Model-based Performance Self-adaptation

Reliability growth models for hardware and software systems based on nonhomogeneous Poisson processes: A survey

The following chapter can be understood as one sort of brief introduction to the history and basics of the Hidden Markov Models. Hidden Markov Models (HMMs) are learnable finite stochastic automates. Nowadays, they are considered as a specific form of dynamic Bayesian networks. Dynamic Bayesian networks are based on the theory of Bayes (Bayes & Price, 1763). A Hidden Markov Model consists of two stochastic processes. The first stochastic process is a Markov chain that is characterized by states and transition probabilities. The states of the chain are externally not visible, therefore “hidden”. The second stochastic process produces emissions observable at each moment, depending on a state-dependent probability distribution. It is important to notice that the denomination “hidden” while defining a Hidden Markov Model is referred to the states of the Markov chain, not to the parameters of the model. The history of the HMMs consists of two parts. On the one hand there is the history of Markov process and Markov chains, and on the other hand there is the history of algorithms needed to develop Hidden Markov Models in order to solve problems in the modern applied sciences by using for example a computer or similar electronic devices.

This paper summarizes our experiences while teaching the Models of Software Systems (MSS) course as part of the MSIT-SE master programme at Carnegie Mellon University (CMU), in Pittsburgh, PA, USA, and at Innopolis University (INNO), in Tatarstan, Russia. The MSS is a Formal Methods course taught to Software Engineers in the Fall of every year. The author was a visiting Faculty to CMU in the Fall of 2015, who then returned to Russia in the Fall of 2016 to replicate the same master course at INNO. The goal of the MSIT-SE master program at CMU as well as at INNO is to create software company leaders in the field of Software Engineering. The MSS course exposes students to several Formal Methods techniques and models, including first order logic, state machines, concurrency models and temporal logic. This paper reports our efforts to improve the quality of the MSS course by introducing the teaching of the Event-B language and its underlying software development methodology into the course programme. Our long time goal is to make Formal Methods (FM) and FM techniques more attractive to millennials and to demonstrate how they can effectively use those techniques to ensure reliability of the software systems employed by them in their IT companies.

Real-time process algebra (RTPA) is a denotational mathematical structure for denoting and manipulating system behavioral processes. RTPA is designed as a coherent algebraic system for intelligent and software system modeling, specification, refinement, and implementation. RTPA encompasses 17 metaprocesses and 17 relational process operations. RTPA can be used to describe both logical and physical models of software and intelligent systems. Logic views of system architectures and their physical platforms can be described using the same set of notations. When a system architecture is formally modeled, the static and dynamic behaviors performed on the architectural model can be specified by a three-level refinement scheme at the system, class, and object levels in a top-down approach. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.

Software systems start small and grow in complexity and size. The larger a software system is, the more it is distributed over organizational and geographical confines. Thus, the modeling of software systems is necessary at a certain level of complexity because it can be used for communication, documentation, configuration and certification purposes. We came to the conclusion that several patterns of software modeling exist. The existence of such patterns is dependent on the history and the evolution of the system under consideration. We will show that a software system in its lifecycle has to face several crises. Such a crisis is a watershed in the application of new patterns. We provide an evolutionary view of software systems and models which helps understanding of current problems and prospective solutions.

In this paper we consider Markov analysis of models of complex software and hardware systems. A Markov analysis tool can be used during verification processes of models of avionics systems. In the introduction we enumerate main advantages and disadvantages of Markov analysis. For example, with Markov analysis, unlike other approaches, such as fault tree analysis and dependency diagram analysis, it is possible to analyze models of systems that are able to recovery. The main drawback of this approach is an exponential growth of models size with number of components in analyzed system. It makes Markov analysis barely used in practice. The other important problem is to develop a new algorithm for translating a model of a system to a model suitable for Markov analysis (Markov chain), since the existing solutions have significant limitations on the architecture of analyzed systems. Next we give a brief description of the context - AADL modeling language with Error Model Annex library, MASIW framework, and also give an explanation of Markov analysis method. In a main section we suggest an algorithm for translating a system model into a Markov chain, partially solving the problem of exponential growth of Markov chain. Then follows a description of further steps, and some heuristics that allow to extremely reduce running time of the algorithm. In this paper we also consider other Markov analysis tools and their features. As a result, we suggest a Markov analysis tool that can be effectively use in practice.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

The available state-based and path-based reliability models for the component-based software consume high computational cost and suffer from the evaluating complexity in the software evaluation. A SVM reliability evaluation model for component-based software systems (SVMREM) with a low complexity is presented in this paper. The component reliability SVM is performed to optimize the reliability evaluation time consumption. SVMREM has a low evaluation complexity and outperforms the state-based model and the path-based model of the reliability evaluation. Another advantage of SVMREM over the others is its software structure robustness. SVMREM only depends on the component historical reliabilities, which are independent from the software system structure. Based on the theory analysis and experiment results, it shows that the SVMREM reliability evaluating accuracy is acceptable when the software system structure is complex and the complexity of SVMREM is evidently lower than the contrast models.

Part I of the thesis investigates novel urban traffic state estimation methods utilizing probe vehicle data. Chapter 2 proposes a method to integrate the collective effect of dispersed probe data with traffic kinematic wave theory and data mining techniques to model the spatial and temporal dynamics of queue formation and dissipation in arterials. The queue estimation method captures interdependencies in queue evolutions of successive intersections, and moreover, the method is applicable in oversaturated conditions and includes a queue spillover statistical inference procedure. Chapter 3 develops a travel time reliability model to estimate arterial route travel times distribution (TTD) considering spatial and temporal correlations between traffic states in consecutive links. The model uses link-level travel time data and a heuristic grid clustering method to estimate the state structure and transition probabilities of a Markov chain. By applying the Markov chain procedure, the correlation between states of successive links is integrated and the route-level TTD is estimated. The methods in Part I are tested with various probe vehicle penetration rates on case studies with field measurements and simulated data. The methods are straightforward in implementation and have demonstrated promising performance and accuracy through numerous experiments. Part II studies network-level modeling and control of large-scale urban networks. Chapter 4 is the pioneer that introduces the urban perimeter control for two-region urban cities as an elegant control strategy to decrease delays in urban networks. Perimeter controllers operate on the border between the two regions, and manipulate the percentages of transfer flows between the two regions, such that the number of trips reaching their destinations is maximized. The optimal perimeter control problem is solved by the model predictive control (MPC) scheme, where the prediction model and the plant (reality) are formulated by macroscopic fundamental diagrams (MFD). Chapter 5 extends the perimeter control strategy and MFD modeling to mixed urban-freeway networks to provide a holistic approach for large-scale integrated corridor management (ICM). The network consists of two urban regions, each one with a well-defined MFD, and a freeway, modeled by the asymmetric cell transmission model, that is an alternative commuting route which has one on-ramp and one off-ramp within each urban region. The optimal traffic control problem is solved by the MPC approach to minimize total delay in the entire network considering several control policies with different levels of urban-freeway control coordination. Chapter 6 integrates traffic heterogeneity dynamics in large-scale urban modeling and control to develop a hierarchical control strategy for heterogeneously congested cities. Two aggregated models, region- and subregion-based MFDs, are introduced to study the effect of link density heterogeneity on the scatter and hysteresis of MFD. A hierarchical perimeter flow control problem is proposed to minimize the network delay and to homogenize the distribution of congestion. The first level of the hierarchical control problem is solved by the MPC approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant is the subregion-based MFD, which is a more detailed model. At the lower level, a feedback controller tries to maximize the network outflow, by increasing regional homogeneity.

Model predictive control for BioPower combined heat and power (CHP) plant

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

A topic of interest in financial mathematics is the Black-Scholes model. However, the underlying asset price in the stock market may not be satisfied by this linear model, which was developed under a number of assumptions, including liquidity and the absence of transaction costs. The linear model has restricted its precision in actual market conditions. We study the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model. Using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Deep Learning (DL) is an advanced technique of machine learning solves the converted ordinary differential equation by fully connected neural network (FCNN). By modelling the complex and nonlinear relationships among market variables, DL models can generate option pricing forecasts that are more dependable and precise, not just for continuous data but also for discontinuous data (at jump point). Introduction: Nonlinear partial differential equation plays a crucial role in financial modelling, especially in the pricing of derivatives like options. The Black- Scholes model, introduced in 1973, continues to be one of the most widely used frameworks for pricing European options. However, this model is a linear one and offers an analytical solution, yet it is not appropriate for the complexities of real market assumptions that exhibit nonlinear effects. We examine the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model created by Seelama et al. (2021). First using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Solves the converted ordinary differential equation by Deep Learning (DL) based fully connected neural network (FCNN) algorithm. This algorithm is capable of handling the nonlinear behaviour of model and produce more accurate option value for European call. Objectives: Find the solution of more realistic nonlinear model of Black-Scholes equation include transaction cost in illiquid market with deep learning algorithm for a European call option. Methods: From extended nonlinear Black-Scholes model, the nonlinear model of transaction cost in illiquid market is considered for study. The nonlinear partial differential equation is converted into a nonlinear ordinary differential equation by semi-discretization finite difference method. DL is a sophisticated machine learning technique that solves transformed ordinary differential equations using fully connected neural network (FCNN) algorithm. DL algorithm uses a Python program. Results: For European call option, option values are predicted for different number of neurons with different loss functions like MSE and MAE at the time of maturity. Graphical representation shows the accuracy of the algorithm at continuous as well as at jump point (strike price). Conclusions: The method of solving a complex nonlinear partial differential equation by transforming it into a nonlinear ordinary differential equation is valuable. More precise pricing estimates for option values with nonlinear effects in financial data could be improved by deep learning.

A Model Predictive Control (MPC) strategy has been suggested and simulated with the empirical dynamic data collected on the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy, in Morgantown, WV. The HyPer facility is able to simulate gasifier/fuel cell power systems and uses hardware-based simulation approach that couples a modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. Dynamic data was collected by operating the HyPer facility continuously during five days. Bypass valves along with electric load of the system were manipulated and variables such as mass flow, turbine speed, temperature, pressure, among others were recorded for analysis. This work was developed by focusing on a multivariable recursive system identification structure fitting measured transient data. The results showed that real-time or online data is a viable means to provide a dynamic model for controller design. The excursion dynamic data collected between the setup changes of the experiments was processed off-line to determine the feasibility of applying an adaptive Model Predictive Control strategy. One of the strengths of MPC is that it can allow the designer to impose strict limits on inputs and outputs in order to keep the system within known safe bounds. Two identification structures, ARX and a State-Space model, were used to fit the measured data to dynamic models of the HyPer facility. The State-Space identification was very accurate with a second order model. Visual inspection of the tracking accuracy shows that the ARX approach was approximately as accurate as the State-Space structure in its ability to reproduce measured data. However, by comparing the Loss Function and the FPE parameters, the State-Space approach gives better results. The MPC proved to be a good strategy to control the HyPer facility. The airflow valves and the electric load were used to control the turbine speed and the cathode airflow. For the ARX/State Space models, the MPC was very robust in tracking set-point variations. The anticipation feature of the MPC was revealed to be a good tool to compensate time delays in the output variables of the facility or to anticipate eventual set-point moves in order to achieve the objectives very quickly. The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate FC heat effluent disturbances. Different off-design scenarios of operation have been tested to confirm the estimated implementation behavior of the plant-controller dynamics.

Phreatic surface flow from a near-reservoir saturated tongue

The paper examined the problem of modeling and design of software computer systems. The article briefly describes the content aspects of the various stages of the implementation of software complexes and systems. The methodology of research and analysis of systems, which uses the method of computer simulation modeling, is given. The aim of the work is to create a new universal approach to the design and implementation of software systems of various levels of complexity. During the study, the following general scientific methods were used: system analysis, synthesis, formalization, modeling, decomposition, structuring, description, comparison. The materials used in the study were: computers and software development tools. As a result of the study proposed a universal agent-module approach to the design and implementation of software systems based on simulation agent-based modeling. The study presents the processes of modeling and designing software systems, their stages, and the possibility of their integration within the described approach. Examples of formal model and structure of the software system under design based on the agent-module approach are presented. The proposed approach can be used in the process of software development at the initial stages related to modeling and designing of software systems. Integration of modeling and design stages in the proposed approach will significantly reduce software development time and simplify the design process by making it clearer and more formalized. Materials of the article are of scientific and practical value for a wide range of specialists interested in system analysis, modeling, design and implementation of software systems.