Real-time embedded software testing method based on extended finite state machine

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

Intrusion Detection for Wireless Mesh Networks using Finite State Machine

Wireless Mesh Networks is vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, Lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer suffi- cient and effective for those features. In this paper, we propose a distributed intrusion detection ap- proach based on timed automata. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then we con- struct the Finite State Machine （FSM） by the way of manually abstracting the correct behaviors of the node according to the routing protocol of Dynamic Source Routing （DSR）. The monitor nodes can verify every node＇s behavior by the Finite State Ma- chine （FSM）, and validly detect real-time attacks without signatures of intrusion or trained data.Compared with the architecture where each node is its own IDS agent, our approach is much more efficient while maintaining the same level of effectiveness. Finally, we evaluate the intrusion detection method through simulation experiments.

Artificial emotional model based on finite state machine

According to the basic emotional theory, the artificial emotional model based on the finite state machine（FSM） was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition fimction was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems： the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input （also a state-emotion）. The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.

Verifying the accuracy of interlocking tables for railway signalling systems using abstract state machines

Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly recommends the utilization of finite state machines during system modelling stage and formal proof methods during the verifi- cation and testing stages of control algorithms. Due to the high importance of interlocking table at the design state of a sig- nalization system, the modelling and verification of inter- locking tables are examined in this work. For this purpose, abstract state machines are used as a modelling tool. The developed models have been performed in a generalized structure such that the model control can be done automatically for the interlocking systems. In this study, NuSMV is used at the verification state. Also, the consistency of the developed models has been supervised through fault injection. The developed models and software components are applied on a real railway station operated by Metro Istanbul Co.

EDSM-Based Binary Protocol State Machine Reversing

Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols are emerging in endlessly various network environments.Herein,relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection.Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols,most existing methods have proven to be time-consuming with limited efficiency,especially when applied on unknown protocol state machines.This paper proposes a state merging algorithm based on EDSM(Evidence-Driven State Merging)to infer the transition rules of unknown protocols in form of state machines with high efficiency.Compared with another classical state machine inferring method based on Exbar algorithm,the experiment results demonstrate that our proposed method could run faster,especially when dealing with massive training data sets.In addition,this method can also make the state machines have higher similarities with the reference state machines constructed from public specifications.

Global Control Simulation of Electric Vehicle Based on Finite State Machine Theory

Finite state machine theory (FSM) is introduced and applied to global control of electric vehicle. Theoretical adaptation for application of FSM in control of electric vehicle is analyzed. Global control logic for parts of electric vehicle is analyzed and built based on FSM. Using Matlab/Simulink, BJD6100-HEV global control algorithm is modeled and prove validity by simulation.

Intelligent decision-making method for vehicles in emergency conditions based on artificial potential fields and finite state machines

This study aims to propose a decision-making method based on artificial potential fields(APFs)and finite state machines(FSMs)in emergency conditions.This study presents a decision-making method based on APFs and FSMs for emergency conditions.By modeling the longitudinal and lateral potential energy fields of the vehicle,the driving state is identified,and the trigger conditions are provided for path planning during lane changing.In addition,this study also designed the state transition rules based on the longitudinal and lateral virtual forces.It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations.To illustrate the performance of the decision-making model by considering APFs and finite state machines.The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals.The contributions of this study are two-fold.A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios.Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model,leading to the formulation of transition rules between different states of autonomous vehicles(AVs).

M-LSM:An Improved Multi-Liquid State Machine for Event-Based Vision Recognition

Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and speed.However,the existing event-based models for vision recogni-tion are faced with several issues,such as large network complexity and expensive training cost.In this paper,we propose an improved multi-liquid state machine(M-LSM)method for high-performance vision recognition.Specifically,we intro-duce two methods,namely multi-state fusion and multi-liquid search,to optimize the liquid state machine(LSM).Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information.We adapt network architecture search(NAS)to find the potential optimal architecture of the multi-liquid state machine.We also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity(STDP).Our M-LSM is evalu-ated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.

Test Data Generation for Stateful Network Protocol Fuzzing Using a Rule-Based State Machine

To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.

Towards a Formal Semantics for UML/MARTE State Machines Based on Hierarchical Timed Automata

UML is a widely-used, general purpose modeling language. But its lack of a rigorous semantics forbids the thorough analysis of designed solution, and thus precludes the discovery of significant problems at design time. To bridge the gap, the paper investigates the underlying semantics of UML state machine diagrams, along with the time-related modeling elements of MARTE, the profile for modeling and analysis of real-time embedded systems, and proposes a formal operational semantics based on extended hierarchical timed automata. The approach is exemplified on a simple example taken from the automotive domain. Verification is accomplished by translating designed models into the input language of the UPPAAL model checker.

Application of Finite State Machine Theory to the Simulation of Reversed Non-Linear Hysteretic Relationships

This paper presents the application of finite state machine (FSM) theory to the programming of nonlinear hysteretic model simulation for both known and newly created rules. The complicated reversed internal paths involved in the nonlinear relationship which not only depend on material properties, but also on load history, often confuse rule creators and scholars. In this paper, we first describe the development of past hysteretic models. Then we introduce the FSM theory conceptually, and explain how it is applied to reversed and diverse routes. Next, state definitions and procedures are explained with a specific data example using the bilinear model. Finally, the successful application to UC-win/FRAME (3D) is described and several characteristics are summarized. By using FSM’s states and the linkages to represent a hysteresis model, we can quickly realize the programming of the defined complex model rules, and the nonlinear modeling becomes more efficient and feasible.

A matrix-based static approach to analysis of finite state machines

Traditional matrix-based approaches in the field of finite state machines construct state transition matrices,and then use the powers of the state transition matrices to represent corresponding dynamic transition processes,which are cornerstones of system analysis.In this study,we propose a static matrix-based approach that revisits a finite state machine from its structure rather than its dynamic transition process,thus avoiding the“explosion of complexity”problem inherent in the existing approaches.Based on the static approach,we reexamine the issues of closed-loop detection and controllability for deterministic finite state machines.In addition,we propose controllable equivalent form and minimal controllable equivalent form concepts and give corresponding algorithms.

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.

Aspect-Oriented Modeling and Verification with Finite State Machines

Aspect-oriented programming modularizes crosscutting concerns into aspects with the advice invoked at the specified points of program execution. Aspects can be used in a harmful way that invalidates desired properties and even destroys the conceptual integrity of programs. To assure the quality of an aspect-oriented system, rigorous analysis and design of aspects are highly desirable. In this paper, we present an approach to aspect-oriented modeling and verification with finite state machines. Our approach provides explicit notations （e.g., pointcut, advice and aspect） for capturing crosscutting concerns and incremental modification requirements with respect to class state models. For verification purposes, we compose the aspect models and class models in an aspect-oriented model through a weaving mechanism. Then we transform the woven models and the class models not affected by the aspects into FSP （Finite State Processes）, which are to be checked by the LTSA （Labeled Transition System Analyzer） model checker against the desired system properties. We have applied our approach to the modeling and verification of three aspect-oriented systems. To further evaluate the effectiveness of verification, we created a large number of flawed aspect models and verified them against the system requirements. The results show that the verification has revealed all flawed models. This indicates that our approach is effective in quality assurance of aspect-oriented state models. As such, our approach can be used for model-checking state-based specification of aspect-oriented design and can uncover some system design problems before the system is implemented.

State space optimization of finite state machines from the viewpoint of control theory

Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines(FSMs)in most existing results,we consider the problem by viewing FSMs as logical dynamic systems.Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory,the concepts of t-equivalent states and t-source equivalent states are introduced.Based on the state transition dynamic equations of FSMs proposed in recent years,several mathematical formulations of t-equivalent states and t-source equivalent states are proposed.These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem.Using these mathematical formulations,two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs.Further,two ways of reducing the state space of FSMs are found.These can be implemented without computers but with only pen and paper in a mathematical manner.In addition,an open question is raised which can further improve these methods into unattended ones.Finally,the correctness and effectiveness of the proposed methods are verified by a practical language model.

Balancing four-state continuous-variable quantum key distribution with linear optics cloning machine

We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution（CV-QKD） by using the linear optics cloning machine（LOCM）. Benefiting from the LOCM operation, the LOCM-tuned noise can be employed by the reference partner of reconciliation to achieve higher secret key generation rates over a long distance. Simulation results show that the LOCM operation can flexibly regulate the secret key generation rate and the maximum secure distance and improve the performance of four-state CV-QKD protocol by dynamically tuning parameters in an appropriate range.

SF^2HDL： A Computational Tool of State Transition Diagram Translation

The lack of standard to electronic circuits modeling made possible the development of many tools and modeling languages for electronic circuits. In this way, several tools to be used on different descriptions stage of the designs are necessary. This paper presents a tool called SF^2HDL （Stateflow to Hardware Description Language or State Transition Table） that translates a finite state machine on state transition diagram representation, described by Stateflow tool, into an input file standard for TABELA program or into a file behavioral VHDL （Very High Speed Integrated Circuits Hardware Description Language） directly. The TABELA program was used to optimization this finite state machine. After that, the TAB2VHDL program was used to generate the VHDL code on register transfer level, what permits comparisons with results obtained by synthesis. The finite state machine must be described by Mealy model and the user can describe the machine on high level abstraction using all Simulink supports. The tool was very efficient on computational cost and it made translation of several cases, for the two VHDL description models. Every state machine translated was simulated and implemented on device EP2C20F484C7 using Quartus II environment.

基于EFSM的智能车间制造系统生产物流建模与仿真

制造业的生产物流方式处于不断变革中,对其建模仿真可为制造系统规划设计、分析及改造提供决策支持。依“人-机-物-环-法”分类给出了智能车间制造系统中实体元素的描述,结合EFSM(extended finite state machine)和组件化建模思想,建立了生产和物流组件化EFSM模型;阐述了智能车间多作业生产的建模过程以及组件模型实例化方法;通过EFSM-DEVS(discrete event system specification)模型自动转换及DEVS引擎完成了仿真运行。仿真结果表明:该方法所建立的模型更符合车间实际状况,适用性更广;组件化建模思想能构造更具扩展性的软件;建模及仿真运行的3D可视化使软件直观性更好,其仿真结果与AnyLogic保持一致。

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 pathplanning 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 theHFSMmodel aswell as its TOPSISGRA 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.