Model-based System Research Articles

A Model-Based Engineering Approach for Evaluating Software-Defined Radio Architecture

In product development, important specification and design decisions must be made at various stages of the lifecycle that include design, manufacturing, operations, and support. However, making these decisions becomes more complex when a multi-disciplinary team of stakeholders is involved in system-level or subsystem-level architecture and design decisions. Model-Based Engineering (MBE) approaches are enabling a digital thread of connected data and models. This work demonstrates a novel MBE approach that incorporates a model-based systems engineering (MBSE) method and a multi-criteria decision-making (MCDM) method to determine the best architecture solution that aligns with stakeholder needs and objectives over multiple domains. This approach demonstrates the connection of a system descriptive model, modeled using the systems modeling language (SysML), to underlying physics-based engineering models for the purpose of better predicting the technical performance of systems during the architecture development phase. This approach is demonstrated for a common aerospace communications application, a software-defined radio. This novel MBE approach supports digital transformation at organizations and allows for earlier design validation, enabling designers to test and select the best system architecture from many candidates and validate that the design meets stakeholder needs.

The task-oriented Smart Tourism Chatbot Service

The smart tourism service provides tourists with a travel planner service before the trip and a tour guide service during the trip. Smart tourism chatbot service can easily and conveniently provide smart tourism services to tourists along with smart tourism apps. In this paper, we develop the smart tourism platform and propose the task-oriented smart tourism chatbot service that efficiently provides tourism information provided by smart tourism apps to users. The smart tourism platform consists of the smart tourism chatbot and smart tourism information systems. The smart tourism chatbot system identifies the intention of the user's question and searches for the tourism information ID in the tourism information knowledgebase. The smart tourism information system provides tourism information of the smart tourism app to the user using the searched tourism information ID. The proposed smart tourism chatbot system applies the tourism information NER (Named Entity Recognition) model to accurately understand the intention of the user's question to the existing DST (Dialogue State Tracking) model-based chatbot system. We create tourist information named entities of the NER model based on the values of the domain and slot in the DST model defined in the 4W1H method. The NER model determines the domain, slot, and value to identify the intention of the user's question, and the DST model manages the dialogue state and retrieves the tourism information ID from the tourism information knowledgebase.

A reference architecture for the American Multi-Modal Energy System enterprise

The American Multimodal Energy System (AMES) is a system-of-systems comprised of four separate but interdependent infrastructure enterprises: the electric grid, the natural gas enterprise, the oil enterprise, and the coal enterprise. Their interdependence creates the need to better understand the underlying architecture in order to move towards a more sustainable, resilient and accessible energy system. Collectively, these requirements necessitate a sustainable energy transition that constitute a change in the AMES instantiated architecture; although it leaves its reference architecture largely unchanged. Consequently, from a model-based systems engineering perspective, identifying the underlying enterprise model’s reference architecture becomes a high priority. This paper defines a reference architecture for the AMES and its four component energy enterprises in a single SysML model. The architecture includes (allocated) block definition and activity diagrams for each enterprise. The reference architecture was developed from the S&P Global Platts (GIS) Map Data Pro data set and the EIA Annual Energy Outlook dataset. This reference architecture serves as the foundation from which to accurately and consistently create mathematical and informatic digital twins of the AMES.

Identification of Barriers of Sustainable Consumption in Vietnamese Fashion Industry: A Data - Driven Model - Based Grey Theory System

The fashion industry plays a significant role in global environmental degradation and social inequality due to its unsustainable consumption patterns. Therefore, understanding the barriers hindering the adoption of sustainable consumption practices in this industry is crucial for promoting positive change. This research aims to identify and analyze the barriers to sustainable consumption in the Vietnamese fashion industry using a two-stage data-driven model-based Grey theory system (Grey Delphi- Grey DEMATEL). To achieve this objective, a comprehensive literature review was conducted to identify the key barriers affecting sustainable consumption in the fashion industry. The critical barriers include social and cultural perspective, pricing and affordability, policy and education, perception and image, and supply chain and industry practices by developing a structured questionnaire that was distributed to 16 valid industry experts and professionals in Vietnam. The Grey Delphi technique is applied to validate 30 critical barriers to sustainable consumption in the Vietnamese fashion industry. The Grey DEMATEL model is employed to determine the causal relationships among selected barriers, considering both the direct and indirect influences. Additionally, the Grey theory system with Grey numbers was integrated into the model to address uncertainties and vagueness associated with the data. By employing the proposed model, the research provides a comprehensive understanding of the complex relationships between the identified factors and their impact on sustainable consumption. The outcomes of this research will enable industry stakeholders, policymakers, and researchers to develop targeted strategies and interventions to overcome these barriers and promote sustainable practices in the Vietnamese fashion industry. Keywords: Sustainable consumption, barriers, fashion industry, Vietnamese, DEMATEL, Grey theory system.

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System.

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

Architecting a Digital Twin-Based Predictive Maintenance System for Modelling Cable Joint Degradation

The large scale adoption of wind turbines and solar panels in the Netherlands places new demands on the medium voltage power grid. For example, highly varying loads can cause failures in certain cables. Cable joints are natural weak spots prone to faults due to varying currents, creating downtime challenges for public utility companies. Predictive maintenance (PdM) practices are necessary to minimize downtime for users. We present a Model-Based System Engineering approach using formal models and UML views to provide a scalable PdM design ontology for modeling cable joint degradation. We aim to monitor cable joint degradation from different manufacturers under varying conditions throughout the Netherlands in real-time using a Digital Twin (DT) approach. Our design provides high-resolution, real-time synchronization between the DT-based PdM system and the cable joints. The proposed architecture is scalable, robust, and flexible, and the software implementation is publicly available in an open-source repository.

A Model-Based Approach for the Methodical Development and Configuration of Modular Product Families

This paper shows how a methodical development and configuration of modular product family concepts and their effects on economic targets can be implemented in SysML. For this purpose, different sources of inconsistency between different methodical tools are highlighted and the need for research is shown. As a solution approach, a methodical framework is presented, which can be used to implement product development methods for the developing of modular product family modeling by means of Model-Based Systems Engineering (MBSE) in the modeling language SysML. By applying the framework, it is shown on the one hand how a product family of vacuum cleaner robots as a simple example can be modularized in a methodical, model-based manner. On the other hand, a configuration system and an impact model of modular product families are connected with the system model and applied to a product family of laser systems as an industrial use case. This made it clear that the framework can be used to model various methodical topics of product family modeling in a consistent manner, to enable higher-level analyses with the use of MBSE tools. This can reduce errors, decrease effort and increase traceability across different methodical tools.

Assessment of marine ranching ecological development using DPSIR-TOPSIS and obstacle degree analysis: A case study of Zhoushan

The depletion of fishery resources and degradation of marine ecology have aroused global concerns. Governments worldwide have been exploring ways to construct marine ranches for optimizing the marine ecological environment and maintaining marine biological resources. As an ecological project integrating human and natural environment, marine ranching ecological development (MRED) can indicate a dynamic process of applying modern marine technology, promoting the sustainable development of marine ecology and improving fishery resources. In this study, a DPSIR model-based MRED index system is developed, and a MRED assessment method is proposed using the entropy-weighted-TOPSIS model and the Obstacle degree model. The above-mentioned methods are adopted to investigate the national marine ranching located in Zhoushan. As indicated by the findings of this study, the MRED level in Zhoushan has been improve steadily from 2019 to 2022, with general upward trends identified in all primary indexes, whereas some fluctuations have been present. In general, the impediments to MRED in Zhoushan arise from seawater pollution, natural disasters, and inadequate scientific research support. Policy support and resource investment have taken on significance role in providing strong support. This study can lay a scientific basis for the habitat construction of marine ranching, and has important reference value for systematically improving the general development level of Zhoushan marine ranching.

A system modeling process based on SysML to support data consistency across system requirement, function, and solution model layers

Model-Based Systems Engineering (MBSE) is a popular system engineering development method focusing on digital models as the primary mean of information exchange to improve communication across disciplines. To implement MBSE, the system model needs to be created to represent systems at different layers of abstraction, such as requirements, functional architectures, and solution designs throughout the development phase. Although many studies have mentioned the need to pay attention to the traceability between different layers of system models, there is still a lack of an automated system modeling process. This can lead to inefficiencies and even errors during the system modeling process. A semi-automated system modeling process based on Systems Modeling Language (SysML) is presented to overcome the issue. This work elaborates on a formalised modeling mechanism based on SysML elements to aid system engineers in system modeling with a function-oriented architecture. This work demonstrates and evaluates the proposed process by developing a wind turbine (WT) system model. The contribution of this study is that it realizes the semi-automatic top-down system modeling process. This ensures data inheritance and consistency between different system model layers of abstraction. It also reduces manual efforts and improves the efficiency of the system modeling process.

Using the ARCADIA/Capella Systems Engineering Method and Tool to Design Manufacturing Systems—Case Study and Industrial Feedback

In a trend towards digital continuity, model-based systems engineering is becoming widely adopted for the design of complex systems, supporting system development from the very first stages. A narrow panel of methods and tools are available on the market; they offer different scopes and approaches, are more or less intuitive to follow, and are sometimes supported by tools. Among them, the Architecture Analysis & Design Integrated Approach (ARCADIA) is becoming popular and is gradually spreading in different industrial fields to model a wide variety of systems at different stages of their development and from different points of view. It is implemented using an open-source tool called Capella. Few feedback on its use in industrial settings have been published, while other feedback remains confidential. The goal of this paper is to analyze the interests and limitations of ARCADIA/Capella. To reach this goal, we experimented with ARCADIA/Capella in several projects and chose one to explain how the method and tool proceeded. In addition, we conducted a survey to obtain industrial feedback. As a result, the paper gives an overview of the relevance of ARCADIA/Capella in projects and of its usefulness, effectiveness, and adaptability in modeling different types of systems. It also provides some perspectives for the evolution of the method and the tool according to industrial feedback.

AFall: Wi-Fi-Based Device-Free Fall Detection System Using Spatial Angle of Arrival

Falling is a common health problem for elderly people. Early detection of falls allows earlier rescue measures to be implemented. Most existing Wi-Fi-based fall detection systems employ learning-based methods, which require large amounts of labeled data for prior training. To address this issue, we in this paper present AFall, a robust model-based fall detection system that does not require prior training for a single person based on Wi-Fi Channel State Information (CSI). Different from previous Wi-Fi-based fall detection systems, we model the relationship between human falls and changes of Angle of Arrival (AoA) of Wi-Fi signals reflected from human body by multiple signal classification (MUSIC) algorithm. In particular, we deploy two receivers in orthogonal spatial layouts to capture diversified AoA information. Since AoA reflected from human body is independent of environments and subjects, the performance of AFall can remain stable when the environment changes slightly, which can meet the daily needs of the elderly people. We implement AFall using commodity Wi-Fi devices and evaluate it in five different indoor environments. The experimental results demonstrate that AFall achieves an average accuracy of 84.31% and an average F1 score of 84.56%.

TOWARDS THE INTEGRATED ADOPTION OF BLM AND DT IN AECO-FM SECTOR: A SCIENTOMETRIC APPROACH

The Architecture, Engineering, Construction, Operation and Facility Management (AECO-FM) industry presents a well-known discrepancy in terms of productivity and digitalization compared with other sectors. The introduction of building lifecycle management (BLM) could lead to several benefits covering all the phases of the building lifecycle (BLC) and enabling the implementation of a model-based integrated management system. Nevertheless, its adoption is far from being spread across the construction field and the case studies in the existing literature present several criticalities related to the adopted technologies and policies which hinder the complete development of a BLM system. To overcome these obstacles, digital twins (DTs) could be exploited to enhance the collaboration among actors, preserving the integrity of data, and offering a multidisciplinary environment to carry out realistic simulations. The purpose of this research is to conduct a scientometric analysis to evaluate the relationships between the topics of BLM and DT in the AECO-FM sector. The most common key studies and patterns are identified by processing bibliometric metadata. The most investigated topics related to BLM and DT are system design, system management, and Building Information Modeling (BIM) applications. The analysis reveals a gap regarding the connection between BLM and DT, identifying a potential research area that is currently unexplored in the academic field. The integrated adoption of these topics could contribute to promoting a holistic approach in the built environment by preserving data consistency, reducing information loss between design and construction phases, and strengthening connectivity among the actors involved in the process.

Model-Based System Engineering Framework for Superconducting Accelerator Magnet Design

The design specification of future high-field accelerator magnets require innovation in and integration of multiple disciplines. Numerical models underpin each step of the design. In this paper, we present a methodology for collaborative modeling based on relevant concepts of model-based systems engineering. The methodology is composed of three pillars: encapsulated computing environments with service query interface, model notebooks with auto-generated model views, and model query interface with results caching. The methodology aims at a de-centralized approach to multi-model and multi-scale collaboration that maintains the cornerstones of traceability and reproducibility. It is demonstrated with a multi-model coil design optimization of a high-field superconducting magnet.

The influence of aerobic exercise on model-based decision making in women with posttraumatic stress disorder

Individuals with PTSD often exhibit deficits in executive functioning. An unexplored aspect of neurocognitive functions associated with PTSD is the type of learning system engaged in during decision-making. A model-free (MF) system is habitual in nature and involves trial-and-error learning that is often updated based on the most recent experience (e.g., repeat action if rewarded). A model-based (MB) system is goal-directed in nature and involves the development of an abstract representation of the environment to facilitate decisions (e.g., choose sequence of actions according to current contextual state and predicted outcomes). The existing neurocognitive literature on PTSD suggests the hypothesis of greater reliance on MF vs MB learning strategies when navigating their environment. While MF systems may be more cognitively efficient, they do not afford flexibility when making prospective predictions about likely outcomes of different decision-tree branches. Emerging research suggests that an acute bout of aerobic exercise improves certain aspects of neurocognition, and thereby could promote the utilization of MB over MF systems during decision making, although prior research has not yet tested this hypothesis. Accordingly, the current study administered a lab-based two-stage Markov decision-making task capable of discriminating MF vs MB decision making, in order to determine if moderate-intensity aerobic exercise (either shortly after or 30-minutes after the exercise bout has ended) promotes greater engagement in MB behavioral strategies compared to light-intensity aerobic exercise in adult women with and without PTSD (N = 61). Results revealed that control women generally displayed higher levels of MB behavior that was further increased following immediate exercise, particularly moderate-intensity exercise. By contrast, the PTSD group generally displayed lower levels of MB behavior, and exhibited greater MB behavior when completing the task following moderate-intensity aerobic exercise compared to light-intensity aerobic exercise regardless of whether there was a short or long delay between exercise and the task. Additionally, women with PTSD demonstrated less impairment in MB decision-making compared to controls following moderate-intensity aerobic exercise. These results suggest that an acute bout of moderate-intensity aerobic exercise boosts MB behavior in women with PTSD, and suggests that aerobic exercise may play an important role in enhancing cognitive outcomes for PTSD.

Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

Model-Based ISO 14971 Risk Management of EEG-Based Medical Devices.

Risk management (RM) is a key component of the development of modern medical devices (MD) to achieve acceptable functional safety and pass the regulatory process. The emerging availability of various techniques, languages, and tools that use model-based system engineering (MBSE) promises to facilitate the development and analysis of complex MD. In this paper, we show how to integrate RM principles and activities recommended in ISO 14971 medical standard into an MBSE-driven MD development process. We propose a method and framework capable of modeling essential RM concepts and performing RM and safety analysis in the early stages of the MD development life cycle. The framework extends OMG RAAML (Object Management Group Risk Analysis and Assessment Modeling Language) to the medical domain according to ISO 14971. We illustrate our approach using a case study of the e-Glass system developed for real-time EEG-based subject monitoring with the intended use of stress monitoring.Clinical Relevance-This facilitates the MD certification process by semi-automation of RM based on ISO 14971 and obtaining safe MD by design.

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

Design of a perceptron model-based physical fitness index monitoring system for sports training

Abstract The purpose of designing a sports training fitness index monitoring system is to grasp better the physical performance data of athletes in the training process to ensure training safety. In this paper, the principle of multilayer perceptron is explained based on the perceptron model, the optimal loss function of multilayer perceptron is solved by using the activation function and forward propagation algorithm, the sensor data collection module is constructed, and the physical fitness index monitoring system for sports training is built by this method. To verify the feasibility of the detection system in this paper, experimental analysis was conducted from three aspects: the distribution of physical fitness index monitoring information density, physical fitness index data and monitoring data accuracy. The index monitoring density distribution was between 0.11 and 2.09 from the monitoring information density. Regarding physical performance indicators, the average values of maximum oxygen uptake, heart rate, relative energy metabolism level, and exercise intensity were 41.02, 121.58, and 11.84, respectively. From the accuracy of indicator monitoring data, the accuracy of the system in this paper was 93.63%, which was 21.57 and 11.03 percentage points higher than that of GAN and MCNN algorithms, respectively. The physical fitness index monitoring system constructed based on the perceptron model can effectively realize the monitoring of physical fitness indexes, help trainers master the training rhythm, and improve the safety of sports training.

An Evolutionary Fake News Detection Based on Tropical Convolutional Neural Networks (TCNNs) Approach

In general, the characteristics of false news are difficult to distinguish from those of legitimate news. Even if it is wrong, people can make money by spreading false information. A long time ago, there were fake news stories, including the one about "Bat-men on the moon" in 1835. A mechanism for fact-checking statements must be put in place, particularly those that garner thousands of views and likes before being refuted and proven false by reputable sources. Many machine learning algorithms have been used to precisely categorize and identify fake news. In this experiment, an ML classifier was employed to distinguish between fake and real news. In this study, we present a Tropical Convolutional Neural Networks (TCNNs) model-based false news identification system. Convolutional neural networks (CNNs), Gradient Boost, long short-term memory (LSTMs), Random Forest, Decision Tree (DT), Ada Boost, and attention mechanisms are just a few of the cutting-edge techniques that are compared in our study. Furthermore, because tropical convolution operators are fundamentally nonlinear operators, we anticipate that TCNNs will be better at nonlinear fitting than traditional CNN. Our analysis leads us to the conclusion that the Tropical Convolutional Neural Networks (TCNNs) model with attention mechanism has the maximum accuracy of 98.93%. The findings demonstrate that TCNN can outperform regular convolutional neural network (CNN) layers in terms of expressive capability.

Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).