An approximation-based incremental SMT approach for diagnosability analysis of real-time systems

The development of an engineering tool (in the form of a computer model) for solving design and analysis problems related with oil and gas well production operations is discussed. The development of the method is based on integrating the concepts of "Systems Analysis" with the techniques of "Computer Graphics". The concepts behind the method are very general in nature. This paper, however, illustrates the application of the method in solving gas well completion design problems. The use of the method will save time and improve the efficiency of such design and analysis problems. The method can be extended to other design and analysis aspects of oil and gas wells.

An incremental approach for architectural modeling and analysis of real-time concurrent systems is presented. The approach integrates existing formal methods, more specifically time Petri nets and real-time computational tree logic, and leverages their complementary strengths in a way that allows us to systematically enforce that architectural design meets the system's timing requirements, and to incrementally verify the conformance. Consequently, our approach is able to provide better assurance to system design and yet reduce the complexity of analysis. The approach is based on a Real-time Architectural Specification (RAS) model, which provides a formal basis to systematically maintain a correlation between the (timing) requirements of a system and its architectural design. Based on RAS, we further present a method to verify timing properties of a system design. This method helps conquer the complexity of analysis in two dimensions. Horizontally at each design level, incremental verification is achieved by introducing TPN reduction rules that allow us to compose analysis results on individual system components. Vertically across design levels, incremental verification is achieved by propagating higher-level constraints to lower-level designs so that we can safely plug a component design into a high-level architecture without having to re-verify the entire model. A naval command and control (C2) system is used throughout the paper to demonstrate the concept and usability of our approach.

The focus of this paper is on the time dependent availability modeling and analysis of repairable multi component systems. Analytical techniques become very complicated and unrealistic especially for modern complex systems. There have been attempts in the literature to evolve more realistic techniques using simulation approach for availability analysis of systems. This paper proposes a hybrid approach called as Markov System Dynamics (MSD) approach which combines the Markov approach with system dynamics simulation approach for the time dependent availability analysis and to study the dynamic behavior of systems. To the best of the authors' knowledge, in the published literature the authors generally assume that repairable systems reach their steady state when operational time reaches infinity. However, in practical situations it is important to know at what time the steady state begins. Therefore, another objective of this paper is to evaluate the time at which repairable multi component systems reach their steady state of operations. The proposed framework is illustrated for multi component systems such as a three component system with one component on standby with a numerical example. The results of the simulation when compared with that obtained by traditional Markov analysis clearly validate the Markov System Dynamics (MSD) approach as an alternative approach for the time dependent availability analysis.

Systems Analysis Approach to Modelling of Surface and Ground Water Resources

Current energy reports confirm the steadily dilating gap between available conventional energy resources and future energy demand. This gap results in increasing energy costs and has become a determining factor in economies. Hence, politics, industry, and research focus either on regenerative energy resources or on energy-efficient concepts, methods, and technologies for energy-consuming devices. A remaining challenge is energy optimization of complex systems during their operation time. In addition to optimization measures that can be applied in development and engineering, the generation of optimization measures that are customized to the specific dynamic operational situation, promise high-cost saving potentials. During operation time, the systems are located in unique situations and environments and are operated according to individual requirements of their users. Hence, in addition to complexity of the systems, individuality and dynamic variability of their surroundings during operation time complicate identification of goal-oriented optimization measures. This contribution introduces a model-based approach for user-centric energy cost analysis of industrial automation systems. The approach allows automated generation and appliance of individual optimization proposals. Focus of this paper is on a basic variant for a single industrial automation system and its operational parameters.

This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.

A new approach for dynamic analysis of flexible manipulator systems

A Galerkin/neural approach for the stochastic dynamics analysis of nonlinear uncertain systems

We present a unified approach for the stability analysis of impulsive hybrid systems. The approach is composed of two key steps. The first step is to group a number of modes of the hybrid system together with the corresponding intervals and reset maps as units. This can be done by analyzing the discrete property of an impulsive hybrid system. The second step is to find an operation to combine each unit together. We show that the system is stable if the Lyapunov function is non-increasing along each unit of the system. In particular, we use the approach to analyze the stability of impulsive differential systems, sampled-data control systems and impulsive switched systems.

In this paper, we present an innovative approach for stability analysis of nonlinear discrete time-varying systems introducing a new notion of dynamic poles and Extended-Routh's stability approach. The stability analysis is carried out by introducing a new notion of dynamic characteristic equation for the nonlinear discrete time-varying system and defining the dynamic poles in m-plane. The m-plane for nonlinear time varying discrete systems is similar to that of the z-plane for linear time invariant discrete systems. The stability theorem is established and applied to various classes of nonlinear discrete systems.

Tensegrity systems are lightweight structures composed of cables and struts. The nonlinear behavior of tensegrity systems is critical; therefore, the design of these types of structures is relatively complex. In the present study, a practical and efficient approach for geometrical nonlinear analysis of tensegrity systems is proposed. The approach is based on the point iterative method. Static equilibrium equations are given in nodes for subsystems, thus the maximum unknown displacement number in each step is three. Pre-stress forces in the system are taken into account in a tangent stiffness matrix, while similar calculations are carried out for each node in the system which has a minimum of one degree of freedom. In each iteration step, the values found in previous steps are used. When it reaches permissible tolerance of calculation, final displacements and internal forces are obtained. The structural behavior of the tensegrity systems were evaluated by the proposed method. The results show that the method can be used effectively for tensegrity systems.

A novel approach for stability analysis of industrial symbiosis systems

An effective approach for high-dimensional reliability analysis of train-bridge vibration systems via the fractional moment

Recently, it has been proposed to use approximation techniques in the context of decision procedures for the quantifier-free theory of fixed-size bit-vectors. We discuss existing and novel variants of under-approximation techniques. Under-approximations produce smaller models and may reduce solving time significantly. We propose a new technique that allows early termination of an under-approximation refinement loop, although the original formula is unsatisfiable. Moreover, we show how over-approximation and under-approximation techniques can be combined. Finally, we evaluate the effectiveness of our approach on array and bit-vector benchmarks of the SMT library.KeywordsModel CheckDecision ProcedureSmall ModelTest Case GenerationOriginal FormulaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safety-and security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of the robustness measurement. We evaluate the effectiveness of Marble on both LSTM and GRU models trained separately with three popular natural language datasets. The results demonstrate that (1) our refinement algorithm is more efficient in deriving an accurate abstraction than the random strategy, and (2) Marble enables quantitative robustness analysis, in rendering better efficiency, accuracy, and scalability than the state-of-the-art techniques.