community and network ecology. The concept that these interactions may be driven by a match between the traits of consumer and resource species is known as trait‐matching. If trait‐matching would allow for general predictions of interaction structure based on sufficiently few and easily‐measurable traits, then this approach could replace the laborious description of each individual pairwise interaction. To resolve imprints of trait‐matching in a species‐rich tri‐trophic Salix–galler–parasitoid network, and to identify the most relevant traits, we applied five different methods, each approaching the same phenomenon from a different perspective. As traits, we used, body sizes, gall type (position on plant, structure of gall) and phenology, among others, as well as phylogenetic proxies. When jointly applied, the methods demonstrate distinctly different imprints of traits within the two bipartite network elements (Salix–galler versus galler–parasitoid interactions). Of the galler–parasitoid sub‐network's interactions, approximately half were explainable by the species traits used; of the Salix–galler sub‐network's interactions, traits explained at most two‐fifths. Gall type appeared to be the most important structuring trait in both networks. Phylogeny explained as much, or more than did our tested traits, suggesting that traits may be conserved and phylogeny therefore an effective proxy. Overall, the more specialized structure of the Salix–galler network versus the more nested structure of the galler–parasitoid network meant that different methods were more effective at capturing interactions and interaction structure in the different sub‐networks. Thus, our analysis reveals how structuring impacts may vary even between levels within the same multitrophic network, and calls for comparative analyses of trait matching across a wide set of systems and methods.

Blockchain for the digital twin-driven autonomous optical network

In recent years, optical networks have become increasingly large-scale and complex, resulting in mounting pressures on operation and maintenance management. The techniques of the artificial intelligence (AI)-driven digital twin (DT) have been applied to optical networks to pave the way for creating the self-optimizing and self-controlling autonomous optical network (AON). However, the AON is confronted with many security and reliability issues: the centralized architecture is prone to data loss during system disconnection and active/standby switchover, and the massive data, DT models, and control instructions in the AON all face the risk of being tampered with, directly affecting the effectiveness of network regulation. In addition, the reliability of complex autonomous processes should be ensured. To address these challenges, in this paper, we applied blockchain to a DT-driven AON with the designed architecture and deployment scheme to realize data loss prevention, anti-tamper storage, secure autonomous operations, access control, and network element controller decisions. A blockchain-based solution was studied by considering the quality of transmission (QoT) estimation and link optimization as examples. Four chaincodes were designed and developed to perform the model storage, service definition, telemetry subscription, data collection, model implementation, and instruction delivery in the AON. Furthermore, the proposed solution was developed and deployed using Hyperledger Fabric. The Hyperledger Caliper was used to test the performance of the proposed blockchain system. The distributed environment is used for performance testing to simulate the real scenario. The test results are analyzed to indicate the future development direction.

State-element-aware syndrome classification based on hypergraph convolutional network

Syndrome classification plays a key role in the clinical diagnosis and treatment with traditional Chinese medicine (TCM), aiming to identify the disease type. Given symptoms of a patient, existing approaches for syndrome classification are generally limited to modeling the interaction between symptoms and syndromes while ignoring the induction of state elements. To alleviate this issue, a state-element-aware hypergraph convolutional network (SEHGCN) is proposed to incorporate state elements into syndrome classification and discover high-order semantic relationships among TCM entities through hypergraph convolutional network (HGCN). Specifically, state elements are initially induced from symptoms by an extraction network, then symptoms and state elements are embedded via convolution on patient hypergraph to obtain the latent representation of patients. Finally, syndromes are classified by a multilayer perceptron (MLP). Extensive experiments on two TCM datasets show that the syndrome classification results with this proposed method are significantly improved over other competing methods.

Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

We present a computational approach that incorporates a convolutional neural network (CNN) for detecting internal delamination in a layered 2D plane-strain anisotropic composite structure of transient elastodynamic fields. The two-dimensional spectral element method (SEM) is utilized to simulate the propagation of elastic waves in an orthotropic solid sandwiched by isotropic solids and their interaction with the internal delamination cavity. This work generates training data consisting of input-layer features (i.e., measured wave signals) and output-layer features (i.e., element types, such as void or regular, of all elements in a domain).To accelerate training data generation, we utilize explicit time integration (e.g., the Runge–Kutta scheme) coupled with an SEM wave solver. Applying the level-set method additionally avoids having to perform an expensive re-meshing process for every possible configuration of the delamination cavities during the data-generation phase. The CNN is trained to classify each element as a non-void or void element from the measured wave signals. Clusters of identified void elements reconstruct targeted cavities. Once our neural network is trained using synthetic data, we analyze how effectively the CNN performs on synthetic measurement data. To this end, we use blind test data from a third-party simulator that explicitly models the traction-free boundary of cavities for anisotropic materials without the application of the level-set method.Our numerical examples show that our approach can effectively detect the internal cavities in an anisotropic structure made of aluminum and carbon fiber-reinforced epoxy using the measured elastic waves without any prior information about the cavities’ locations, shapes, and numbers. The presented method can be extended into a more realistic 3D setting and utilized for the nondestructive test of various anisotropic composite structures.