Graphical Modeling Tool Research Articles

Discrete Event System Decision: an Approach to the Maintenance Based on Colored Petri Net

Industrial processes, over the years, need to be modernized to reduce costs and maintain product quality. In this context, new tools or techniques have been developed for such an event to further improve industrial processes and the quality of the manufactured product. This paper refers to the application of a graphical and mathematical modeling tool, in this case, the Colored Petri Net (CPN), applied to a corrective maintenance process of a conical machine in the textile industry. That is, thecorrective maintenance process is modeled as a discrete event system for decision-making to identify which task must be performed to perform corrective maintenance. It also presents the operation of CPN for the described process, the general concepts of maintenance applied to the textile industry, the theory about PN, as well as the modeling of the process. The results show the mathematical model of the PN and the CPN graph, modeled for the maintenance process, which will facilitate the understanding of the maintenance process, as well as solve future problems more quickly and with quality. Finally, it is concluded that the model carried out can be used as a decision-making system to facilitate the corrective maintenance process.

Assessing the risk of using hazardous drugs in Hospital-at-Home Units: a big data study protocol

ObjectiveThis article describes a study protocol for the implementation of quality and traceability control in the hazardous medication circuit through an analysis of risks and the development and introduction of a Big Data-based software application aimed at performing a continuous and dynamic audit of the whole system. MethodA standardized graphical modeling tool called Business Process Model Notation will be used to generate a detailed description of each of the stages in the hazardous medication circuit with a view to ensuring full traceability of the system. The information on each stage will be collected in a flowchart, which will be used —together with each event's likelihood of occurrence and severity— as a basis to calculate the criticality index of the different control points established and to determine any control measures that may be required. The flowcharts will also be used to develop the technological support needed to capture all such data as may be relevant to the model. Proper quality control of the process will be ensured by client software agents intended to allow automatic application of efficient data processing tools at the different phases. In addition, Big Data methodologies, in particular machine learning, will be used to develop algorithms based on the repository of generated data to come up with patterns capable of improving the protocols to be applied. Lastly, proper operation of the process will be ensured by means of clinical-pharmaceutical verification and a full technical-documentary review of control and registration systems. ConclusionsThe development of a risk management system based on mobile technology will allow integration of hazardous drugs into a standardized system, ensuring the safety, quality, and traceability of the hazardous medication handling process.

Power Consumption Modeling of Wheeled Mobile Robots With Multiple Driving Modes

Wheeled mobile robots (WMRs) are being used in many real-world applications. The main challenge in deployment of WMRs is their limited battery power. In order to complete a given task autonomously, it becomes necessary to estimate a priori the total energy consumption for the task assigned to a WMR. An accurate power consumption model allows to estimate the energy consumption of a WMR. This paper presents the power consumption modeling of WMRs using a power-based graphical modeling tool called bond graph. First, the dynamic bond graph model is developed for the generalized configuration of a WMR according to number and types of wheels attached in chassis. Then, the developed bond graph model is utilized for the power consumption modeling of a WMR. The proposed power consumption model includes the various driving modes of a WMR, by considering its degree of steerability. Finally, the proposed methodology is applied to a reconfigurable outdoor WMR with a degree of steerability varying from 0 to 2. The estimated power from the model is compared with the measured power from the experiments. Further, the proposed power model is used for the energy planning in a trajectory tracking task based on changing driving modes of a reconfigurable WMR.

Graphic modeling of radioecological data using Violinplot

Data collection of environmental radioactive chemical contaminants is information of fundamental importance for decision-making in environmental planning and management. In this case, data analyses should be performed with a high level of significance within robust statistical planning. The objective of this study was to utilize the Violinplot as a graphical modeling tool for statistical analysis of radioecology data. To achieve this, a computational algorithm was developed in the Python language. The findings clearly demonstrate that the Violinplot is an outstanding data analysis tool for robust statistical planning in environmental management.

Causal Effect Identification in Cluster DAGs

Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been developed for performing such tasks in the last decades. One of the pervasive requirements found throughout this literature is the articulation of assumptions, which commonly appear in the form of causal diagrams. Despite the power of this approach, there are significant settings where the knowledge necessary to specify a causal diagram over all variables is not available, particularly in complex, high-dimensional domains. In this paper, we introduce a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. A C-DAG specifies relationships between clusters of variables, while the relationships between the variables within a cluster are left unspecified, and can be seen as a graphical representation of an equivalence class of causal diagrams that share the relationships among the clusters. We develop the foundations and machinery for valid inferences over C-DAGs about the clusters of variables at each layer of Pearl's Causal Hierarchy - L1 (probabilistic), L2 (interventional), and L3 (counterfactual). In particular, we prove the soundness and completeness of d-separation for probabilistic inference in C-DAGs. Further, we demonstrate the validity of Pearl's do-calculus rules over C-DAGs and show that the standard ID identification algorithm is sound and complete to systematically compute causal effects from observational data given a C-DAG. Finally, we show that C-DAGs are valid for performing counterfactual inferences about clusters of variables.

Time Series Prediction Method of Metrics of Dispatching Automation System Based on AI Platform

In view of the shortcomings of manual operation mode and passive anomaly perception in the current dispatching automation system, this paper provides a time series prediction method for the metrics of the dispatching automation system based on the artificial intelligence platform. Firstly, based on the collected metrics of the dispatching automation system, the time series prediction algorithm of metrics based on Long Short-Term Memory(LSTM) and LightGBM(LGB) combined model is studied. Then, the algorithm is integrated into a one-stop graphical interactive modeling tool and the model training process is drawn. Through the task is automatic split and distributed model training is automatically carried out on a regular basis. Then, the AI model service sharing technology based on Kubernetes is used to realize the model service release and automatic update, and the platform interactive development component is used to realize automatic online prediction and rolling iterative update. Finally, the actual data of the system metrics is used for example analysis, and the results show that the proposed method can realize the advance prediction of the system metrics, and realize the whole process automatic integration model training and prediction based on the artificial intelligence platform, and effectively improve the model training efficiency, meet the real-time and security requirements of the dispatching automation system, and is more suitable for engineering application scenarios.

A Novel Method for Converting Colored Petri Nets to Ladder Diagram in the Automation of Automated Manufacturing Systems

The Ladder Diagram (LD) is an industry-standard programming language for constructing automated manufacturing systems (AMSs) control algorithms. Petri net (PN) theory, on the other hand, is a mathematical and graphical modeling tool for AMSs. Multiple types of PN-based LDs are designed for AMSs with highly complicated LD structures. Thus, it is necessary to propose a technique that can assist in the minimization of the structural complexity of LDs. The main purpose of this study is to propose a methodology for the implementation of LDs in AMSs. First, a colored resource-oriented Petri net (CROPN) is developed for modeling and guaranteeing the deadlock-free behavior of AMS. Second, a ladder diagram CROPN (LDCROPN) is constructed in order to transform the CROPN into an LD. The proposed LDCROPN is assessed using instances from the literature. The results show that the LDCROPN is effective, has a simpler structure, and has less computational overhead than existing techniques.

Cinco-Based Approach for Agent Petri Net Models

In this paper, a simplified simulation approach of the Agent Petri Nets (APN) model is proposed to facilitate and accelerate the development and evaluation of system models. The main idea is to apply the new trends of model-driven approach to the design of specific tools and provide facilities to evaluate quickly the developed system. The CINCO tooling suite is used to full generation of a specific graphical modelling tool for the APN model. The simulation code is simply added to the generated code. This tool allows users to build models based on Agent Petri Nets formalism and at the same time to execute these models. Combined APN with the CINCO tool greatly simplifies the development of modelling applications and allows users to get an early experience with a prototype system. To highlight the ability of the proposed tool, a real-life example of a traffic light adaptive system is considered and discussed here.

Crucial States Estimation in Radio Block Center Handover Using Petri Nets With Unobservable Transitions

The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system.

Toward a Graphical Modeling Tool for Response-Time Requirements Based on Soft and Hard Real-Time Capabilities in Industrial Cyber-Physical Systems

Emerging industrial digitization metaphors such as cyber-physical systems and digital twins increasingly advocate the use of distributed and autonomous modular solutions. This article proposes a visual notation for modeling the timing properties of such modular industrial systems. The proposed work builds upon an existing notation that enables the analysis of the timing properties and requirements of potentially competing automation solutions. It provides a set of symbols that capture, among others, communication networks and devices, I/O interfaces, and industrial controllers. This article extends this preexisting notation by modeling the relation between physical devices and their controlling software in the context of industrial automation applications. The existing notation and its extensions are described and applied to a miniaturized automation system to illustrate its dual usage: as a tool to collect requirements and as a tool to analyze existing systems. The testing scenario highlights the opportunities provided by the extensions in comparison to the preexisting notation, which, for the sake of completeness, is more briefly introduced and described.

A Petri nets-based framework for whole-cell modeling

Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction.

Scheduling of Dual-Gripper Robotic Cells With Reinforcement Learning

A dual-gripper robotic cell consists of multiple processing machines and one material handling robot, which can perform an unloading or a loading task one at a time but can hold two parts at the same time. We address a scheduling problem of the robotic cell that determines a robot task sequence when two part types are processed in a different set of machines and all machines have variable processing times within a given interval. The objective is to minimize the makespan. This study proposes a learning-based method, i.e., a reinforcement learning (RL) approach, for the first time, to address a dual-gripper robotic cell scheduling problem. The problem is modeled with a Petri net, a graphical and mathematical modeling tool, which is used as an environment in RL. The states, actions, and rewards are defined by using flow shop scheduling properties, features from a Petri net, and knowledge from previous studies of scheduling robotized tools. Then, the RL approach is compared to the first-in-first-out (FIFO) rule, which is generally used in practice, a swap sequence, which is widely used for cyclic scheduling of dual-gripper robotic cells, and a lower bound. The extensive experiments show that the proposed method performs better than FIFO and the swap sequence; moreover, the gap between the makespan of the proposed method and the lower bound is not large. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —We address a scheduling problem of dual-gripper robotic cells with two-part types when all machines have processing time variations. We propose an RL approach to obtain an efficient robot task sequence in order to minimize makespan. The proposed method is performed offline, and a robot task sequence is then obtained instantaneously. The proposed method performs better than the FIFO rule used in practice and the swap sequence used for cyclic scheduling of robotic cells. It can be easily extended for scheduling other configurations of robotic cells.

“VTMine for Visio”: Graphical Tool for Modeling in Process Mining

Process-Aware Information Systems (PAIS) is a special class of the IS intended for the support the tasks of initialization, end-to-end management and completion of business processes. During the operation such systems accumulate a large number of data that are recorded in the form of the event logs. Event logs are a valuable source of knowledge about the actual behavior of a system. For example, there can be found information about the discrepancy between the real and the prescribed behavior of the system; to identify bottlenecks and performance issues; to detect anti-patterns of building a business system. These problems are studied by the discipline called “Process Mining”.The practical application of the process mining methods and practices is carried out using the specialized software for data analysts. The subject area of the process analysis involves the work of an analyst with a large number of graphical models. Such work will be more efficient with a convenient graphical modeling tool. The paper discusses the principles of building a graphical tool “VTMine for Visio” for the process modeling, based on the widespread application for business intelligence Microsoft Visio. There are presented features of the architecture design of the software extension for application in the process mining domain and integration with the existing libraries and tools for working with data. The application of the developed tool for solving various types of tasks for modeling and analysis of processes is demonstrated on a set of experimental schemes.

A Tool to Manage Traceability on Several Models and Its Use Case

Abstract To examine requirements and design of a system, using graphical models such as UML is one of the effective ways because it helps developers to understand the system and activities using the system. Usually, more than two types of notations are used to represent a system. At the age of digital transformation, relationships among several different systems should be also discussed and they are of course represented in several different notations. To improve the development and the analysis of several systems using such several notations, traceability among elements in the different notations should be managed, but most techniques focus on the traceability among a single project. In this paper, we present a tool to manage traceability on several different models. The tool is developed as a plugin of an existing graphical modeling tool called Astah. Astah enables us to describe UML models as well as mind maps, data flow diagrams, flow charts and so on. To evaluate our tool, we performed a method to elicit requirements of several different systems together by using the tool. We confirmed our tool was helpful to perform the method, but some additional functions would improve the performance more than now. The additional functions are as follows: tracing links transitively, annotating each link to clarify its type and recording an end of a link while the end is removed from a model.

Засоби автоматизованого формулювання умов працездатності складних технічних систем

Під надійністю техніки розуміють здатність системи або її складових виконувати свої функції за заданих умов та у визначений період часу. Надійність є комплексною характеристикою технічної системи і, відповідно до її призначення, може включати: безвідмовність, довговічність, ремонтопридатність й інші атрибути або їх комбінації. Надійнісний аналіз проводиться з використанням певної кількості засобів графічного моделювання для забезпечення наочності етапів процесу, таких як структурні схеми надійності, дерева відмов та ін. Структурні схеми надійності (англ. Reliability Block Diagram) є простим, але наочним методом демонстрації зв'язків між елементами системи за допомогою блоків і з'єднувальних ліній. Моделювання структури технічної системи і визначення характеристик її складових з використанням структурної схеми надійності є потужним та наочним засобом, але складність його зростає відповідно до кількості елементів та вузлів системи. Методи аналізу структурних схем мають експоненціальну складність, яка залежить від кількості елементів та способу їх розміщення. Для вирішення цієї проблеми розроблено засоби автоматизованого формулювання умов працездатності складних технічних систем, які складаються з: алгоритму обходу структурної схеми надійності, алгоритму виявлення послідовного з'єднання та алгоритму виявлення паралельного з'єднання, які виявляють сегменти, котрі з'єднані між собою послідовно та паралельно відповідно. Також подано програмне забезпечення візуалізації структурних схем надійності та автоматизованого формулювання умов працездатності технічних систем, яке дає змогу звільнити користувача від багаторазового виконання рутинних операцій та автоматизувати процес формування вхідних даних для подальшого надійнісного аналізу.

CaaSSET: A Framework for Model-Driven Development of Context as a Service

Developing a stand-alone context-aware application is costly and time-consuming due to the diversity and complexity of the requirements. Context-as-a-Service (CaaS) seeks to overcome the issues by following the idea of Software-as-a-Service (SaaS). The CaaS delivery model attempts to separate context service consumers from context service providers. However, CaaS shifts the complexities of context service provisioning and management from consumers toward providers. Considering the merits of Model-Driven Development (MDD) in coping with the complexities of problems in different domains, we leverage MDD in the CaaS domain and propose the CaaSSET framework to ease the development of context services. A prerequisite for realizing this aim is to specify the CaaS domain as a meta-model. Our CaaSSET framework consists of a CaaS reference model, a context service meta-model, a graphical modeling tool, a code generation tool, and a context service management tool. Using CaaSSET, the context services are modeled in a graphical environment and transformed into executable context web services (semi-) automatically. To evaluate the applicability of the CaaSSET framework, we have modeled and developed a tourism context service as a case study. We have also compared the framework with the related work based on 44 evaluation criteria, which we have extracted from 14 context service requirements.

A Petri Net-Based Heuristic Algorithm for Short-Term Vehicle Scheduling in a Vehicle Inspection System

Vehicle inspection systems (VIS) have been extensively used by automobile manufacturers, maintenance companies, and the traffic administrative departments for the inspection of safety, reliability, and other indicators of both newly produced and in-used vehicles. How to increase their productivity by effectively operating such systems is an interesting and crucial problem. Because of the space limitation of testing field in such a system, a vehicle must complete pre-set inspection items at multiple stations in sequence. Hence, it is very challenging to sequence the vehicles to be tested to shorten the unnecessary waiting time for vehicle testing. This paper investigates the vehicle scheduling problem of such systems, where the time for vehicle movement in the system can be ignored. The system is modeled by resource-oriented Petri net, a graphical and mathematical modeling tool. Based on the model, this work analyzes the testing cycle time and develops a heuristic algorithm to efficiently solve the vehicle scheduling problem in such a system. For the first time, a heuristic algorithm for generating an optimal vehicle queue for testing is proposed. Industrial examples are used to illustrate the proposed algorithm and results show that a significant reduction in total test turnaround time can be obtained in comparison with the existing methods.

Reusability and composability analysis for an agent-based hierarchical modelling and simulation framework

Agent-based modeling and simulation has been proved useful in a variety of different complex adaptive systems comprised of autonomous, interacting components. To resolve the shortcoming of current agent-based modeling frameworks with respect to decomposition and modularity, this paper presents a formal hierarchical modeling and simulation framework with three–level architecture to reduce ambiguity as well as improve clarity in the model definition. The bottom level is Component Model (CM), which implements some domain-specific support functionality, such as curve motion in the physical domain, intelligent decision-making in the cognitive domain, etc. The middle level is Agent Model (AM), which describes an agent which can react to the current situation by executing a sequence of CMs. The top level is System Model (SM), which defines that a CAS model consists of several AMs and also the interactions between these AMs. In the hierarchical architecture, one SM can be built up from lower-level models, which were linked in a loosely coupled fashion via an event-driven interface. We then analyze the reusability and composability of lower-level models of this hierarchical framework in a formalized way. To demonstrate the effectiveness of the proposed solution, we develop a graphical composite modelling tool named GraphSim, and the case study concerning two social dynamics system scenarios is also presented.

Improving user productivity in modeling tools by explicitly modeling workflows

Software engineering aims to create software tools that allow people to solve particular problems in an easy and efficient way. In this regard, model-driven engineering (MDE) enables to generate software tools, by systematically modeling and transforming models. To do so, MDE relies on language workbenches: Integrated Development Environment for engineering modeling languages, designing models, executing them, and verifying them. However, the usability of these tools is far from efficient. Common MDE activities, such as creating a domain-specific language or developing a model transformation, are non-trivial and often require repetitive tasks. This results in unnecessary risings of development time. The goal of this paper is to increase the productivity of modelers in their daily activities by automating the tasks performed in current MDE tools. We propose an MDE-based solution where the user defines a reusable workflow that can be parameterized at run-time and executed. We have implemented workflows in the graphical modeling tool AToMPM. An empirical evaluation shows that the users’ productivity is significantly improved.

Parameter Estimation for Quantitative Dependability Analysis of Safety-Critical and Control Systems of NPP

Unified Modeling Language (UML) has been frequently used as one of the most powerful object-oriented graphical modeling tools for designing and modeling a safety-critical and control system since past decades. UML can capture all the system requirements, and the developed model is well understood by all the stakeholders. However, UML model has some limitations in demonstrating the dynamic behavior of a system. Furthermore, UML cannot be directly mapped to a mathematical modeling tool for a critical analysis. A state-space modeling technique overcomes the above-stated limitations of UML maturely and is useful for prognostics of safety-critical systems. However, existing methods to perform safety analysis using a state-space model are based on assumed state transition probabilities. The inaccuracies in predicting transition probability results translate into erroneous safety analysis—the extent of that error depends on a criticality of the system’s state. Thus, to have practical value, a prediction must compute probabilities, rather than assuming or qualitatively assess them. In this paper, a safety analysis framework is introduced, which uses a technique to map the UML model into the state-space model for capturing all the system requirements along with dynamic behavioral analysis and compute state transition probabilities, instead of taking assumed values, to analyze a safety-critical and control system. The validation of this approach is done on 29 different safety-critical and control systems of Nuclear Power Plant, and shown on the digital feed water control system, which consists of diversified network.