Exchange Of Model Data Research Articles

Semantic Web-Based Energy-Efficient Design of Dual-Mode Vehicles Based on Deep Neural Networks

Dual-Mode Vehicles (DMVs) represent a new and sustainable solution to the problems of urban mobility, and energy consumption. This proposed work of a DMV can operate without a conventional battery. Instead, it uses a combination of energy management technology, renewable energy systems, and other energy sources to ensure continuous operation. The efficiency and the working statistics of such DMV could also be sufficiently monitored through semantic techniques. The user can freely choose the type of working of the vehicle based on the current inputs given by the users. These semantics serve a crucial role in establishing a reference model for seamless data exchange between the trained Bald Eagle Search (BES) algorithm and the deep neural network (DNN). This proposed work has been implemented and the performance has been analyzed concerning the State Of Charging (SOC), generator voltage, and DC-DC converter voltage. The proposed work yields higher SOC and voltage than other existing schemes.

A Blockchain-Driven Smart Broker for Data Quality Assurance of the Tagged Periodic IoT Data in Publisher-Subscriber Model

The Publisher-Subscriber model of data exchange has been a popular method for many Internet-based applications, including the Internet of Things (IoT). A traditional PS system consists of publishers, subscribers, and a broker. The publishers create new data for a registered topic, and the data broker relays the data to the corresponding subscribers. This paper introduces a blockchain-based smart broker for the publisher-subscriber (PS) framework for the IoT network. As IoT data comes from devices operating in various environments, it may suffer from multiple challenges, such as hardware failures, connectivity issues, and external vulnerabilities, thereby impacting data quality in terms of accuracy and timeliness. It is important to monitor this data and inform subscribers about its quality. The proposed smart broker is composed of multiple smart contracts that continuously monitor the quality of the topic data by assessing its relationship with other related topics and its drift or delay in publishing intervals. It assigns a reputation score to each topic computed based on its quality and drifts, and it passes both the original data and the reputation score as a measure of quality to the subscriber. Furthermore, the smart broker can suggest substitute topics to subscribers when the requested topic data are unavailable or of very poor quality. The evaluation shows that a smart broker efficiently monitors the reputation of the topic data, and its efficiency increases notably when the data quality is worse. As the broker is run inside the blockchain, it automatically inherits the advantages of the blockchain, and the quality scoring is indisputable based on immutable data.

LF3PFL: A Practical Privacy-Preserving Federated Learning Algorithm Based on Local Federalization Scheme.

In the realm of federated learning (FL), the exchange of model data may inadvertently expose sensitive information of participants, leading to significant privacy concerns. Existing FL privacy-preserving techniques, such as differential privacy (DP) and secure multi-party computing (SMC), though offering viable solutions, face practical challenges including reduced performance and complex implementations. To overcome these hurdles, we propose a novel and pragmatic approach to privacy preservation in FL by employing localized federated updates (LF3PFL) aimed at enhancing the protection of participant data. Furthermore, this research refines the approach by incorporating cross-entropy optimization, carefully fine-tuning measurement, and improving information loss during the model training phase to enhance both model efficacy and data confidentiality. Our approach is theoretically supported and empirically validated through extensive simulations on three public datasets: CIFAR-10, Shakespeare, and MNIST. We evaluate its effectiveness by comparing training accuracy and privacy protection against state-of-the-art techniques. Our experiments, which involve five distinct local models (Simple-CNN, ModerateCNN, Lenet, VGG9, and Resnet18), provide a comprehensive assessment across a variety of scenarios. The results clearly demonstrate that LF3PFL not only maintains competitive training accuracies but also significantly improves privacy preservation, surpassing existing methods in practical applications. This balance between privacy and performance underscores the potential of localized federated updates as a key component in future FL privacy strategies, offering a scalable and effective solution to one of the most pressing challenges in FL.

Model of lnformation Support for a Regional Operator for the Management of Municipal Solid Waste

The purpose of the research is to compile a modern model of information support for a regional operator for the management of municipal solid waste and analyze the requirements for this system.Methods are based on various publications of reports by environmental operators, the public law company "Russian Еcological Operator" (РРK RЕO), on legislative documentation, research and software solutions (SТЕC Housing and Рublic Utilities, Sirena Housing and Рublic Utilities, Еlectronic Тrading Рlatform RЕO) in terms of automation of waste removal.Results. А modern information model for data exchange in the network of a regional operator for the management of solid waste has been built, which meets the needs of the industry. Based on the legislation, the study included recom- mendations for the work and functioning of departments. Ready-made solutions for automating the work of a regional operator for the removal of municipal solid waste are presented. Аn algorithm has been developed for the functioning of the system to send data on the amount of municipal solid waste (MSW) arriving at the landfill in accordance with the new requirements for automatic monitoring of waste accepted at the landfill.Conclusion. Тhe implementation of a modern level of information support for a regional operator for the management of municipal solid waste is relevant, not only from an environmental point of view, the formation of an integrated effective approach to waste management, but also from information security. Organizing the work of a regional operator for the management of municipal solid waste is a process that requires modern information support. Тo carry out the process of building competent logistics, infocommunication kits and data transmission systems are used to track vehicles trans- porting waste. Software is used to monitor the level of vehicle load, the total amount of waste removed, fuel consump- tion, analyze the load and condition of landfills with solid municipal waste, waste separation and recycling processes.

DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces.

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model's acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.

On Designing Market Model and Pricing Mechanisms for IoT Data Exchange

On Designing Market Model and Pricing Mechanisms for IoT Data Exchange

Interoperability performance evaluation for discrete event simulation models: A step towards multi-level data exchange

According to use scenarios, 3D-based virtual commissioning can be divided into three levels: machine, cell and plant. As virtual commissioning at each level focuses on different contents, corresponding applications are developed in different data models. The resulting 3D emulation and simulation models are thus not easily exchangeable or reusable. At machine level, a physics-based model is used to conduct emulation. While at plant level discrete event simulation models are quite common. A framework for exchanging 3D emulation and simulation models between software at different levels would stimulate model reuse and lower the effort for model creation. To date, no interoperability performance evaluation for discrete event simulation models has been presented. This paper investigates industrial standards and related work on discrete event model generation and data exchange. The interoperability performance of discrete event simulation tools is evaluated by the example case of exchanging models between two commercial tools (i.e., FlexSim and Visual Components) via Core Manufacturing Simulation Data and AutomationML. The result shows the former converts 7% more attributes than the latter. Furthermore, an element-based comparison is conducted to reveal overlaps and assess the potential of (partial) model reuse during the data exchange between 3D emulation and simulation models.

A Methodology for Integrating Hierarchical VMAP-Data Structures into an Ontology Using Semantically Represented Analyses

Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.

Massive Data Connection Architecture Design of Web Platform Based on Blockchain

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era.

G-code converter using interface system for a STEP file (ISO 10303)

The STEP file is the “standard for the exchange of product model data,” which is usually used to exchange geometric data in boundary representation (B-rep) between different computer-aided design (CAD) platforms. These data can be fully utilized and integrated into a larger manufacturing organization, such as a computer-aided manufacturing (CAM) environment for computer numerical control (CNC) machining applications based on ISO 6983. The 3D models of 2D machining profiles were created in CAD software and saved as STEP files. The data structure was analyzed by comparing the geometric entities of the CAD model and the STEP file. The algorithm was created using the hypertext preprocessor (PHP) programming language and produced a computer interface system to convert STEP files into G-code format. The machining blocks with profile machining features were simulated using a CNC simulator and PC-based open architecture control (OAC) software. The G-code was validated on a three-axis CNC milling machine, and the result was compared to the CAD model to confirm the machining profile. The integrated interface system I2S demonstrates its ability to interpret all 2D profiles and generate machining tool paths and G-code, allowing data flow between CAD and CAM environments and shortening product development cycles.

THE ROLE OF CLOUD COMPUTING IN MACHINE LEARNING APPROACHES

This paper is presented to explain the intersection of cloud computing (CC) and machine learning (ML), focusing on their synergies, challenges and solutions. It shows the changes in the Internet service area led by cloud computing (CC) and the economic impact of data collection and analysis. The document specifically addresses security issues in distributed models and introduces the concept of edge computing as a version of cloud computing (CC) for time-sensitive data. In this paper, we are discussing about data encryption, distribution of rights, and transfer of data responsibility from service providers to end users. The document breaks down Cloud computing into service and delivery models, addressing security issues related to integrity, availability, and identity threats. It offers machine learning (ML) algorithms as a solution to security and data quality management. With the help of this paper, we are highlights the challenges of integrating Cloud Computing (CC) and machine learning (ML), including data exchange latency, scalability optimization, resource management, data security, model deployment, and monitoring. A resource plan is provided to train organizations in the use of Cloud Computing (CC) and machine learning (ML). The summary ends by highlighting the evolution of Cloud Computing (CC) and machine learning (ML) integration to shape the future of computing and analytics and make organizations more competitive in the digital age.

Milling Features Recognition Methodology of Rectangular Fillet Blind Pocket for Universal Data Structure

Various studies have focused on developing an interface system that could integrate the machining process with CNC (Computer Numerical Control) machine tools by producing tool paths using STEP (Standard for the Exchange of Product model data) file as an input function. Studies to recognize features in STEP file by encoding the data of geometric entities of a CAD (Computer-Aided Design) design has been done by many researchers. However, they only focus on Cutter Location (CL) which eventually describes the machining profile in the x and y planes without considering the depth of the machining feature in the z-axis. Identifying and recognizing machining features in the STEP file is important since STEP is a platform for converting industry-standard CAD systems and software data models. The aim of this paper is to identify profiles of geometric machining features for rectangular fillet blind pockets using the Geometric Data Extraction (GDE) approach. Three cases have been carried out namely single rectangular fillet blind pocket, double rectangular fillet blind pocket and triple rectangular fillet blind pocket. The results were verified by manual graph plotting and automatic graph plotting using CPM 3D Plotter.

A Direct Toolpath Constructive Design Method for Controllable Porous Structure Configuration with a TSP-based Sequence Planning Determination

The inherent capabilities of additive manufacturing (AM) to fabricate porous lattice structures with controllable structural and functional properties have raised interest in the design methods for the production of extremely intricate internal geometries. Current popular methods of porous lattice structure design still follow the traditional flow, which mainly consists of computer-aided design (CAD) model construction, STereoLithography (STL) model conversion, slicing model acquisition, and toolpath configuration, which causes a loss of accuracy and manufacturability uncertainty in AM preparation stages. Moreover, toolpath configuration relies on a knowledge-based approach summarized by expert systems. In this process, geometrical construction information is always ignored when a CAD model is created or constructed. To fully use this geometrical information, avoid accuracy loss and ensure qualified manufacturability of porous lattice structures, this paper proposes a novel toolpath-based constructive design method to directly generate toolpath printing file of parametric and controllable porous lattice structures to facilitate model data exchange during the AM preparation stages. To optimize the laser jumping route between lattice cells, we use a hybrid travelling salesman problem (TSP) solver to determine the laser jumping points on contour scans. Four kinds of laser jumping orders are calculated and compared to select a minimal laser jumping route for sequence planning inside lattice cells. Hence, the proposed method can achieve high-precision lattice printing and avoid computational consumption in model conversion stages from a geometrical view. The optical metallographic images show that the shape accuracy of lattice patterns can be guaranteed. The existence of “grain boundaries” brought about by the multi-contour scanning strategy may lead to different mechanical properties.

GraphSTEP: concurrent, cloud-based, lifecycle-oriented editing and updating of product data

A product's digital representation enables various activities in its lifecycle. In a neutral, file-based exchange format, like the STandard for the Exchange of Product model data (STEP), asynchronous access is possible throughout different software solutions, increasing the work interoperability across different stakeholders. However, the file-based handling of product data from the top to the shop floor and further lifecycle phases, subject to industrial practice, contradicts the Digital Twin (DT) concept embodying a centralized, single source of trust. To address this deficit, we propose GraphSTEP: a cloud-based platform to store, access, and manipulate product data, enabling concurrent, collaborative data management while preserving history through versioning.

A framework for evaluating a generic virtual commissioning data model

The interoperability of different virtual commissioning applications requires a lot of manual work, as they mainly use proprietary file formats. In this case, a generic virtual commissioning data model is highly demanded to realize interoperability among different virtual commissioning applications. Based on this model, the effort of exchanging data between different virtual commissioning applications is significantly reduced. A comprehensive generic virtual commissioning data model contains a variety of virtual commissioning-related information, such as geometry, physics, kinematics, sensors, actuators, and signal connections. For such an elaborate data model, no method is contemporarily indicated to evaluate its interoperability performance. Thus, in this paper, a case-based evaluation framework is introduced to fill in this gap. With this framework, the interoperability performance of a generic virtual commissioning data model can be evaluated by conducting data exchange of several emulation models developed in different virtual commissioning applications, and each of their specific virtual commissioning modeling functions is covered in at least one of these emulation models. As an example, a case study is presented by exchanging 10 carefully selected emulation models between Siemens NX and Visual Components software applications via AutomationML data format. Based on the case study, the applicability of this evaluation framework is analyzed and discussed. Finally, conclusion and outlook are illustrated in the end.

A semantic similarity-based method to support the conversion from EXPRESS to OWL

Abstract Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.

Integrated Interface System of STEP Data Models for the Tool Path Generation

The STEP (STandard for the Exchange of Product model data) is a worldwide standard for exchanging products between diverse computer systems and industrial applications. STEP files (ISO 10303) contain 3D data in the universal data format with a boundary representation (B-rep) that allows the exchange of data models between CAD, CAM, and CNC systems. Traditionally, G-code generation relies on CAM software as an add-on, which incurs significant installation costs. The purpose of this paper is to describe the development of an Integrated Interface System (I2S) for Tool Path Generation (TPG) for the CNC milling operations utilizing neutral STEP files.The algorithm was interpreted in Hypertext Preprocessor (PHP) programming and produced a computer interface system that is capable to convert STEP file geometric data to G-code format.The geometric features of the sample block were modeled using a 3D CAD model. The result was validated using machining simulation, and the machining is performed on a three-axis CNC milling machine with a PC-based controller.In modern manufacturing systems based on Open Architecture Controller (OAC), the development of the I2S provides an alternative to generating machining tool paths and G-code by intercepting CAM software.

An Additive Manufacturing Direct Slicing Algorithm Based on a STEP Model

The Standard Template Library (STL) file is the most common data format for the description of an additive manufacturing (AM) geometric model, but it has some disadvantages, such as large errors of the geometric model description, the easy loss of topology information, data duplication, large file sizes, and so on. Aiming at these problems, a direct slicing algorithm based on a Standard for the Exchange of Product Model Data (STEP) model was proposed. For the parts composed of basic types of surfaces such as boundary curves, spherical surfaces and cylindrical surfaces, the traditional geometric method was used to calculate the intersection. For the parts with complex surfaces, the three-dimensional models were described based on Non-Uniform Rational B-Spline (NURBS) surfaces. The NURBS surfaces were layered using a discrete tracking algorithm, the tracking starting point was determined, the intersection line between the tangent plane and each NURBS sub-surface was obtained, and the closed layer contour was formed. Finally, the slicing simulations and printing experiments of solid parts were carried out using the direct slicing algorithm based on the STEP model. It was shown that the dimensional accuracy and surface quality of the printed parts from this algorithm had been significantly improved.

A blockchain-driven data exchange model in multi-domain IoT with controllability and parallelity

With the extensive application of the Internet of Things (IoT), multi-domain IoT appears as the additional coordination and control ability of IoT. However, the emerging multi-domain IoT suffers from some security challenges, such as insider threats and cross-domain data exchange issues. In this paper, we propose a data exchange model for the multi-domain IoT environment driven by blockchain, addressing expensive cross-domain access control and low data exchange throughput issues. At the core of this model is a chaincode-based cross-domain access control scheme and a domain-as-a-shard (DaaS) high parallel throughput optimization technology. In particular, the access control scheme is maintained by multiple blockchain nodes to ensure access control strategy distributed storage and data cross-domain controllability. The data in this model cannot be exchanged across domains until all nodes jointly verify that the access control strategy is valid. Moreover, the throughput optimization technology sets the IoT domain as the blockchain shard to process data exchange in parallel and scale up the throughput dramatically. In each shard, data exchange is processed in the form of the blockchain transaction independently to improve the transaction throughput of our model. Finally, security analysis proves that our model successfully ensures access control strategy non-repudiation and cross-domain data controllability. Extensive experiments on Hyperledger Fabric show that the transaction throughput of our model is nearly three times that of the original Fabric v1.4.

Interoperability in AECO and the oil & gas sectors: object-based standards and systems

This research proposes recommendations that could improve interoperability in the architecture, engineering, construction and operations (AECO) sector, by connecting domains, building lifecycles, and software systems with each other and the web. The objective has been to identify methods that promote evolution from file-based formats by advancing object-based data exchange solutions. The research design is a mapping of standards and systems that have affected the nature of object-based data exchanges, and which have either been proposed or implemented in AECO and the Oil & Gas sector. This is an approach which allows for the range and diversity of information to be examined. A review of the Oil & Gas sector confirms a norm where object-based, rather than file-based, transactions have shaped data exchange models, formats, use case methodologies, and collaboration mechanisms, thus contributing towards semantic connectivity across its diverse systems. Key research questions address the nature of these sectors, the promise that object-based data exchange offers, and examine recommendations that would improve standards and systems. The paper affirms that measures taken to improve interoperability in the Oil & Gas sector have relevance for the AECO sector, and that better understanding, and recognition of the structure of AECOs interoperability ecosystem is central to effecting lasting and significant change. Thus, we make recommendations that acknowledge the hybrid nature of AECO data exchanges and propose an interoperability ecosystem that connects both distributed and centralised federated models. Improved standards to define application programming interfaces (APIs) and adaptors, based on a modular approach, would be central to this proposal. We also make recommendations to improve use case definitions, and to ensure that semantic connectivity at the object level is scalable to web-based transactions. Finally, we assert that, to realise these changes, the developers and vendors of its systems should recognise and address the AECO sector’s pressing needs and concerns.