Model-based System Research Articles

System integration based on packing, piping and harness routing automation using graph-based design languages

The implementation of a fully instrumented, automated and simulation-enabled engineering software platform capable of automating the currently still manual model-based systems engineering (MBSE) design process for physical systems architecture generation and optimization in an aircraft wing is presented. The software platform uses graph-based design languages to integrate and entirely automate the mainly manual packing, piping and harness routing design. This design automation and optimization is achieved by a novel software stack of an optimization software coupled with a design compiler. It is shown that through rule-based model generation by a design compiler in the form of a design graph as a central data model, a cross-domain data consistency is achieved. This allows for automated execution and coupling of engineering tasks over several different domains such as packing, piping and routing design to converge to an optimized wing physical architecture design variant in agreement with given predetermined design constraints.

Model-Based Systems Engineering of the Aft Collision Assist Advanced Driver Assistance System

Model-Based Systems Engineering of the Aft Collision Assist Advanced Driver Assistance System

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

Integration of the Cyber–Physical System (CPS) concept with bio-analytical devices is highly desirable to enable device automation as well as to improve diagnostic and analytical capabilities. However, the modeling must account for entity interactions, system dynamics, and non-functional aspects required for proper device functionality.This paper presents a model-based system architecture that builds upon an extended timed automata-based formal technique. In contrast to prior works that utilized SysML or UML-based models, this allows for the wireless control of bio-analytical instruments. Using this formal method, the UPPAAL tool is used to model and test a case study called “A droplet flow cytometer for testing bacteria’s susceptibility to antibiotics”. The study shows the implications of formal techniques for the design and verification of wireless automation of high-throughput laboratory setups in Model-Based System Engineering (MBSE). Moreover, the paper extends the above aspects by adding the possibility to model multi-system interaction. This is used to analyze the trade-off between centralized and decentralized information flow strategies for better system performance under delay and bandwidth constraints. UPPAAL Stratego is used to analyze strategies for achieving specific delays and bandwidth consumption while avoiding packet losses in the event of network congestion. The results show that under strict delay constraints and high traffic, the use-case system selects the decentralized strategy over the centralized strategy. In low-traffic scenarios, the centralized strategy is more effective at ensuring the reliable operation of systems.

Iterative Nonlinear Fuzzy Modeling of Lithium-Ion Batteries

Electric vehicles (EVs), in their pure and hybrid variants, have become the main alternative to ensure the decarbonization of the current vehicle fleet. Due to its excellent performance, EV technology is closely linked to lithium-ion battery (LIB) technology. A LIB is a complex dynamic system with extraordinary nonlinear behavior defined by electrical, thermal and electrochemical dynamics. To ensure the proper management of a LIB in such demanding applications as EVs, it is crucial to have an accurate mathematical model that can adequately predict its dynamic behavior. Furthermore, this model must be able to iteratively adapt its parameters to accommodate system disturbances during its operation as well as performance loss in terms of efficiency and nominal capacity during its life cycle. To this end, a methodology that employs the extended Kalman filter to iteratively improve a fuzzy model applied to a real LIB is presented in this paper. This algorithm allows to improve the classical Takagi–Sugeno fuzzy model (TSFM) with each new set of data obtained, adapting the model to the variations of the battery characteristics throughout its operating cycle. Data for modeling and subsequent validation were collected during experimental tests on a real LIB under EVs driving cycle conditions according to the “worldwide harmonised light vehicle test procedure” (WLTP) standard. The TSFM results allow the creation of an accurate nonlinear dynamic model of the LIB, even under fluctuating operating conditions, demonstrating its suitability for modeling and design of model-based control systems for LIBs used in EVs applications.

Proselfs depend more on model-based than model-free learning in a non-social probabilistic state-transition task

Humans form complex societies in which we routinely engage in social decision-making regarding the allocation of resources among ourselves and others. One dimension that characterizes social decision-making in particular is whether to prioritize self-interest or respect for others—proself or prosocial. What causes this individual difference in social value orientation? Recent developments in the social dual-process theory argue that social decision-making is characterized by its underlying domain-general learning systems: the model-free and model-based systems. In line with this “learning” approach, we propose and experimentally test the hypothesis that differences in social preferences stem from which learning system is dominant in an individual. Here, we used a non-social state transition task that allowed us to assess the balance between model-free/model-based learning and investigate its relation to the social value orientations. The results showed that proselfs depended more on model-based learning, whereas prosocials depended more on model-free learning. Reward amount and reaction time analyses showed that proselfs learned the task structure earlier in the session than prosocials, reflecting their difference in model-based/model-free learning dependence. These findings support the learning hypothesis on what makes differences in social preferences and have implications for understanding the mechanisms of prosocial behavior.

Model-Based Systems Engineering Cybersecurity for Space Systems

As industries in various sectors increasingly adopt model-based systems engineering (MBSE) for system lifecycle design and development, engineers can manage and describe systems of higher complexity than ever before. This is especially true for the field of space systems; while past missions have developed using document-based planning, it is only in the last several years that NASA and other organizations in the space industry have begun using MBSE. One crucial factor of space systems development that is often overlooked is cybersecurity. As space systems become more complex and cyberphysical in nature, cybersecurity requirements become more difficult to capture, especially through document-based methods; a need for a means by which to continuously verify and validate systems cybersecurity for cyberphysical space missions arises. By expanding upon a National Institute of Standards and Technology (NIST) framework for cyber resiliency, this work proposes a methodology that uses MBSE traceability functionality to demonstrate adequate cybersecurity for cyberphysical space systems using SysML requirements modeling capabilities. Key goals, objectives, and strategic principles leading to achieving cybersecurity at all levels of the system’s architectural hierarchy are presented. Recommendations for the future of space cybersecurity include the addition of the space sector to the Department of Homeland Security Cybersecurity & Infrastructure Security Agency’s list of critical infrastructure sectors to improve standardization and control of space cyberinfrastructure.

DynamicRetriever: A Pre-trained Model-based IR System Without an Explicit Index

Web search provides a promising way for people to obtain information and has been extensively studied. With the surge of deep learning and large-scale pre-training techniques, various neural information retrieval models are proposed, and they have demonstrated the power for improving search (especially, the ranking) quality. All these existing search methods follow a common paradigm, i.e., index-retrieve-rerank, where they first build an index of all documents based on document terms (i.e., sparse inverted index) or representation vectors (i.e., dense vector index), then retrieve and rerank retrieved documents based on the similarity between the query and documents via ranking models. In this paper, we explore a new paradigm of information retrieval without an explicit index but only with a pre-trained model. Instead, all of the knowledge of the documents is encoded into model parameters, which can be regarded as a differentiable indexer and optimized in an end-to-end manner. Specifically, we propose a pre-trained model-based information retrieval (IR) system called DynamicRetriever, which directly returns document identifiers for a given query. Under such a framework, we implement two variants to explore how to train the model from scratch and how to combine the advantages of dense retrieval models. Compared with existing search methods, the model-based IR system parameterizes the traditional static index with a pre-training model, which converts the document semantic mapping into a dynamic and updatable process. Extensive experiments conducted on the public search benchmark Microsoft machine reading comprehension (MS MARCO) verify the effectiveness and potential of our proposed new paradigm for information retrieval.

Proposing a Small-Scale Digital Twin Implementation Framework for Manufacturing from a Systems Perspective

Due to the fourth industrial revolution, manufacturing companies are looking to implement digital twins in their factories to be more competitive. However, the implementation of digital twins in manufacturing systems is a complex task. Factories need a framework that can guide them in the development of digital twins. Hence, this article proposes a small-scale digital twin implementation framework for manufacturing systems. To build this framework, the authors gathered several concepts from the literature and designed a digital twin subsystem model using a model-based systems engineering (MBSE) approach and the systems engineering “Vee” model. The systems modelling defines the digital twin components, functionalities, and structure. The authors distribute most of these concepts throughout the framework configuration and some concepts next to this general configuration. This configuration presents three spaces: physical, virtual, and information. The physical space presents a physical layer and a perception layer. The information space has a single layer called middleware. Finally, the virtual space presents two layers: application and model. In addition to these layers, this framework includes other concepts such as digital thread, data, ontology, and enabling technologies. This framework could help researchers and practitioners to learn more about digital twins and apply it to different domains.

Measurement and Model-Based Control of Solidification in Continuous Casting of Steel Billets

A laser vibrometer system in combination with appropriate artificial intelligence methods for clustering of the measured vibration spectra was tested at a continuous steel casting machine to receive information on the solidification status of the strand. Measurements with the laser vibrometer at a fixed strand position of the billet caster of ESF under conditions of incrementally increasing casting speeds revealed a transition in the population of the identified vibration clusters as a footprint of the passed crater end position with a change from a fully solidified strand to a strand with some liquid core at the measurement position. This was in accordance with the results from a three-dimensional dynamic temperature and solidification model which was set up based on a state-of-the-art approach for solution of the heat flow equation with tailored submodels for the different boundary zones of the ESF billet caster (i.e., mould, secondary spray water zones and radiation zones) and installed at the steel plant for online monitoring and control of the casting process. The application of the newly installed measurement and model-based information systems at ESF revealed significant improvements in their billet casting process in terms of halved strand breakout rates and correspondingly increased productivity.

A probe-based demand responsive signal control for isolated intersections under mixed traffic conditions

ABSTRACT The paper presents a model-based demand-responsive traffic control system for mixed traffic conditions using sample travel time data. The model incorporates mixed traffic characteristics such as heterogeneity, limited lane discipline of varied vehicle types, and spatio-temporal traffic dynamics across the width of the road. The methodology includes optimization of intersection performance by accommodating the varying traffic demand through signal timing variables. On validation, the model yielded reliable queue estimates within a close proximity of the actual, ranging from 20 to 40 meters. Upon optimization, the proposed model reduced total intersection delay by 15.42% on an average across 14 cycles, for near-saturated traffic conditions. The optimal green splits are found to be responsive to the varying traffic demand. The proposed system is simple and can be easily implemented in the mixed traffic conditions.

Enabling Cross-Domain Modeling of Complex Autonomous Vehicles in System-of-Systems Architectures: A Model-Based System Specification for the Development of Complex Automotive Architectures

Enabling Cross-Domain Modeling of Complex Autonomous Vehicles in System-of-Systems Architectures: A Model-Based System Specification for the Development of Complex Automotive Architectures

Practice and Enlightenment of China's Aerospace Complex System Management in the National System

Aerospace engineering is characterized by its huge scale, technological complexity, high standards of quality and reliability, tremendous cost, and long R&D cycle. It is closely tied with national politics and defense security, and may indirectly provide socioeconomic benefits. To organize and implement a complex aerospace project, it is necessary to make innovations in technologies, schedules, qualities, and facilit ies construction management measures and establish an aerospace engineering management system adaptive to the high-complexity feature, based on the management models of the traditional aerospace system engineering. Different types of participants play different functions in project decision-making. The expert group participates in the whole process of the formulation and decision-making of the schemes, providing decision support for each system and the general department. This study describes the implications for the space domain of China’s deployment of the state-owned enterprises (SOEs) as power projection vehicles to advance Beijing’s national strategy. In order to provide reference for the application of model-based systems engineering (MBSE) in the aerospace field, suggestions and conclusions on the development of MBSE application in China were provided.

Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient exploreexploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of modelbased planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MIHPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.

Closed-Loop Dynamic Blending Optimization Based on Variational Bayesian and its Application in Industry

There exist uncertainties in raw material components and distribution parameters in blending process. Considering the dynamic uncertainty of distribution parameters and the dynamic change of raw material inventory, a general closed-loop dynamic blending optimization (CDBO) is proposed. The proposed method consists of both data-driven system and model-based system. In the data-driven system, the feedback optimization problem is reconstructed into a linear regression problem, and the variational Bayesian is proposed to obtain the mean and variance of the distribution. Then, according to the incoming raw materials, the mean value of each distribution parameter is adjusted by expert rules. Last, in the model-based system, blending ratio is obtained by chance-constrained programming. To verify the effectiveness of CDBO, a detailed derivation process of variable Bayesian, expert rule and chance-constrained programming are established for zinc smelting process. Numerical studies and an industrial application for zinc smelting process are presented to demonstrate the advantages of the proposed method. Compared with the manual blending, the volatility index of chemical tests data is greatly reduced and the compliance rates of zinc and lead is increased by 6.7% and 3.3%, respectively.

Human-Centric Co-Design of Model-Based System Architecture

Model-Based Systems Engineers, particularly those moving from software to systems engineering, claim that SysML-like notations, which are symbolic two-dimensional diagrams made of boxes and lines, are domain-independent, thus very convenient to support the cross-functional design of a system architecture. However, the abstract diagramming syntax of MBSE notations makes their adoption difficult, especially by notational nonexperts, and using iconic graphics is one way of improvement. Very few studies attempted to replace 2D diagrams with immersive 3D visuals without objective evidence. We assume that it is due to a lack of quality criteria to compare 2D diagrams with 3D visuals. This paper will argue that human-centric 3D visuals should replace MBSE diagrams where appropriate. A new human-centric MBSE modelling environment motivates the adoption of interactive and immersive 3D visuals to facilitate communication and participation in multidisciplinary co-design activities from mission to concept definition. Our claim is not supported by measured evidence, but a functional demonstrator provides observed evidence and, compared to existing studies, we discuss our proposal based on the Physics of Notation. Future studies will concentrate on the modelling of the behavioural and structural views and the definition of criteria to validate the human-centric MBSE modelling environment.

Design of intelligent decision support system for supply chain sustainability assessment

The objective of this research is to develop an intelligent decision support system (IDSS) that can aid decision-makers in enhancing the performance and sustainability of the sugarcane agroindustry's supply chain. A case study focusing on a sugarcane agroindustry supply chain was proposed. The IDSS was developed using the waterfall approach, which follows the system development life cycle (SDLC) and employed an object-oriented programming technique. The outcome of this study is a prototype of the IDSS, consisting of a database, model base, and knowledge-based management system configuration. To evaluate the sustainability of the supply chain, a fuzzy inference system and an adaptive fuzzy inference system algorithm were utilized. Through verification and validation, it was determined that the IDSS prototype has the potential to be implemented in real-world scenarios, as long as certain assumptions related to the model are taken into consideration.

Enhanced electrocoagulation process for natural organic matter removal from surface drinking water sources: coagulant dose control & organic matter characteristics

An optimized electrocoagulation process was investigated for enhanced removal of dissolved organic matter for eco-friendly drinking water production from surface water sources. A model-based control system was developed for electro-coagulant dosing.

Targeted-oriented selection of engineering methods

This paper presents the development of a web-based method portal to enable target groups a suitable selection of engineering methods concerning (Model-Based) Systems Engineering. An overview of the current state of research and the benefits of using method descriptions as well as providing support for the selection of methods is given. In order to gain acceptance for engineering methods among employees, individual needs must be addressed. To achieve this, the target group orientation was taken into focus during the development of the method portal. The special characteristic of the method portal was the methodology of the industry-oriented creation which is explained in detail in the paper.

A model-based early warning system for runoff-generated debris-flow occurrence: Preliminary results

Early warning systems for debris flows are low cost measures for mitigating this kind of hazard. The early warning systems provide a timely alert for upcoming events in order to take protective measures, such as closing railways-roads, evacuating people from the threatened areas, and put rescue forces into readiness. These systems usually are sensor-based, and the alert time is the interval between the timing of the first detachment of debris flow by a sensor and its arrival into the threatened area. At the purpose of increasing the alert time, we propose an early warning system based on a model-cascade: nowcasting, hydrological- and triggering models. Nowcasting anticipates rainfall pattern that is transformed into runoff by the hydrological model. The triggering model estimates the volume of sediments that the runoff can entrain, and compares it with a critical threshold. If this is exceeded the alert is launched. The proposed early warning system is tested against the available data of the Rovina di Cancia (Northeast Italy) site.

A Novel Telecom Customer Churn Analysis System Based on RFM Model and Feature Importance Ranking

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.