Modeling Of Real-time Systems Research Articles

Improving visibility forecasting during haze-fog processes in shanghai and eastern China: The significance of aerosol and hydrometeor extinction

Aerosols and droplets are the main factors of visibility reduction by scattering and absorbing light. In this study we use the 3-D meteorology fields from the 9-km WRF-ADAS Real-Time Modeling System (WARMS) to drive the Community Multiscale Air Quality (CMAQ) model. A visibility forecasting framework is developed by combining extinction due to hydrometeors and aerosols, based on WARMS and CMAQ predictions. We have analyzed the results of a one-month forecasting period during the winter of 2021–2022 to assess the performance of the WARM-CMAQ model and to understand the impact of hydrometeor and aerosol extinction on operational visibility forecasting in Shanghai, the Yangtze River Delta (YRD), and part of the North China Plain (NCP). With our newly developed framework, we generally find improved or comparable results in visibility forecasts when compared to recent studies. We find that for the city of Shanghai, aerosol extinction has a minor impact on the model's performance when forecasting visibility below 1 km, but it becomes crucial for predictions spanning 1–10 km. Comparison against observations shows that the framework well captures the general contributions from various chemical constituents with nitrate as the most important factor in aerosol extinction (∼60%). Finally, our assessment of the YRD and part of the NCP highlights that in highly polluted areas aerosols could be significant for visibility below 1 km. This study emphasizes the necessity of integrating both aerosol and hydrometeor extinction in visibility forecasting during haze-fog processes, particularly for regions characterized by diverse pollutant levels and environmental conditions.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

Domestic Cats Facial Expression Recognition Based on Convolutional Neural Networks

Despite extensive research on Facial Expression Recognition (FER) in humans using deep learning technology, significantly less focus has been placed on applying these advancements to recognize facial expressions in domestic animals. Recognizing this gap, our research aims to extend FER techniques specifically to domestic cats, one of the most popular domestic pets. In this paper, we present a real-time system model that employs deep learning to identify and classify cat facial expressions into four categories: Pleased, Angry, Alarmed, and Calm. This innovative model not only helps cat owners understand their pets' behavior more accurately but also holds substantial potential for applications in domestic animal health services. By identifying and interpreting the emotional states of cats, we can address a critical need for improved communication between humans and their pets, fostering better care and well-being for these animals. To develop this system, we conducted extensive experiments and training using a diverse dataset of cat images annotated with corresponding facial expressions. Our approach involved using convolutional neural networks (CNNs) to analyze and learn from the subtleties in feline facial features by investigating the models' robustness considering metrics such as accuracy, precision, recall, confusion matrix, and f1-score. The experimental results demonstrate the high recognition accuracy and practicality of our model, underscoring its effectiveness. This research aims to empower pet owners, veterinarians, and researchers with advanced tools and insights, ensuring the well-being and happiness of domestic cats. Ultimately, our work highlights the potential of FER technology to significantly enhance the quality of life for cats by enabling better understanding and more responsive care from their human companions

The MATERIAL framework: Modeling and AuTomatic code Generation of Edge Real-TIme AppLications under the QNX RTOS

Modern edge real-time automotive applications are becoming more complex, dynamic, and distributed, moving away from conventional static operating environments to support advanced driving assistance and autonomous driving functionalities. This shift necessitates formulating more complex task models to represent the evolving nature of these applications aptly. Modeling of real-time automotive systems is typically performed leveraging Architectural Languages (ALs) such as Amalthea, which are commonly used by the industry to describe the characteristics of processing platforms, operating systems, and tasks. However, these architectural languages are originally derived for classical automotive applications and need to evolve to meet the needs of next-generation applications. This paper proposes an automatic framework for the modeling and automatic code generation of dynamic automotive applications under the QNX RTOS. To this end, we extend Amalthea to describe chains of communicating tasks with multiple operating modes and to consider the QNX’s reservation-based scheduler, called APS, which allows providing temporal isolation between applications co-located on the same hardware platform. Finally, an evaluation is presented to compare different implementation alternatives under QNX that are automatically generated by our code generation framework.

Advanced Deep Learning Methodologies in the Diagnosis of Parkinson's Disease: A Comprehensive Review

This study of the literature explores the field of sophisticated deep techniques for diagnosing Parkinson's illness and severity evaluation. The research looks into the use of machine learning algorithms as potential markers of Parkinson's illness in manual illustrations and speech impairments. These algorithms include XGBoost, Neural Networks with Recurrence, and ensemble models. Numerous feature extraction techniques, model comparison assessments, and preprocessing methodologies are investigated in an effort to improve precision and efficacy of Parkinson's illness diagnosis. In order to improve performance and interpretability of the prototype, the importance of feature engineering, ensemble learning techniques, and interpretability techniques like LIME and SHAP values is emphasized. The study also highlights how crucial it is to integrate several models for real-time monitoring systems to assist in the prompt identification and ongoing tracking of Parkinson's illness. Future possibilities for examine include adding other symptoms including handwriting distortion and olfactory sound loss, investigating new algorithms, and using deep learning approaches to improve system performance. Considerable progress in Parkinson's disease diagnosis and therapy can be made by filling up these research gaps and utilizing deep learning. By addressing these problems via additional study and technical developments, PD can be detected and managed earlier and more effectively, which will ultimately improve patient quality of life. Keywords: Keywords: interpretability, early diagnosis, severity assessment, ensemble learning, XGBoost, deep learning, recurrent neural networks,, machine learning, Parkinson's illness, and feature engineering.

Characterizing Parameter Scaling with Quantization for Deployment of CNNs on Real-Time Systems

Modern deep-learning models tend to include billions of parameters, reducing real-time performance. Embedded systems are compute-constrained while frequently used to deploy these models for real-time systems given size, weight, and power requirements. Tools like parameter-scaling methods help to shrink models to ease deployment. This research compares two scaling methods for convolutional neural networks, uniform scaling and NeuralScale, and analyzes their impact on inference latency, memory utilization, and power. Uniform scaling scales the number of filters evenly across a network. NeuralScale adaptively scales the model to theoretically achieve the highest accuracy for a target parameter count. In this study, VGG-11, MobileNetV2, and ResNet-50 models were scaled to four ratios: 0.25×, 0.50×, 0.75×, 1.00×. These models were benchmarked on an ARM Cortex-A72 CPU, an NVIDIA Jetson AGX Xavier GPU, and a Xilinx ZCU104 FPGA. Additionally, quantization was applied to meet real-time objectives. The CIFAR-10 and tinyImageNet datasets were studied. On CIFAR-10, NeuralScale creates more computationally intensive models than uniform scaling for the same parameter count, with relative speeds of 41% on the CPU, 72% on the GPU, and 96% on the FPGA. The additional computational complexity is a tradeoff for accuracy improvements in VGG-11 and MobileNetV2 NeuralScale models but reduced ResNet-50 NeuralScale accuracy. Furthermore, quantization alone achieves similar or better performance on the CPU and GPU devices when compared with models scaled to 0.50×, despite slight reductions in accuracy. On the GPU, quantization reduces latency by 2.7× and memory consumption by 4.3×. Uniform-scaling models are 1.8× faster and use 2.8× less memory. NeuralScale reduces latency by 1.3× and dropped memory by 1.1×. We find quantization to be a practical first tool for improved performance. Uniform scaling can easily be applied for additional improvements. NeuralScale may improve accuracy but tends to negatively impact performance, so more care must be taken with it.

Actor modeling of real-time cognitive systems: ontological basis and software-mathematical implementation

The article is devoted to the study of the problem of increasing the reliability of modeling of cognitive systems, to which the authors refer not only human intelligence, but also artificial intelligence systems, as well as intelligent control systems for production, technological processes and complex equipment. It is shown that the use of cognitive systems for solving control problems causes very high rapidity requirements for them. These requirements combined with the necessity to simplify modeling methods as the modeling object becomes more complex determine the choice of an approach to modeling cognitive systems. Models should be based on the use of simple algorithms in the form of trend detection, correlation, as well as (for solving intellectual problems) on the use of algorithms based on the application of various patterns of forms and laws. In addition, the models should be decentralized. An adequate representation of decentralized systems formed from a large number of autonomous elements can be formed within the framework of agent-based models. For cognitive systems, two models are the most elaborated: actor and reactor models. Actor models of cognitive systems have two possible realizations: as an instrumental model or as a simulation. Both implementations have the right to exist, but the possibilities of realizing a reliable description when using the tool model are higher, because it provides incommensurably higher rapidity, and also assumes variability of the modeled reality. The actor model can be realized by means of a large number of existing programming languages. The solution to the problem of creating simulative actor models is available in most languages that work with actors. Realization of instrumental actor models requires rapidity, which is unattainable in imperative programming. In this case, the optimal solution is to use actor metaprogramming. Such programming is realizable in many existing languages.

State-based opacity of labeled real-time automata

State-based opacity is a special type of opacity as a confidentiality property, which describes whether an external intruder cannot be sure whether secret states of a system have been visited by observing its generated outputs, given that the intruder knows complete knowledge of the system's structure but can only see generated outputs. When the time of visiting secret states is specified as the initial time, the current time, any past time, and at most K observational steps prior to the current time, the notions of state-based opacity can be formulated as initial-state opacity, current-state opacity, infinite-step opacity, and K-step opacity, respectively. In this paper, we formulate the four versions of opacity for labeled real-time automata which are a widely-used model of real-time systems, and give N2EXPTIME verification algorithms for the four notions by defining appropriate notions of observer and reverse observer for labeled real-time automata that are proven to be computable in N2EXPTIME.

Dynamic system modeling and simulation of a power-to-methanol process based on proton-conducting tubular solid oxide cells

The importance of methanol as a basic building block of the chemical industry and as a means of chemical energy storage of renewable energy sources (e.g. wind & PV) will steadily increase in the upcoming years. Based on renewable electricity and through the coupling of a proton-conducting steam electrolyzer for the generation of pure H2 with a heterogeneously catalyzed direct synthesis of methanol from anthropogenic CO2, an attractive method for the production of methanol can be provided. The efficient and economic application and operation of these so-called power-to-methanol processes requires suitable system control and heat integration concepts for alternating operating conditions. In this work, a transient and real-time capable system model of a power-to-methanol process based on tubular proton-conducting high temperature electrolyzers is presented. The obtained stationary simulation results reveal beneficial operational windows and system efficiencies (0.488 to 0.637) with respect to the chosen process design and heat integration concept. The power-to-methanol process model also incorporates a multitude of feedback control loops or controllers, to manipulate relevant operating parameters of all employed sub-processes in case of fluctuating power inputs. Furthermore, the presented studies assess the transient responses of the power-to-methanol system to defined step changes of the apparent cell voltage based on a multitude of negative feedback control loops.

Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters

Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring predictions remain consistent with fundamental principles, which is crucial when dealing with scientific phenomena. This study focuses on optimizing the application of this specialized network for simultaneous system dynamics simulations and learning time-varying parameters, particularly when the number of unknowns in the system matches the number of undetermined parameters. Additionally, we explore scenarios with a mismatch between parameters and equations, optimizing network architecture to enhance convergence speed, computational efficiency, and accuracy in learning the time-varying parameter. Our approach enhances the algorithm’s performance and accuracy, ensuring optimal use of computational resources and yielding more precise results. Extensive experiments are conducted on four different dynamical systems: first-order irreversible chain reactions, biomass transfer, the Brusselsator model, and the Lotka-Volterra model, using synthetically generated data to validate our approach. Additionally, we apply our method to the susceptible-infected-recovered model, utilizing real-world COVID-19 data to learn the time-varying parameters of the pandemic’s spread. A comprehensive comparison between the performance of our approach and fully connected deep neural networks is presented, evaluating both accuracy and computational efficiency in parameter identification and system dynamics capture. The results demonstrate that the physics-informed neural networks outperform fully connected deep neural networks in performance, especially with increased network depth, making them ideal for real-time complex system modeling. This underscores the physics-informed neural network’s effectiveness in scientific modeling in scenarios with balanced unknowns and parameters. Furthermore, it provides a fast, accurate, and efficient alternative for analyzing dynamic systems.

A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge

Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards it

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

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

First Steps towards a near Real-Time Modelling System of Vibrio vulnificus in the Baltic Sea.

Over the last two decades, Vibrio vulnificus infections have emerged as an increasingly serious public health threat along the German Baltic coast. To manage related risks, near real-time (NRT) modelling of V. vulnificus quantities has often been proposed. Such models require spatially explicit input data, for example, from remote sensing or numerical model products. We tested if data from a hydrodynamic, a meteorological, and a biogeochemical model are suitable as input for an NRT model system by coupling it with field samples and assessing the models' ability to capture known ecological parameters of V. vulnificus. We also identify the most important predictors for V. vulnificus in the Baltic Sea by leveraging the St. Nicolas House Analysis. Using a 27-year time series of sea surface temperature, we have investigated trends of V. vulnificus season length, which pinpoint hotspots mainly in the east of our study region. Our results underline the importance of water temperature and salinity on V. vulnificus abundance but also highlight the potential of air temperature, oxygen, and precipitation to serve as predictors in a statistical model, albeit their relationship with V. vulnificus may not be causal. The evaluated models cannot be used in an NRT model system due to data availability constraints, but promising alternatives are presented. The results provide a valuable basis for a future NRT model for V. vulnificus in the Baltic Sea.

COVID-19 Pandemi Önlemleri Altında Çalışan Üretim Tesisleri için Gerçek

By reason of the COVID-19 pandemic, essential digital transformations are taking place in many areas of business life. Although the most important one of these transformations is due to the widespread use of the remote working model, the production sector does not have the opportunity to switch to such a model completely. Therefore, it is inevitable to maintain social distance to prevent the spread of COVID-19 while working in production facilities. In this study, a real-time location system (RTLS) model is proposed to keep track of social distance in production facilities and to ensure occupational health safety (OHS) at the same time. Since the social distance rule is essential for every production facility, the most important feature of the proposed system is that it can easily be integrated into the standard personnel tracking system in almost every enterprise. Besides, the proposed RTLS is designed as an efficient system based on ultra-wideband and radio-frequency identification, which can operate as a closed-loop monitoring system within itself. An adequately installed RTLS can monitor the position of employees in real-time and provides to intervene in the situation instantly when necessary. In case of a violation of social distance or a situation against OHS, it can be prevented instantly by the proposed system. It is also a useful model in the management of emergencies.

Evolving scheduling heuristics with genetic programming for optimization of quality of service in weakly hard real-time systems

The weakly hard real-time system model is used for describing the real-time systems that allow occasional violations of real-time timing constraints. These systems include real-time control systems, multimedia systems, and communication systems. In some approaches that deal with mitigating the system overload in real-time systems with periodic tasks, namely job-skipping algorithms, the constraints defined by the weakly hard real-time model are used as a mechanism for defining the pattern of task instances (jobs) that may be skipped in order to reduce the system load. The performance of these algorithms is usually evaluated with respect to the quality of service metric, which depends on the number of skipped jobs. In this work, we investigate the possibility of using genetic programming in the automated synthesis of scheduling heuristics for optimizing skipping patterns in order to increase the average quality of service in comparison with the conventional job-skipping algorithms. Using genetic programming to automatically synthesize heuristics allows for an easy and quick design of novel heuristics for various problem types and optimization criteria. We present two different approaches for implementing the proposed method. The first approach is to encapsulate the evolved heuristics into job-skipping algorithms known from the literature, namely Red Tasks as Late as Possible (RLP) and Blue When Possible (BWP). The idea of the second approach is to employ the evolved heuristics as standalone job-skipping algorithms. The results show an improvement of up to 15% in comparison with the state-of-the-art algorithms. The novel methods described in this work present a significant upgrade of the standard job-skipping algorithms as they provide a notable improvement in terms of quality of service while ensuring the fulfillment of weakly hard constraints. Moreover, the presented methods are computationally efficient and are therefore suitable for implementation on real-time operating systems, which is not the case with similar methods based on optimization techniques.

Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects

Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time urban flood forecasting systems for flood disaster prevention authorities and the urban public. This paper reviewed the state-of-the-art models of real-time forecasting systems for urban flash floods. The real-time system basically consists of the following subsystems, i.e., rainfall forecasting, drainage system modelling, and inundation area mapping. This paper summarized the recent radar data utilization methods for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. This paper also dealt with available technologies for modelling, e.g., digital surface models (DSMs) for the finer urban terrain of drainage systems. The review indicated that an obstacle to using process-based hydraulic models was the limited computational resources and shorter lead time for real-time forecasting in many urban areas in tropical Southeast Asia. The review further discussed the prospects of data-driven AI models for real-time forecasting systems.

Practical Study of Recurrent Neural Networks for Efficient Real-Time Drone Sound Detection: A Review

The detection and classification of engine-based moving objects in restricted scenes from acoustic signals allow better Unmanned Aerial System (UAS)-specific intelligent systems and audio-based surveillance systems. Recurrent Neural Networks (RNNs) provide wide coverage in the field of acoustic analysis due to their effectiveness in widespread practical applications. In this work, we propose to study SimpleRNN, LSTM, BiLSTM, and GRU recurrent network models for real-time UAV sound recognition systems based on Mel-spectrogram using Kapre layers. The main goal of the work is to study the types of RNN networks in a practical sense for a reliable drone sound recognition system. According to the results of an experimental study, the GRU (Gated Recurrent Units) network model demonstrated a higher prediction ability than other RNN architectures for detecting differences and the state of objects from acoustic signals. That is, RNNs gave higher recognition than CNNs for loaded and unloaded audio states of various UAV models, while the GRU model showed about 98% accuracy for determining the UAV load states and 99% accuracy for background noise, which consisted of more other data.

Modeling and Analysis of Probabilistic Real-time Systems through Integrating Event-B and Probabilistic Model Checking

Event-B is a formal method used in the development of safety critical systems. However, these systems may introduce uncertainty, and need also to meet real-time requirements, which make their modeling and analysis a challenging task. Existing works on extending Event-B with probability and time did not address both probability and time in a single framework. Besides, they did focus the most on extending the language itself, not on integrating the extended Event-B with verification. In this paper, we aim to represent both probability and time in the Event-B language, and we will show how such a representation can be automatically translated into Probabilistic Timed Automata (PTA) described in the language of the probabilistic model checker PRISM. This translation would allow us to analyze probabilistic, as well as time-bounded probabilistic reachability properties of probabilistic real-time systems through the Probabilistic Timed CTL (PTCTL) logic.

The development and validation of a dashboard prototype for real-time suicide mortality data.

Introduction/AimData visualisation is key to informing data-driven decision-making, yet this is an underexplored area of suicide surveillance. By way of enhancing a real-time suicide surveillance system model, an interactive dashboard prototype has been developed to facilitate emerging cluster detection, risk profiling and trend observation, as well as to establish a formal data sharing connection with key stakeholders via an intuitive interface.Materials and MethodsIndividual-level demographic and circumstantial data on cases of confirmed suicide and open verdicts meeting the criteria for suicide in County Cork 2008–2017 were analysed to validate the model. The retrospective and prospective space-time scan statistics based on a discrete Poisson model were employed via the R software environment using the “rsatscan” and “shiny” packages to conduct the space-time cluster analysis and deliver the mapping and graphic components encompassing the dashboard interface.ResultsUsing the best-fit parameters, the retrospective scan statistic returned several emerging non-significant clusters detected during the 10-year period, while the prospective approach demonstrated the predictive ability of the model. The outputs of the investigations are visually displayed using a geographical map of the identified clusters and a timeline of cluster occurrence.DiscussionThe challenges of designing and implementing visualizations for suspected suicide data are presented through a discussion of the development of the dashboard prototype and the potential it holds for supporting real-time decision-making.ConclusionsThe results demonstrate that integration of a cluster detection approach involving geo-visualisation techniques, space-time scan statistics and predictive modelling would facilitate prospective early detection of emerging clusters, at-risk populations, and locations of concern. The prototype demonstrates real-world applicability as a proactive monitoring tool for timely action in suicide prevention by facilitating informed planning and preparedness to respond to emerging suicide clusters and other concerning trends.

Improving Interference Analysis for Real-Time DAG Tasks Under Partitioned Scheduling

Real-time systems with strict timing constraints have been widely applied in many fields. The Directed acyclic graph (DAG) task model has been widely studied and applied to model real-time systems with partial parallelism and precedence constraints in each task. Our paper focuses on the worst-case response time (WCRT) analysis of DAG tasks under partitioned scheduling on multiprocessors. We investigate a parallel structure named <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$Str$</tex> </formula> , which helps obtain more accurate analysis results, and propose a new offline scheduling analysis algorithm named reducing repetitive calculation (RRC). Experiments with synthetic workload are conducted to compare the results calculated by RRC and the state-of-the-art, as well as the observed average response time on a real embedded system. Results show that RRC has better performance in terms of analysis accuracy.