Modeling Of Real-time Systems Research Articles

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

Model and design of real-time control system for aerial variable spray.

The dosage sprayed upon per unit area is an important index to measure the effects of pesticide application. Owing to the fact that parameters such as flight height, flight speed, and spray swath can change at any given time, it is impossible to ensure a consistent amount of pesticide application per unit area during the course of aerial variable spray. In order to ensure a consistent amount of pesticide application per unit area, a set of control models of aerial variable spray using an unmanned aerial vehicle (UAV) was proposed, and the corresponding control system was developed based on the technology of aerial variable spray. According to the change of flight parameters, this system was able to adjust the opening degree of solenoid valve through the control model of aerial variable spray. After that, the amount per unit time would change to ensure a consistent amount of pesticide application per unit area, which effectively avoided the phenomenon of uneven pesticide application and improved the accuracy. According to the actual demand for the area in need of pesticide application, the operator can manually control the amount of pesticide applied and change the dosage sprayed upon per unit area to achieve a better effect. Through field tests, it was verified that the system has high accuracy of variable control. The deviation range was between 0.11% and 9.79%, which met the demands of agricultural aviation pesticide application. Furthermore, the system had strong stability for working continuously for more than 6 h at 30°C to meet the environmental requirements of pesticide application via UAV. All the data related to the pesticide application were stored in this system, which provided a reference for the further study of the precision technology in pesticide application. The model proposed in this paper also provided a theoretical basis for the technology development of aerial variable spray.

INFORMATION REAL-TIME DELIVERY (IRTD) SYSTEM BASED INTERNET OF THING (IOT) IN FORWARDING INDUSTRY

Logistic sector, particularly in the container haulage industry, is dealing with million shipments of cargo every day, origin and destination, size, weight, content, and location are all tracked across the global delivery network. The container in and out at the port causes congestion and effectson truck late delivery. This issue resulted incontainer haulage invariably received lots of complaints by the customer concerning the cargo delay without the customer grasp precisely situation.The cargo delays happen caused by waiting time at the port terminal, customs clearance, road congestion, bad weather, the breakdown of truck and shortage of driver.Thus, this study seeks to explore the related issues towards further improvements on delays notification. The qualitative research was adopted in the study and data were collected from related expert participants in three different areas, namely haulage, forwarding agent and technology service provider. This research focuses on the current process of delivery notification status to the customer and redesigns a new model of information real-time delivery system based Internet of Thing (IoT) at the haulage industry. Furthermore, this research will determine the expected benefits of the IRTD system inthe haulage industry. This study is about the Information Real-Time Delivery Systems (IRTDS) through the Internet of Things (IoT) for real-time monitoring and updated delivery systems to the stakeholders. Business Process Re-engineeringemployed in this study to provide holistic guidance about a development model of IRTDSthrough the Internet of things as innovation in a haulage industry environment.

Exact acceleration of complex real-time model checking based on overlapping cycle.

When real-time systems are modeled as timed automata, different time scales may lead to substantial fragmentation of the symbolic state space. Exact acceleration solves the fragmentation problem without changing system reachability. The relatively mature technology of exact acceleration has been used with an appended cycle or a parking cycle, which can be applied to the calculation of a single acceleratable cycle model. Using these two technologies to develop a complex real-time model requires additional states and consumes a large amount of time cost, thereby influencing acceleration efficiency. In this paper, a complex real-time exact acceleration method based on an overlapping cycle is proposed, which is an application scenario extension of the parking-cycle technique. By comprehensively analyzing the accelerating impacts of multiple acceleratable cycles, it is only necessary to add a single overlapping period with a fixed length without relying on the windows of acceleratable cycles. Experimental results show that the proposed timed automaton model is simple and effectively decreases the time costs of exact acceleration. For the complex real-time system model, the method based on an overlapping cycle can accelerate the large scale and concurrent states which cannot be solved by the original exact acceleration theory.

A situation-aware task model for adaptive real-time systems

Real-time systems are usually well-defined and operate based on a specific task model defined during system design. However, the system can interact with different objects from its environment at runtime and needs to guarantee its operational as well as timing behavior even in adverse environmental situations. Uncertainties in the system environment impose challenges on assuring the runtime behavior during system design. The system needs to adapt to different environmental situations which require the task model to consider the execution of adaptive tasks which can be activated in response to environmental events. We present an operational environment model that characterizes environmental situations of the real-time system and identifies the adaptive tasks needed to be activated at runtime. The adaptive tasks can be included and executed using a number of existing task models which allow non-deterministic task activation patterns. We present a situation-aware task model which efficiently maps the environmental events to (reduced) adaptive tasks. To demonstrate the applicability and usability of the proposed situation-aware task model, we perform the experimental analysis using two case studies: an automotive situation-aware task model, and an unmanned aerial vehicle situation-aware task model. The experimental results of our work show that the constructed situation-aware task model contains a maximum of nine vertices and 68 edges, provides an improvement in terms of scheduling overhead and in adaptation time (with respect to the considered existing task models).

Intelligent agent for formal modelling of temporal multi-agent systems

Abstract Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system.

Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles

Modelling and design of real-time energy management systems for optimising the operating costs of a fuel cell/battery electric vehicle are presented in this paper. The proposed energy management system consists of optimally sharing the propulsion power demand between the fuel cell and battery by enabling them to support each other for operating cost minimisation. The optimisation is achieved through real-time minimisation of a cost function, which accounts for fuel cell and battery degradation, hydrogen consumption and charge sustaining costs. A detailed analysis of each term of the overall cost function is performed and presented, which enables the development of a real-time, advanced energy management system for improving a previously presented simplified version using more accurate modelling and by considering cost function minimisation over a given time horizon. The performance of the proposed advanced energy management system are verified through numerical simulations over different driving cycles; particularly, simulations were performed in MATLAB-Simulink by considering a hysteresis-based energy management system and both simplified and advanced versions of the proposed energy management system for comparison.

Conceptual model of real-time IoT systems

We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & controlling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.

Schedulability analysis of real-time multiframe cosimulations on multicore platforms

For real-time simulations, the fidelity of simulation depends not only on the functional accuracy of simulation but also on its timeliness. It is helpful for simulation designers if they can analyze and verify that a simulation will always meet its timing requirements without unnecessarily sacrificing functional accuracy. Abstracting the simulated processes simply as software tasks allows us to transform the problem of verifying timeliness into a schedulability analysis problem where tasks are checked as to whether they are schedulable under the timing constraints or not. In this paper we extend a timed automaton-based framework due to Fersman and Yi for schedulability analysis of real-time systems, for the special case of real-time multiframe cosimulations. To the best of our knowledge, this work is the first to analyze the schedulability of single- or multiframe real-time simulations. We found that there are some special requirements posed by multiframe simulations, which necessitate changes and improvements in the existing framework designed for actual real-time systems. We made the required theoretical extensions to the framework and implemented our extended framework in UPPAAL, a tool for modeling, simulation, and verification of real-time systems modeled as timed automata. The functional correctness and resource requirements of the implemented framework are then demonstrated using simple examples.

RETRACTED ARTICLE: A fuzzy model of wearable network real-time health monitoring system on pharmaceutical industry

With the introduction of wearable network technologies, smart healthcare is gradually being realized. Real-time health care monitoring system should be safe, effective, and patient-centered. This paper aims to analyze the opinions and emotions of medical and health products, and to design and develop monitoring systems by crawling multiple websites and reviews in a variety of formats. Data preprocessing is used to preprocess the quantized image, and finally, it is recommended in combination with the analysis results. This system combines a large amount of products’ comment data, combined with MATLAB and other tools to achieve the visualization of big data analysis, by the use of the maximum entropy algorithm for comprehensive analysis and comparison. Support vector machine (SVM) algorithm is used to analyze and compare various parts of the medical image in detail. It is suitable for current medical applications. It can provide advice for consumers to purchase drugs and achieve real-time health monitoring objectives.

Performance Optimization of Operational WRF Model Configured for Indian Monsoon Region

Providing timely weather forecasts from operational weather forecast centers is extremely critical as many sectors rely on accurate predictions provided by numerical weather models. Weather research and forecasting (WRF) is one of primary tools used in generating weather predictions at operational weather centers, and an optimal domain configuration of WRF along with suitable combination of robust computational resources and high-speed network are required to achieve the maximum performance on high-performance computing cluster (HPC) for the WRF model to deliver the timely forecasts. In this study, we have analyzed the number of methods to optimize WRF model to reduce computational time taking for the operational weather forecasts tested on HPC available at University Grants Commission center for mesosphere stratosphere troposphere radar applications, Sri Venkateswara University (SVU). To do this exercise, we have prepared a benchmark dataset by configuring WRF model for the Indian monsoon region as similar to real-time weather forecasting system model configuration. We have first carried out a series of scalability tests by increasing the number of computational nodes till it reaches a scalable point using the prepared benchmark dataset. Our node scalability results indicate the WRF model is scalable up to 65 nodes for the benchmark dataset and configured model domain on HPC available at UGC SVU center. As the total time taken for generating the model forecasts is the sum of computational time taken for predicting weather and the input/output (IO) time for writing into the storage disks. Further, we have performed several tests to optimize the time taken for IO by the weather model, and the results of IO tests clearly indicate that the WRF configured with parallel IO is highly beneficial method to reduce the total time taken for the generation of weather forecasts by the WRF model.

Reduced-Order Modelling of LTI Systems by Using Routh Approximation and Factor Division Methods

In this paper, a new model reduction technique for the large-scale continuous time systems is proposed. The proposed technique is a mixed method of Routh approximation and factor division techniques. In this technique, the Routh approximation method is applied for determining the denominator coefficients of the reduced model and the numerator coefficients are calculated by the factor division method. The proposed technique has two main advantages as it gives the stable reduced-order model if the original model is stable and ensures the retention of first “r” number of time moments of the actual system in the rth-order reduced system. This method is also applicable for those systems for which Routh approximation method fails. To illustrate the proposed method, a real-time system model is reduced where the reduced model retains the fundamental properties of the actual model. In order to examine the efficiency, accuracy and comparison to other existing standard model reduction methods, the presented technique has been verified on two standard numerical examples taken from the literature.

An Integrated Modeling, Simulation and Analysis Framework for Engineering Complex Systems

The discipline of component-based modeling and simulation offers promising gains in reducing cost, time, and the complexity of model development through the (re)use of modular components. Model-driven development suggests 1) the realization of a complex system using a conceptual model; 2) its automatic transformation into an executable form using transformation rules, and; 3) its automatic verification using a formal analysis technique for an accurate assessment of its correctness. Both approaches have numerous complementary benefits in rapid prototyping of complex systems using model reuse. In this paper, we propose a framework grounded in a combination of component-based and model-driven approaches to promote rapid prototyping of complex systems through the effective reuse of the simulation models. Our proposed process allows developers to 1) build or select existing components and compose them to formulate the conceptual models of complex systems; 2) automatically transform the conceptual models for the rapid implementation and simulation, and; 3) automatically verify them as per the requirement specifications. We propose the use of the extended finite-state machine (EFSM) as conceptual modeling formalism, anylogic simulation platform for the implementation, and probabilistic model checking technique using communicating sequential process (CSP) formalism for the verification. Finally, we present a case study of a real-time adaptive cruise control system to demonstrate the functionality of our framework. Our proposed component-based model-driven approach facilitates rapid prototyping and effective meaningful reuse of complex system models, which further accelerates the modeling, simulation, and analysis process of real-time systems and aids in complex engineering designs and implementations.

The Opacity of Real-Time Automata

Opacity is an important property on information flow to guarantee that a system under attack keeps its “secrets”, possibly subsets of traces (language-based opacity) or subsets of states (state-based opacity), opaque to the outside intruder with partial observability. In this paper, we investigate the opacity problems of real-time automata (RTA), which is a popular model for real-time systems. In order to prove that the language-opacity problem of RTA is decidable, we introduce the notion of trace-equivalence and then translate RTA into finite-state automata (FA) with timed alphabets. Besides, we also introduce the notions of partitioned timed alphabet and language to guarantee trace equivalence is preserved by complementation and product operations over FA with timed alphabets. Thus, our decision procedure can be sketched as follows: first, translate the RTA to model a system under attack and the RTA to specify the secret behavior of the system into FA, respectively; then, compute another FA, which accepts all traces accepted by the first FA, but not by the second one; afterwards, project these FA onto the given observable set; finally, unify the alphabets of these FA such that for any two timed actions with the same event, their time parts do not have any overlap. Thus, whether the original system is language-opaque with respect to the secret RTA and the observable set is reduced to the inclusion problem of regular languages. Similarly, we can show decidability of initial-opacity of RTA.

Analytical Characterization of End-to-End Communication Delays With Logical Execution Time

Modern automotive embedded systems are composed of multiple real-time tasks communicating by means of shared variables. The effect of an initial event is typically propagated to an actuation signal through sequences of tasks writing/reading shared variables, creating an effect chain (EC). The responsiveness, performance and stability of the control algorithms of an automotive application typically depend on the propagation delays of selected ECs. Indeed, task jitter can have a negative impact on the system potentially leading to instability. The logical execution time (LET) model has been recently adopted by the automotive industry as a way of reducing jitter and improving the determinism of the system. In this paper, we provide a formal analysis of the LET model for real-time systems composed of periodic tasks with harmonic and nonharmonic periods, analytically characterizing the control performance of LET ECs. We also show that by introducing tasks offsets, the real-time performance of nonharmonic tasks may improve, getting closer to the constant end-to-end latency experienced in the harmonic case. Further, we present a heuristic algorithm to obtain a set of offsets that might reduce end-to-end latencies, improving LET communication determinism. Finally, we apply this technique to an industrial case study consisting of an automotive engine control system.

Research on real-time simulation modelling of a diesel engine based on fuel inter-zone transfer and an array calculation method

This paper proposed a real-time simulation model of a diesel engine based on the fuel transfer in the combustion zone and an array calculation method. The proposed model conducts real-time optimized modelling of the diesel engine multi-zone model from two aspects: a modelling method and a model algorithm. From the modelling method perspective, the model proposed a combustion process model that transfers fuel mass and momentum between the combustion zones. Based on the diesel combustion characteristics, this modelling method divides the cylinder into three thermodynamic zones: an unburned area, a premixed combustion zone, and a diffusion combustion zone. This division addresses the assumption that fuel mass and momentum transfer are not performed between combustion zones in the traditional multi-zone model. While reducing the computation regions and improving the simulation calculation speed, the combustion process of the premixed combustion zone and the diffusion combustion zone were described using the method of Stefan flow and entrainment combustion. The physical depth and the predictivity of the model was enhanced, which rendered the description of the combustion process more consistent with the diesel combustion characteristics. The thermodynamic calculation process of the three regions was maintained throughout the entire cyclic operation in the entire diesel engine operating cycle. From the model calculation perspective, the model proposed a direct single-step calculation method for temperature and pressure to satisfy the demand of real-time model calculation. To optimize the multi-cylinder multi-zone plan in a real-time environment, the model utilizes an array method for calculation. The results of the model verification of a diesel engine in high and low loading conditions and using fixed combustion model parameters indicate that the difference between the calculation results and the experimental data is less than 5% and the results are consistent, which proves that the model has predictivity. The analysis of the model calculation results indicates that the model can accurately reflect the performance change pattern and regional change characteristics for different working conditions and provide a suitable foundation platform for controlling system combustion performance optimization. In addition, the array algorithm and multi-zone through-operation calculation method were validated in a real-time hardware system test environment. The methods can effectively reduce the multi cylinder multi-zone model simulation time to 30%. The application range of the real-time system model was expanded; thus, this method is very suitable for real-time system calculation.

Development of a real-time steering system model for driving simulators

Driving simulators have boosted the vehicle design with the introduction of human beings in the simulation loop. For a realistic functioning, the steering system must provide an accurate behaviour, since the hand wheel is a crucial human interface. Despite a large diffusion of steering models, this paper deals with the creation of a specific solution for real-time applications, characterized by precise features as numerical stability and low computational cost. The proposed model is based on a physical structure and considers all the key phenomena, such as the system elasticities, the power steering effects and friction hysteresis, making the model more accurate in terms of steering wheel torque and lateral acceleration than other angle-driven models. Its two degrees of freedom design allows a proper behaviour of the power steering sub-model; another key aspect is the friction model: the use of the LuGre formulation greatly improves accuracy and stability in comparison to the lookup table friction models. Compared to the literature reference torque-driven model, it does not need the use of a torque sensor when implemented in driving simulators having an angle-driven formulation (the input of the steering wheel is its angle and the torque needed is its output), hence it is cheaper to implement; nevertheless, its accuracy is close to state-of-art reference. An original parametrization procedure is proposed since a generalized one is not available in literature; using a steering test-rig, all the model variables are defined. The validation phase combines offline and online simulations, assessing objectively and subjectively the model’s capabilities and showing accurate results in terms of steering wheel torque, lateral acceleration and steering feeling. In addition, a minor contribution of this paper shows how different analyses (steering effort evaluation, experimental data comparison or simulator feedback computation) require different output torques.

Zebrafish: an emerging real-time model system to study Alzheimer\u2019s disease and neurospecific drug discovery

Zebrafish (Danio rerio) is emerging as an increasingly successful model for translational research on human neurological disorders. In this review, we appraise the high degree of neurological and behavioural resemblance of zebrafish with humans. It is highly validated as a powerful vertebrate model for investigating human neurodegenerative diseases. The neuroanatomic and neurochemical pathways of zebrafish brain exhibit a profound resemblance with the human brain. Physiological, emotional and social behavioural pattern similarities between them have also been well established. Interestingly, zebrafish models have been used successfully to simulate the pathology of Alzheimer’s disease (AD) as well as Tauopathy. Their relatively simple nervous system and the optical transparency of the embryos permit real-time neurological imaging. Here, we further elaborate on the use of recent real-time imaging techniques to obtain vital insights into the neurodegeneration that occurs in AD. Zebrafish is adeptly suitable for Ca2+ imaging, which provides a better understanding of neuronal activity and axonal dystrophy in a non-invasive manner. Three-dimensional imaging in zebrafish is a rapidly evolving technique, which allows the visualisation of the whole organism for an elaborate in vivo functional and neurophysiological analysis in disease condition. Suitability to high-throughput screening and similarity with humans makes zebrafish an excellent model for screening neurospecific compounds. Thus, the zebrafish model can be pivotal in bridging the gap from the bench to the bedside. This fish is becoming an increasingly successful model to understand AD with further scope for investigation in neurodevelopment and neurodegeneration, which promises exciting research opportunities in the future.

Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification

In order for traffic management and information systems to provide proper traffic flow, it is necessary to obtain information about traffic with the help of various sensors. In this context, in recent years the use of video cameras in traffic observation and control has become very widespread and actively used. Numerous studies such as license plate recognition, vehicle number finding, traffic intensity determination, vehicle speed calculation, band violation and vehicle classification can be done with the help of video processing based video monitoring systems. Traffic surveillance videos are very actively used for this purpose. In this paper, we have developed a system that classifies vehicles according to their type. Firstly we create a vehicle dataset from an uncalibrated camera. Then, we test Tiny-YOLO real-time object detection and classification system and support vector machine (SVM) classifier model on our dataset and well-known public BIT-Vehicle dataset in terms of recall, precision, and intersection over union performance metrics. Experimental results show that two methods can be used to classify real-time streaming traffic video data.

A Novel Approach to Modeling and Verifying Real-Time Systems for High Reliability

This paper proposes a novel approach to modeling and verifying real-time systems for high reliability. To do so, we first extend projection temporal logic to timed projection temporal logic. Further, we define a timed modeling, simulation, and verification language (TMSVL) for real-time systems. As a result, both systems and desired properties can be expressed in TMSVL. In particular, real-time behaviors such as delay, timeout, and interrupt can be formalized. Compared with commonly used property specification language, TMSVL is capable of specifying more sophisticated properties such as quantitative timing properties, interval-related properties, and periodically repeated properties. Moreover, the unified model checking approach to verifying real-time systems via dynamical program execution is implemented. In addition, a case study for modeling and verifying a $\mu$ C/OS-III multitask system with interrupt is conducted to demonstrate how the proposed approach works.