State Transition System Research Articles

Specification, Construction, and Exact Reduction of Finite State Transition System Models of Biochemical Processes

Biochemical reaction systems may be viewed as discrete event processes characterized by a number of states and state transitions. These systems may be modeled as state transition systems with transitions representing individual reaction events. Since they often involve a large number of interactions, it can be difficult to construct such a model for a system, and since the resulting state-level model can involve a huge number of states, model analysis can be difficult or impossible. Here, we describe methods for the high-level specification of a system using hypergraphs, for the automated generation of a state-level model from a high-level model, and for the exact reduction of a state-level model using information from the high-level model. Exact reduction is achieved through the automated application of symmetry reduction and invariant manifold reduction techniques to the high-level model, allowing potentially significant reductions without the need to generate a full model. The application of the method to biochemical reaction systems is illustrated by models describing a hypothetical ion-channel at several levels of complexity. The method allows for the reduction of the otherwise intractable example models to a manageable size.

Spatio-Temporal Querying in Smart Spaces

A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with information about the location of people at various points in time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of a set of individuals who are present in various zones of the smart space. We formulate a data model based upon an occupancy relation with a real-valued probability attribute and describe some of the spatio-temporal queries in SQL and CLP(R), focusing on the computation of probabilities, an aspect that is novel to this model. We define concepts of precision and recall to quantify the performance of this model based on its ability to answer various spatio-temporal queries and discuss results from our experimental prototype.

Information flow in trust management systems

This article proposes a systematic study of information flow in credential-based declarative authorization policies. It argues that a treatment in terms of information flow is needed to adequately describe, analyze and mitigate a class of probing attacks which allow an adversary to infer any confidential fact within a policy. Two information flow properties that have been studied in the context of state transition systems, non-interference and opacity, are reformulated in the current context of policy languages. A comparison between these properties reveals that opacity is the more useful, and more general of the two; indeed, it is shown that non-interference can be stated in terms of opacity. The article then presents an inference system for non-opacity or detectability, in Datalog-based policies. Finally, a pragmatic method is presented, based on a mild modification of the mechanics of delegation, for preventing a particularly dangerous kind of probing attack that abuses delegation of authority.

Qualitative Description and Quantitative Optimization of Tactical Reconnaissance Agents System Organization

Abstract In this paper, the problem of qualitative description and quantitative optimization for tactical reconnaissance agents system organization is considered with objective of higher teamwork efficiency and more reasonable task balancing strategies. By analyzing tactical reconnaissance system and its environment, task-(role)-entity agent mapping mechanism and agents in system organization, the system framework is qualitatively described. By transforming the system into an interaction task request-service mechanism queuing system, a Markov chain of system state transition is obtained, since its state transition process in interaction is Markov process and accords with real tactical reconnaissance behaviors. By solving the state transition equations, the inherent relationship of tactical reconnaissance agents is found and the optimized system configuration is obtained. The established simulation demonstration system proves that the proposed approach and model are feasible and effective.

Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities

In this paper, we propose a self-diagnosing intelligent highway surveillance system and design effective solutions for both daytime and nighttime traffic surveillance. For daytime surveillance, vehicles are detected via background modeling. For nighttime videos, headlights of vehicles need to be located and paired for vehicle detection. An algorithm based on likelihood computation is developed to pair the headlights of vehicles at night. Moreover, to balance between the robustness and abundance of acquired information, the proposed system adapts different strategies under different traffic conditions. Performing tracking would be preferred when traffic is smooth. However, under congestion conditions, it is better to obtain traffic parameters by estimation. We utilize a time-varying adaptive system state transition matrix in Kalman filter for better prediction in a traffic surveillance scene when performing tracking. We also propose a mechanism for estimating the traffic flow parameter via regression analysis. The experimental results have shown that the self-diagnosis ability and the modules designed for the system make the proposed system robust and reliable.

Formalization of Abstract State Transition Systems for SAT

We present a formalization of modern SAT solvers and their properties in a form of abstract state transition systems. SAT solving procedures are described as transition relations over states that represent the values of the solver's global variables. Several different SAT solvers are formalized, including both the classical DPLL procedure and its state-of-the-art successors. The formalization is made within the Isabelle/HOL system and the total correctness (soundness, termination, completeness) is shown for each presented system (with respect to a simple notion of satisfiability that can be manually checked). The systems are defined in a general way and cover procedures used in a wide range of modern SAT solvers. Our formalization builds up on the previous work on state transition systems for SAT, but it gives machine-verifiable proofs, somewhat more general specifications, and weaker assumptions that ensure the key correctness properties. The presented proofs of formal correctness of the transition systems can be used as a key building block in proving correctness of SAT solvers by using other verification approaches.

SAGA: A DSL for Story Management

Video game development is currently a very labour-intensive endeavour. Furthermore it involves multi-disciplinary teams of artistic content creators and programmers, whose typical working patterns are not easily meshed. SAGA is our first effort at augmenting the productivity of such teams. Already convinced of the benefits of DSLs, we set out to analyze the domains present in games in order to find out which would be most amenable to the DSL approach. Based on previous work, we thus sought those sub-parts that already had a partially established vocabulary and at the same time could be well modeled using classical computer science structures. We settled on the 'story' aspect of video games as the best candidate domain, which can be modeled using state transition systems. As we are working with a specific company as the ultimate customer for this work, an additional requirement was that our DSL should produce code that can be used within a pre-existing framework. We developed a full system (SAGA) comprised of a parser for a human-friendly language for 'story events', an internal representation of design patterns for implementing object-oriented state-transitions systems, an instantiator for these patterns for a specific 'story', and three renderers (for C++, C# and Java) for the instantiated abstract code.

Abstracting abstract machines

Predictive models are fundamental to engineering reliable software systems. However, designing conservative, computable approximations for the behavior of programs (static analyses) remains a difficult and error-prone process for modern high-level programming languages. What analysis designers need is a principled method for navigating the gap between semantics and analytic models: analysis designers need a method that tames the interaction of complex languages features such as higher-order functions, recursion, exceptions, continuations, objects and dynamic allocation. We contribute a systematic approach to program analysis that yields novel and transparently sound static analyses. Our approach relies on existing derivational techniques to transform high-level language semantics into low-level deterministic state-transition systems (with potentially infinite state spaces). We then perform a series of simple machine refactorings to obtain a sound, computable approximation, which takes the form of a non-deterministic state-transition systems with finite state spaces. The approach scales up uniformly to enable program analysis of realistic language features, including higher-order functions, tail calls, conditionals, side effects, exceptions, first-class continuations, and even garbage collection.

Automated Trust Negotiation with Time Behavior

In automated trust negotiation(ATN), strangers build trust relationship by disclosing attributes credentials alternately. In the recent study of ATN, there was no rigorous formal definition of ATN abstract model, and have nothing to do with the time behavior. It exist denial of service attack which can lead system to paralysis. We present the basic components of ATN abstract model. It defines ATN abstract model by bringing state-transition system. Based on this formalized basis, the time behavior will be explored. We analyze and discuss some time semantics of ATN, and describe the extended ATN by constructing a state-transition system with time behavior. It can describe the ATN abstract model more comprehensive and secure. The satiability of security policies in ATN is discussed.

The Research on Chaotic Signal Tracking Algorithm for SR-UKF-PF

In view of the difficult in the chaotic signal detection and track in the low signal-to-noise(SNR) environment, a modified SR-UKF-PF is developed that has much better robustness than the traditional SR-UKF and gets almost the same performance as the Particle filter. The main idea of this algorithm is to calculated by the system state transition matrix and the error covariance matrix which are gained from the SR-UKF and the sequential fusion to construct the importance density function of the particle filter. Then the importance density function can integrates the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution maximumly. To demonstrate the effectiveness of this model, simulations are carried out based on tracking algorithm for the typical chaotic time series of low dimension chaos mapping and super chaos mapping. The simulation results show that this algorithm can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improves the accuracy of state estimation, and demonstrates the superiorities of particle filtering in the low SNR.

A kripke logical relation between ML and assembly

There has recently been great progress in proving the correctness of compilers for increasingly realistic languages with increasingly realistic runtime systems. Most work on this problem has focused on proving the correctness of a particular compiler, leaving open the question of how to verify the correctness of assembly code that is hand-optimized or linked together from the output of multiple compilers. This has led Benton and other researchers to propose more abstract, compositional notions of when a low-level program correctly realizes a high-level one. However, the state of the art in so-called "compositional compiler correctness" has only considered relatively simple high-level and low-level languages. In this paper, we propose a novel, extensional, compiler-independent notion of equivalence between high-level programs in an expressive, impure ML-like λ-calculus and low-level programs in an (only slightly) idealized assembly language. We define this equivalence by means of a biorthogonal, step-indexed, Kripke logical relation, which enables us to reason quite flexibly about assembly code that uses local state in a different manner than the high-level code it implements ( e.g. self-modifying code). In contrast to prior work, we factor our relation in a symmetric, language-generic fashion, which helps to simplify and clarify the formal presentation, and we also show how to account for the presence of a garbage collector. Our approach relies on recent developments in Kripke logical relations for ML-like languages, in particular the idea of possible worlds as state transition systems.

Spatio-Temporal Reasoning in Biometrics Based Smart Environments

We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key technical idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step and the transition function defines the mapping between states upon the occurrence of an event. We define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment. We provide experimental results to show performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances its overall performance.

Nonlinear Model Predictive Control for Heterogeneous Process Models in Water Resources

AbstractThis paper introduces a novel Non-linear Model Predictive Control (NMPC) algorithm for the real-time control of heterogeneous, large-scale water resources systems. The algorithm is based on the idea of defining a Lagrangian function in which the system state transition equations are defined as separate equality constraints of the optimization problem. This enables the derivation of an adjoint of the state transition equations and provides the objective function gradient. The optimization problem can then be solved by any numerical solver, under the consideration of the constraints and the gradient. The main advantage of the proposed approach is the possibility of easily dealing with the process-based models commonly adopted in the environmental literature, under the assumption of both explicit and implicit time-stepping schemes. The proposed procedure computes the gradient of a cost function by computational costs comparable to a model simulation itself, thus enabling the management of large-scale systems. The capabilities of the NMPC algorithm are first evaluated on a test case study, and then demonstrated for the control of six hydraulic structures and two major flood detention basins along the bifurcation points of the Rhine River in The Netherlands.

Availability of a general k-out-of-n:G system with non-identical components considering shut-off rules using quasi-birth–death process

A general repairable k-out-of-n:G system with non-identical components, which is a common form of redundancy, is considered in this paper. The number of repairmen is assumed to be r (1≤ r≤ n− k+1) while components can have similar or different repair priorities. The objective of this work is to address the problem of efficient evaluation of the system's availability in a way that steady state solutions can be obtained systematically in a reasonable computational time. This problem is modeled as a finite state-dependent non-homogeneous quasi-birth–death (QBD) process. An algorithm is introduced to systematically generate the system state vectors and transition rate matrix and then an iterative method based on the Block Gauss–Seidel method is employed to determine the steady state probabilities. These are the novel contributions made in this paper. An analog Monte Carlo simulation is presented to demonstrate the correctness and the efficiency of the proposed method.

Security analysis of GTRBAC and its variants using model checking

Security analysis is a formal verification technique to ascertain certain desirable guarantees on the access control policy specification. Given a set of access control policies, a general safety requirement in such a system is to determine whether a desirable property is satisfied in all the reachable states. Such an analysis calls for the use of formal verification techniques. While formal analysis on traditional Role Based Access Control (RBAC) has been done to some extent, recent extensions to RBAC lack such an analysis. In this paper, we consider the temporal RBAC extensions and propose a formal technique using timed automata to perform security analysis by analyzing both safety and liveness properties. Using safety properties one ensures that something bad never happens while liveness properties show that some good state is also achieved. GTRBAC is a well accepted generalized temporal RBAC model which can handle a wide range of temporal constraints while specifying different access control policies. Analysis of such a model involves a process of mapping a GTRBAC based system into a state transition system. Different reduction rules are proposed to simplify the modeling process depending upon the constraints supported by the system. The effect of different constraints on the modeling process is also studied.

Variational Equations for a Generalized Spacecraft Trajectory Model

Linear perturbation theory is used to develop the variational equations needed to determine the sensitivities of the state at some final time with respect to all of the independent variables associated with a spacecraft trajectory model that is general enough for most applications of interest. The state vector is an augmented vector that includes the position, velocity, mass, and all other control-related variables, such as thrust magnitude and direction. The force model is general and the trajectory can have any number of impulsive and/or finite burn maneuvers. The gradient expressions depend, in part, on the system state transition matrix associated with the given state and its corresponding equations of motion. As an example, the procedure developed is applied to the numerical optimization of a multi-impulse escape trajectory from the moon.

Analysing graph transformation systems through constraint handling rules

AbstractGraph transformation systems (GTS) and constraint handling rules (CHR) are non-deterministic rule-based state transition systems. CHR is well known for its powerful confluence and program equivalence analyses, for which we provide the basis in this work to apply them to GTS. We give a sound and complete embedding of GTS in CHR, investigate confluence of an embedded GTS and provide a program equivalence analysis for GTS via the embedding. The results confirm the suitability of CHR-based program analyses for other formalisms embedded in CHR.

TRACKING ALGORITHM FOR MMW/IR MULTI-SENSORS FUSION

Millimeter wave(MMW)/infrared (IR) sensor is a key technology for composite guidance system of missiles. Aimming to solve the problems that there were linear errors in the algorithm of square-root unscented Kalman filter (SR-UKF) and it was difficult to obtain the importance density function for the algorithm of particle filter(PF), a square-root unscented Kalman particle filter (SR-UK-PF) algorithm with the sequential fusion was presented by combining SR-UKF with PF. The main idea of this algorithm was to calculate the state transition matrix and the error covariance matrix by SR-UKF, and to construct the importance density function by the sequential fusion of particle filter. Thus, the importance density function could integrate the latest observation into system state transition density, and the proposal distribution could be more in line with the distribution of real states. To demonstrate the effectiveness of this model, simulations were carried out based on tracking algorithm for the surface-to-air missile with MMW/IR sensor.The results show that this technique can overcome the flaw that it is hard to get the optimization importance density function in the particle filter, and it can significantly improve the accuracy of state estimation for the system with multi-sensors.

Approach of Degenerate Test Set Generation Based on Secure State Transition

在总结前人工作的基础上,结合安全操作系统对测试的特殊需求,提出了简并测试集(degenerate test set,简称DTS)的概念,设计了一种使用模型检测的基于安全状态转移的高效测试集生成方法.该方法以状态转移为化简对象,在利用模型检测技术生成测试用例的同时,归并相同的状态转移并化简需求集中的冗余属性,从而最终达到化简测试集的目的.在此基础上,探讨了单个用例失败时用例集的有效性问题,并对DTS 生成算法进行了改进.实验结果表明,该方法可以有效地对测试集中的冗余进行化简.

Multimodal identification and tracking in smart environments

We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait, and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of `precision' and `recall'. We have developed a prototype implementation of our proposed concepts and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a good basis for developing practical systems.