Search Results (8)

A model transformation method based on state refinement and semantic mapping is proposed to address the challenges of high modeling complexity and resource consumption in symbolic validation of industrial software requirements. First, a rule-based semantic mapping system is constructed through the explicit definition of element correspondence between statechart components and verification models, coupled with a composite state-level refinement strategy to structurally optimize model hierarchy. Second, an automated transformation algorithm is developed to bridge graphical modeling tools with formal verification environments, supported by quantitative evaluation metrics for mapping validity. To demonstrate its practical applicability, the methodology is systematically applied to railway infrastructure safety—specifically the railroad turnout control system—as a critical case study. The experimental implementation converts operational statecharts of turnout control logic into optimized NuSMV models. Not only did the models remain intact, but the state space was also effectively reduced through the optimization of the hierarchical structure. In the validation phase, the converted model is tested for robustness using the fault injection method, and boundary condition anomalies that are not explicitly stated in the requirement specification are successfully detected. The experimental results show that the validation model generated by this method has improved validation efficiency in the NuSMV tool, which is significantly better than the traditional conversion method. Full article

This article investigates the application of a deep learning model for evaluating the conformity of model images to types of UML diagrams to be used in self-training and educational settings. Our approach leans on a feature-based dataset that captures a broad range of modeling elements from class, state machine, and sequence diagrams, enhancing the ability to recognize a larger variety of categories selected for this research. The neural network trained with these features representing parts of the UML concrete syntax demonstrates 90% in classification accuracy on average, in respect to our previous research on UML diagrams classification without using a feature-based dataset. This study concludes that a feature-based approach, combined with advanced neural network architectures, can improve the classification of such images, especially in edge cases where diagrams contain similar graphical details but the whole does not represent a UML diagram. For the given research, we obtained a 0.87 F1 score. Full article

Unmanned platforms are becoming more and more widely used in both civilian and military applications. The flight safety of such aircraft is crucial. Therefore, it becomes necessary to formally model and rigorously test their software. Commencement of an unmanned air vehicle’s (UAV) operation requires it undergoing a certification process, which further heightens the quality requirements. The validation process of the designed system is presented on the example of verifying the correctness of the operational scenario involving the transfer of control over a UAV between two ground control stations (GCSs). The paper introduces the method of designing unmanned aerial vehicles and testing their functionality based on finite-state machines. The architecture description of the system encompasses the use cases view and logical view of the 1+5 architectural views model. The paper concentrates on system requirements and their transformation into unified modeling language (UML) state machine diagrams. The authors present a method of mapping requirements to functions related to flight safety and system dynamics models expressed as communicating extended finite-state machines (CEFSMs). Moreover, the paper shows the rules for testing the correctness of state machine models using methods based on determining flows in directed graphs. It should be emphasized that the paper considers the certification of UAV software components from the system safety viewpoint, which directly affects the airworthiness of the air platform. Full article

Inferring a functional specification from an existing digital design is a challenge that is suitable with reverse-engineering methods. One of the most widely used functional specification formats is a finite state machine (FSM). This article studies the possibility of blind passive specification mining for a digital device, where the device is treated as a “black box”. The presented approach treats an input and output signal waveform as the transition graph of an incomplete deterministic FSM and learns the FSM through FSM minimization. It employs a Boolean satisfiability problem (SAT) solver to find a minimal FSM that complies with observed object behavior. The known approach to identifying state machines in discrete event systems is adapted to operate with variables in the form of coloring and transition tables. The developed implementation produces a synthesizable specification in hardware description language (HDL) and a state diagram in unified modeling language (UML). The proposed approach for inferring an FSM from a waveform trace can serve as a supplementary tool during reverse engineering to provide developers with meaningful insight regarding the analyzed device. The presented case study defines metrics of successful FSM inference and applies them to a synthetic FSM and a real-world example FSM to demonstrate the applicability of the approach. Full article

Technological progress in recent years in the Cyber-Physical Systems (CPSs) area has given designers unprecedented possibilities and computational power, but as a consequence, the modeled CPSs are becoming increasingly complex, hierarchical, and concurrent. Therefore, new methods of CPSs design (especially using abstract modeling) are needed. The paper presents an approach to the CPS control part modeling using state machine diagrams from Unified Modelling Language (UML). The proposed design method attempts to combine the advantages of graphical notation (intuitiveness, convenience, readability) with the benefits of text specification languages (unambiguity, precision, versatility). The UML specification is transformed using Model-Driven Development (MDD) techniques into an effective program in Hardware Description Language (HDL), using Concurrent Finite State Machine (CFSM) as a temporary model. The obtained HDL specification can be analyzed, validated, synthesized, and finally implemented in Field Programmable Gate Array (FPGA) devices. The dynamic, partial reconfiguration (a feature of modern FPGAs) allows for the exchange of a part of the implemented CPS algorithm without stopping the device. But to use this feature, the model must be safe, which in the proposed approach means, that it should possess special idle states, where the control is transferred during the reconfiguration process. Applying the CFSM model greatly facilitates this task. The proposed design method offers efficient graphical modeling of a control part of CPS, and automatic translation of the behavior model into a synthesizable Verilog description, which can be directly implemented in FPGA devices, and dynamically reconfigured as needed. A practical example illustrating the successive stages of the proposed method is also presented. Full article

The paper proposes a novel formal verification method for a state-based control module of a cyber-physical system. The initial specification in the form of user-friendly UML state machine diagrams is written as an abstract rule-based logical model. The logical model is then used both for formal verification using the model checking technique and for prototype implementation in FPGA devices. The model is automatically transformed into a verifiable model in nuXmv format and into synthesizable code in VHDL language, which ensures that the resulting models are consistent with each other. It also allows the early detection of any errors related to the specification. A case study of a manufacturing automation system is presented to illustrate the approach. Full article

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model. Full article

Building fire is a complex geographic process related to the indoor spatial environment, a smart spatial data model can accurately describe the spatial-temporal information of a building fire scene, which is important for modeling a fire process. With the development of fire dynamics and computer science, many building fire models have been proposed and widely used. However, the spatial representation of these models is relatively weak. In this study, a fire process modeled via the Fire Dynamics Simulator (FDS) and the requirements of a spatial data model are initially analyzed. Then, a new spatial data model named the Combinatorial Spatial Data Model (CSDM) is combined with Geographic Information System (GIS). The key features of the CSDM, which include spatial, semantic, topological, event and state representations of a building fire scene modeled via the CSDM are subsequently presented. In addition, the Unified Modeling Language (UML) class diagram of the CSDM is also presented, and then experiments with a simplified building are conducted as a CSDM implementation case. A method of transferring data from the CSDM to FDS and a building fire analysis approach using the CSDM are subsequently proposed. Full article