UML State Machines Research Articles

Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulation

Improving energy efficiency of space heating systems is as important as adopting emerging clean energy technologies for decarbonising heating. While exploring strategies for reducing energy consumption, the space occupants’ expectations to thermal comfort should not be sacrificed. Conventional space heating control systems are often developed under the hypothesis of uniform temperature distribution in a bounded space and the heating system controller is primarily operated based on the information from the temperature sensor at a fixed position. That is, the measured temperature does not represent the temperature surrounding the occupant’s location. This paper explores a new control method; the heating system control is led by the occupant’s thermal experience, expressed by the temperature surrounding the occupant. A new hierarchical state machine control strategy interacting with a Multiphysics simulation model to dynamically estimate the temperature at the point of the occupant is developed. The accuracy of the Multiphysics simulation model is verified based on the corresponding experimental test room data. A co-simulation platform is built to allow COMSOL and SIMULINK to communicate in real-time for control strategy realisation. The experimental results show that the maximum and average mean square error between the measured and simulated temperature under different operating modes is less than 0.21℃ and 0.06℃, respectively. The simulation results indicate that energy consumption could be potentially reduced around 20% with an improved thermal comfort of the occupant by 30%, compared with the conventional fixed sensor control method.

A Brain-Inspired Decision-Making method for upper limb exoskeleton based on Multi-Brain-Region structure and multimodal information fusion

For the challenges faced by upper limb exoskeleton in meeting human motion intention, this article proposes a novel brain-inspired decision-making model based on multi-brain-region information transmission mechanism and multimodal information fusion method to provide accurate prediction of motion trajectory. Firstly, a multimodal information perception and fusion method is proposed. Based on surface electromyographic (sEMG) signals and angle signals collected by sensors. Then a random forest algorithm is applied to screen the key features of sEMG signal for trajectory prediction. A multimodal information hierarchical fusion method based on dual-period mechanism is designed to solve the problem of fusion defect of information with different frequency. Finally, a multi-brain-region decision-making model based on hybrid hierarchical liquid state machine (HHLSM) is established to predict motion trajectory for the exoskeleton to meet human motion intention. Experiments show that the established model has a high computational efficiency and improves the effectiveness of exoskeleton assistance. The brain-inspired decision-making model based on HHLSM established in this paper is of great significance for further research on brain-inspired control and improving the human-robot interaction effect of upper limb exoskeleton.

Using process model to define the legislative framework of electronic prescription in the Czech Republic.

Defining legislation for electronic prescription systems (EPS) is inherently challenging due to conflicting interests and requirements. The study aimed to develop a comprehensive EPS within the Czech healthcare framework, integrating legislative, process, and technical aspects to ensure security, user acceptability, and compliance with health regulations. A process modeling tool based on hierarchical state machines was employed to create a detailed process architecture for the EPS. Key participants, scenarios, and state transitions were identified and incorporated into a process model using the Craft.CASE based on the BORM methodology. The final process architecture model facilitated interdisciplinary communication and consensus-building among stakeholders, including healthcare professionals, IT specialists, and legislators. The model served as a foundation for the legislative framework and was included in the explanatory memorandum for the draft amendment to the Pharmaceuticals Act. The use of hierarchical state machines and process modeling tools in developing healthcare legislation effectively reduced misunderstandings and ensured precise implementation. This method can be applied to other complex legislative and system design projects, enhancing stakeholder communication and project success.

AGL: Incorporating behavioral aspects into domain-driven design

Domain-driven design (DDD) aims to iteratively develop software around a realistic domain model. Recent research in DDD has been focusing on using annotation-based domain-specific languages (aDSLs) to build the domain model. However, within current approaches behavioral aspects, that are often represented using UML Activity and State machine diagrams, are not explicitly captured in the domain model. The focus of this paper is to introduce a new approach for incorporating behavioral aspects into domain models within the Domain-Driven Design (DDD) approach. The proposed approach involves using a new activity graph language (AGL) as an aDSL for representing behavioral aspects within a unified domain model. This integration of AGL and the previously developed aDSL (DCSLto represent domain models) aims to achieve three important features of DDD: feasibility, productivity, and understandability. Our approach involves building a unified class model in DCSLwithin a domain-driven architecture, which uses the annotation attachment feature of the host programming language (such as Java) to attach AGL activity graphs directly to the activity class of the unified class model, resulting in a unified domain model. In this work, we define the abstract and concrete syntax of AGL. To demonstrate our method, we use a Java framework called jDomainApp and evaluate AGL through a case study to show that it is expressive and practical for real-world software. This paper presents two contributions. Firstly, it proposes a mechanism to include behavioral aspects in a unified domain model by introducing a new aDSL called AGL to represent domain behaviors. Secondly, it presents a unified modeling method for domain-driven software development. Our method significantly extends the state-of-the-art in DDD in two important fronts: constructing a unified domain model for both structural and behavioral aspects of domain models and bridging the gaps between model and code.

A Constructive State-based Semantics and Interpreter for a Synchronous Data-flow Language with State Machines

Scade is a domain-specific synchronous functional language used to implement safety-critical real-time software for more than twenty years. Two main approaches have been considered for its semantics: (i) an indirect collapsing semantics based on a source-to-source translation of high-level constructs into a data-flow core language whose semantics is precisely specified and is the entry for code generation; a relational synchronous semantics , akin to Esterel, that applies directly to the source. It defines what is a valid synchronous reaction but hides, on purpose, if a semantics exists, is unique and can be computed; hence, it is not executable. This paper presents, for the first time, an executable , state-based semantics for a language that has the key constructs of Scade all together, in particular the arbitrary combination of data-flow equations and hierarchical state machines. It can apply directly to the source language before static checks and compilation steps. It is constructive in the sense that the language in which the semantics is defined is a statically typed functional language with call-by-value and strong normalization, e.g., it is expressible in a proof-assistant where all functions terminate. It leads to a reference, purely functional, interpreter. This semantics is modular and can account for possible errors, allowing to establish what property is ensured by each static verification performed by the compiler. It also clarifies how causality is treated in Scade compared with Esterel. This semantics can serve as an oracle for compiler testing and validation; to prototype novel language constructs before they are implemented, to execute possibly unfinished models or that are correct but rejected by the compiler; to prove the correctness of compilation steps. The semantics given in the paper is implemented as an interpreter in a purely functional style, in OCaml.

Review on the application of deep learning algorithms in video game AI agent

AI algorithms have been applied in video games more than before. Since the early 2000s, the benchmark of the application of AI agent in video games has been put forward by researchers and engineers. This paper introduces the application of four different deep learning algorithms in video games, namely Component-based Hierarchical State Machine (CBHSM) algorithm, Monte Carlo Search Tree (MCTS) algorithm, Generative Adversarial Networks (GAN) algorithm, and the combination of A* algorithm and Q-learning algorithm. It can be summarized that both CBHSM and MCTS algorithms can modify and optimize the traditional Finite State Machine (FSM) algorithm applied in NPC (Non Player Characters) system. Additionally, A* algorithm can be combined with Q-learning algorithm to solve the problem of the difficulty on huge state space and limited time, and GAN algorithm, a model of unsupervised learning, can generate results by fewer training sets.

Using a process algebra interface for verification and validation of UML statecharts

UML diagrams are the conventional methods for visual modeling systems. Among them, the Statechart diagrams are used to show the runtime behavior of a system, but the correctness of such diagrams is the primary concern of the designers because of concurrency issues like livelock, inaccessible states, and non-deterministic states. Process algebra methods have the capabilities that are suitable for verification and validation of Statecharts. To this end, in this paper, process algebra language LOTOS (Language Of Temporal Ordering Specification) is used as the target language, and a method is presented to map UML Statecharts to the LOTOS processes, called USLP. Then the correctness of the proposed mappings is proved by demonstrating the isomorphism relation between the Labeled Transition System (LTS) of a Statechart and the LTS of its transformed LOTOS specification. Next, tools CADP (Construction and Analysis of Distributed Processes) is used for verification and validation of the mapped LOTOS models, and the CSP process algebra and its tools, FDR are used to verify the properties could not be verified by the LOTOS and its toolset. The experimental results show our approach can: (1) verify some properties (the issues) that are not verified by other approaches and (2) reduce the space that should be searched to verify the properties.

Assessing the specification of modelling language semantics: a study on UML PSSM

Modelling languages play a central role in developing complex, critical systems. A precise, comprehensible, and high-quality modelling language specification is essential to all stakeholders using, implementing, or extending the language. Many good practices can be found that improve the understandability or consistency of the languages’ semantics. However, designing a modelling language intended for a large audience is still challenging. In this paper, we investigate the challenges and typical issues with assessing the specifications of behavioural modelling language semantics. Our key insight is that the various stakeholder’s understandings of the language’s semantics are often misaligned, and the semantics defined in various artefacts (simulators, test suites) are inconsistent. Therefore assessment of semantics should focus on identifying and resolving these inconsistencies. To illustrate these challenges and techniques, we assessed parts of a state-of-the-art specification for a general-purpose modelling language, the Precise Semantics of UML State Machines (PSSM). We reviewed the text of the specification, analysed and executed PSSM’s conformance test suite, and categorised our experiences according to questions generally relevant to modelling languages. Finally, we made recommendations for improving the development of future modelling languages by representing the semantic domain and traces more explicitly, applying diverse test design techniques to obtain conformance test suites, and using various tools to support early-phase language design.

Implementation of FSM Based Chat-Bots in a Graphical Designer

Finite state machine (FSM) is a powerful tool to model object behavior. Using FSM and its extensions to model program behavior followed by the automatic generation of executable code is the approach encouraged by the model-driven development (MDD) – a software development methodology based on the concepts of model and model transformation.In this paper, a brief overview of FSM-based common methods to model and develop software programs of any nature is given. These methods include David Harel’s statecharts, UML State Machines, Virtual Finite State Machine, etc. Examples of all types of software systems (transformational, interactive, reactive) implemented using FSM are cited.Chat-bots as an example of an interactive software system are considered: concept, classification methods, implementation techniques. A graphical designer of rule-based chat-bots to be integrated in the messenger Telegram is developed and implemented. In this designer, chat-bot behavior is modeled using FSM.Formal method to model a rule-based chat-bot using FSM is provided. The FSM concept is extended by disabled transitions to save history of transition changes made during the FSM design process. A brief overview of code generation methods from FSM specification is done; advantages and disadvantages of the most popular approaches are considered. Dynamic approach to generate code by FSM specification saved in DB is proposed. To implement this approach, document MongoDB and in-memory key-value Redis DB are used; FSM is kept as a JSON-document. This approach is efficient in flexibility, speed and memory needs.Architecture diagram of developed chat-bot graphical designer is given. It has the microservice architecture. The FSM model-to-code transformation is carried out by the bot-execution service written using compiled language Go. Other services include the front-end (UI for end-user, CRUD API for chat-bot) and the bot-management (synchronization of document and key-value databases) services.

A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection

In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly.

Enriching UML Statecharts through a Metamodel: A Model Driven Approach for the Graphical Definition of DEVS Atomic Models

The Discrete Event System Specification (DEVS) formalism provides a set of mathematical elements for modeling time-varying systems. However, when DEVS models are implemented in an executable representation (i.e., using a general-purpose programming language), some deviation from the formalism is unavoidable. One way to bridge the gap between modeling and simulation theory and practice is to define new artifacts that support both views during the specification. When the specification is supported with a graphical representation, the formalization task is less complex and can be performed by non-expert modelers. For DEVS atomic models, most common graphical representation is through UML statecharts. In this paper, we present a theoretical and practical metamodel for the definition of atomic models structured following the Classic DEVS with Ports formalization. Such a metamodel is the core of a model-driven approach used to develop a modeling software tool that employs enriched UML statecharts for the graphical representation of the DEVS behavior. In here, the traditional UML statechart representation is enriched with a set of new components with the aim to provide a broad definition of DEVS atomic models. The final software tool is deployed as a plugin for Eclipse Platform.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA). The HFSM model is composed of two layers: the global FSM model and the local FSM model. The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local path-planning module, meanwhile pulsating feedback to the former as real-time refresh data. To identify different traffic scenarios in a cerebrum-like way, the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events, and the local FSM model is designed as 16 states and 8 characteristic events. In respect to designing a cerebrum-like algorithm for state transition, this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event; then, it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition. To verify the feasibility, reliability, and safety of the HFSM model as well as its TOPSIS-GRA state transition algorithm, this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform. The results display that the accuracy of obstacle detection gets 98%, lane line prediction is beyond 70 m, the speed of collision avoidance is higher than 45 km/h, the distance of collision avoidance is less than 5 m, path planning time for obstacle avoidance is averagely less than 50 ms, and brake deceleration is controlled under 6 m/s2. These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

TABS: Transforming automatically BPMN models into blockchain smart contracts

Research on blockchains addresses multiple issues, with one being the automated creation of smart contracts. Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity. We report on a new approach to developing smart contracts with the objective of automating the process to increase developer efficiency and reduce the risk of errors introduced by software developers. To support industry adoption, we use Business Process Model and Notation (BPMN) modeling to describe an application while targeting applications in the trade vertical. We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event (DE) modeling for concurrency with Hierarchical State Machines (HSMs) to represent application functionality. Then, further transformations are used to transform the DE-HSM model into methods in smart contracts. The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of (i) reducing processing costs and/or (ii) providing privacy so that other participants in the smart contract do not have visibility into the processing of the pattern. We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.

Providing scalability and privacy for smart contract in the healthcare system

As the healthcare industry evolved, it became an integral participant in providing services. In recent years, remote observation services have grown increasingly important for patients. In cases where patients are not physically present in clinics or hospitals, healthcare providers produce observations systems that diagnose and prescribe treatment. In/on patients' bodies, sensors are installed. Data can be exchanged wirelessly between healthcare systems. As a result of the implementation of blockchain-based characteristics (e.g. distributed ledger, decentralized storage, and authentication), healthcare systems can be enhanced. Accordingly, smart contracts are applied for the achievement of more authentication and sharing of records, and requirements for participants automatically. As the number of users or other components increases, blockchains and smart contracts should be able to provide quality service. Technically, smart contracts using blockchains have several challenges that are important, such as scalability, enhanced security, and optimal performance. There is still a question about the scalability of blockchain as there is a significant amount of computational power needed to scale up with a high number of participants in healthcare systems. In this paper, Hierarchical State Machines (HSMs) and ZoKrates are used to lower the consumption gas and the scalability of blockchain with smart contracts.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Events detection and handling based on hierarchical finite state machines in EAST PCS

Experimental Advanced Superconducting Tokamak (EAST) is committed to long pulse high performance operation where plasma parameters will necessarily approach stability limits, which sets a higher demand on real-time events detection and handling (EDH). In order to enhance the current EAST plasma control system (PCS) events handling ability and explore a new intelligent scheduling mode for China Fusion Engineering Testing Reactor (CFTER) PCS, the EDH algorithm based on hierarchical finite state machines (HFSM) is designed in EAST plasma control system (PCS). The events and states in PCS were designed, and we successfully validated the state transition and algorithm scheduling. Moreover, a new event detection and processing for controlling hot spots of in-vessel components from hot spots is developed, and a simulation experiment is carried out in the closed-loop test environment between PCS and MATLAB/Simulink. The simulation results show that the EDH algorithm could capture the exception in time and implement effective strategies with the change of hot spots temperature. The temperature of hot spots were prevented from reaching a drastic situation, which proved the effectiveness of the EDH to protect the internal components of the device.

Complexity Metrics for Statechart Diagrams

Model-Driven Development and the Model-Driven Architecture paradigm have in the recent past been emphasizing on the importance of good models. In the Object-Oriented paradigm one of the key artefacts are the Statechart diagrams. Statechart diagrams have inherent complexity which keeps increasing every time the diagrams are modified, and this complexity poses problems when it comes to comprehending the diagrams. Statechart diagrams provide a foundation for analysing the dynamic behaviour of systems, and therefore, their quality should be maintained. The aim of this study is to develop and validate metrics for measuring the complexity of UML Statechart diagrams. This study used design science which involved the definition of metrics, development of a metrics tool, and theoretical and empirical validation of the metrics. For the measurement of the cognitive complexity of statechart diagrams, this study proposes three metrics. The defined metrics were further used to calculate the complexity of two sample statechart diagrams and found relevant. Also, theoretical validation of the defined metrics was done using the Weyuker’s nine properties and revealed they are mathematically sound. Empirical validations were performed on the metrics and results indicate that all the three metrics are good for the measurement of the cognitive complexity of statecharts.

A Graph Transformation Approach for Modeling UML Diagrams

This article proposes an approach for transforming UML Statecharts, Sequence Diagrams and activity Diagrams to Time Petri Nets with Action Duration (DTPN) using graph transformation. By this transformation the author aims to bridge the gap between semi-formal models generated by UML and formal models DTPN. UML is considered to be the standardized language for modelling and describing systems behaviors for analysis. In other hand, DTPN models are tools for the specification and performance analysis of distributed and concurrent systems. However, the proposed approach allows to generate automatically a visual modeling tool for DTPN. The cost of building a visual modeling tool from scratch is prohibitive. Meta-modeling approach is useful to deal with this problem since it allows the modeling of the formalisms themselves, by means of graph grammars. The meta-modeling tool AToM3 is used.

An event-triggered real-time motion planning strategy for autonomous vehicles

Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

Decision making framework for autonomous vehicles driving behavior in complex scenarios via hierarchical state machine

With the development of autonomous car, a vehicle is capable to sense its environment more precisely. That allows improved drving behavior decision strategy to be used for more safety and effectiveness in complex scenarios. In this paper, a decision making framework based on hierarchical state machine is proposed with a top-down structure of three-layer finite state machine decision system. The upper layer classifies the driving scenario based on relative position of the vehicle and its surrounding vehicles. The middle layer judges the optimal driving behavior according to the improved energy efficiency function targeted at multiple criteria including driving efficiency, safety and the grid-based lane vacancy rate. The lower layer constructs the state transition matrix combined with the calculation results of the previous layer to predict the optimal pass way in the region. The simulation results show that the proposed driving strategy can integrate multiple criteria to evaluate the energy efficiency value of vehicle behavior in real time, and realize the selection of optimal vehicle driving strategy. With popularity of automatic vehicles in future, the driving strategy can be used as a reference to provide assistance for human drive or even the real-time decision-making of autonomous driving.