Real-time verification of wireless home networks using bigraphs with sharing

Home wireless networks are difficult to manage and comprehend because of evolving locality, co-locality, connectivity and interaction. We define formal models of home wireless network infrastructure and policies and investigate how they can be used in a network management system designed to provide user-oriented support. We model spatial and temporal behaviour of network interactions and user-initiated network policies and define an online framework for generation of models from network and user-initiated events. The models are expressed in an extension to Milnerʼs bigraphical reactive systems. Analysis of the models is carried out in real-time by a bespoke bigraph reasoning system based on checking predicates, which is encoded as bigraph matching. Real-time model generation and analysis is implemented on the experimental Homework system router and trialled with synthetic and actual network data.     

A computer algebra approach to verification and deduction in many-valued knowledge systems

 A theoretical result [10] that relates tautological consequence in many-valued logics to the ideal membership problem in algebra is revisited. The intended use of the approach in this article and its implementation is the verification of consistency and the automated extraction of knowledge in rule-based knowledge systems. Programs are written in the CoCoA language. Four RBS are studied to illustrate the implementation.     

Managing the false alarms: A framework for assurance and verification of surveillance monitoring

This article discusses methods to support assurance of surveillance monitoring and compliance verification knowledge management (CV-KM). The discussion includes aspects of primary monitoring systems, the different environments in which they operate, the verification problem solving and decision making tasks, the problem structure, and the coordination of the review process to facilitate truth maintenance and regulatory Meta rules. Based on the ALCOD (Alert Coding) prototype developed with the Surveillance Division of the Australian Stock Exchange (ASX), the surveillance operation is considered a primary monitoring function with the analysis of the resulting output the second-tier monitoring function—the assurance component.

A requirements-based programming approach to developing a NASA autonomous ground control system

A new requirements-based programming approach to the engineering of computer-based systems offers not only an underlying formalism, but also full formal development from requirements capture through to the automatic generation of provably-correct code. The method, Requirements-to-Design-to-Code (R2D2C), is directly applicable to the development of autonomous systems and systems having autonomic properties. We describe both the R2D2C method and a prototype tool that embodies the method, and illustrate the applicability of the method by describing how the prototype tool could be used in the development of LOGOS, a NASA autonomous ground control system that exhibits autonomic behavior. Finally, we briefly discuss other possible areas of application of the approach.     

Implementing a Quantum Algorithm with Exchange-Coupled Quantum Dots: A Feasibility Study

We present Monte Carlo wavefunction simulations for quantum computations employing an exchange-coupled array of quantum dots. Employing a combination of experimentally and theoretically available parameters, we find that gate fidelities greater than 98% may be obtained with current experimental and technological capabilities. Application to an encoded 3 qubit (nine physical qubits) Deutsch-Josza computation indicates that the algorithmic fidelity is more a question of the total time to implement the gates than of the physical complexity of those gates. PACS: 81.07.Ta, 02.70.Ss, 03.67.Lx, 03.65.Yz     

A metaheuristic framework for stochastic combinatorial optimization problems based on GPGPU with a case study on the probabilistic traveling salesman problem with deadlines

In this work we propose a general metaheuristic framework for solving stochastic combinatorial optimization problems based on general-purpose computing on graphics processing units (GPGPU). This framework is applied to the probabilistic traveling salesman problem with deadlines (PTSPD) as a case study. Computational studies reveal significant improvements over state-of-the-art methods for the PTSPD. Additionally, our results reveal the huge potential of the proposed framework and sampling-based methods for stochastic combinatorial optimization problems.     

A unified discrete framework for intrinsic and extrinsic Dirac operators for geometry processing

Spectral mesh analysis and processing methods, namely ones that utilize eigenvalues and eigenfunctions of linear operators on meshes, have been applied to numerous geometric processing applications. The operator used predominantly in these methods is the Laplace‐Beltrami operator, which has the often‐cited property that it is intrinsic, namely invariant to isometric deformation of the underlying geometry, including rigid transformations. Depending on the application, this can be either an advantage or a drawback. Recent work has proposed the alternative of using the Dirac operator on surfaces for spectral processing. The available versions of the Dirac operator either only focus on the extrinsic version, or introduce a range of mixed operators on a spectrum between fully extrinsic Dirac operator and intrinsic Laplace operator. In this work, we introduce a unified discretization scheme that describes both an extrinsic and intrinsic Dirac operator on meshes, based on their continuous counterparts on smooth manifolds. In this discretization, both operators are very closely related, and preserve their key properties from the smooth case. We showcase various applications of our operators, with improved numerics over prior work.     

A simulation-based optimization approach for a semiconductor photobay with automated material handling system

This study addressed the issue of automated material handling systems (AMHS) in the photolithography zone of a 300 mm (12-in.) wafer fab facility. The lithography process accounts for 40–50% of the time required to produce wafers. Therefore, managing the AMHS in the photolithography zone is a challenging task. This paper examines the dispatching rule and the number of vehicles in variable wafer input cases. With a stochastic and complex manufacturing process, a photobay simulation may lead to excessive iterations and wasted computation time. The most frequently used approach for process management in the literature is performance analysis with a model that simulates each alternative for N times. However, this approach becomes time consuming as the number of variables and iterations increases. To address this issue, we use Optimal Computing Budget Allocation (OCBA) and extend OCBA by adding particle swarm optimization (PSO). With this combined approached, the number of iterations of each alternative is determined by OCBA, and the optimal solution in the domain of feasible solutions is identified through PSO. This research provides a useful reference to optimally allocate lithographical resources and the number of iterations with random parameters for both scholars and practitioners. Results demonstrate the superiority of PSO_OCBA in terms of searching quality and robustness.     

A mathematical framework to optimize ATR systems with non-declarations and sensor fusion

Combat identification is one example where incorrect automatic target recognition (ATR) output labels may have substantial decision costs. For example, the incorrect labeling of hostile targets vs. friendly non-targets may have high costs; yet, these costs are difficult to quantify. One way to increase decision confidence is through fusion of data from multiple sources or from multiple looks through time. Numerous methods have been published to determine a Bayes’ optimal fusion decision if decision costs are well known. This research presents a novel mathematical programming ATR evaluation framework. A new objective function inclusive of time is introduced to optimize and compare ATR systems. Constraints are developed to enforce both decision maker preferences and traditional engineering measures of performance. This research merges rejection and receiver operating characteristic (ROC) analysis by incorporating rejection and ROC thresholds as decision variables. The rejection thresholds specify non-declaration regions, while the ROC thresholds explore viable true positive and false positive tradeoffs for output target labels. This methodology yields an optimal ATR system subject to decision maker constraints without using explicit costs for each type of output decision. A sample application is included for the fusion of two channels of collected polarized radar data for 10 different ground targets. A Boolean logic and probabilistic neural network fusion method are optimized and compared. Sensitivity analysis of significant performance parameters then reveals preferred regions for each of the fusion algorithms.     

Modeling temporal processes via belief networks and Petri nets,with application to expert systems

We show how Bayesian belief networks (BNs) can be used to model common temporal knowledge. Two approaches to their structuring are proposed. The first leads to BNs with nodes representing states of a process and times spent in such states, and with a graphical structure reflecting the conditional independence assumptions of a Markovian process. A second approach leads to BNs whose topology represents a conditional independence structure between event-times. Once required distributional specifications are stored within the nodes of a BN, this becomes a powerful inference machine capable, for example, of reasoning backwards in time. We discuss computational difficulties associated with propagation algorithms necessary to perform these inferences, and the reasons why we chose to adopt Monte Carlo-based propagation algorithms. Two improvements to existing Monte Carlo algorithms are proposed; an enhancement based on the principle of importance sampling, and a combined technique that exploits both forward and Markov sampling. Finally, we consider Petri nets, a very interesting and general representation of temporal knowledge. A combined approach is proposed, in which the user structures temporal knowledge in Petri net formalism. The obtained Petri net is then automatically translated into an equivalent BN for probability propagation. Inferred conclusions may finally be explained with the aid of Petri nets again.     

A distributed control system and its application to a board mill

A distributed microcomputer-based automation system, which has been applied to a new board mill is described. The automation system used was based on several independent data processing units with dedicated functions. The data processing units were interconnected with serial buses which used HDLC communication protocol. The operating stations were based on a colour visual display unit which made a new type of control room concept possible. The substations and the buses are backed up by standbys to ensure a sufficiently high availability as maintenance staff were unavailable outside normal working hours. The board mill where the system was applied for the first time called for special solutions, which made a digital distributed system the natural choice. A short account of the experience with the system is also given.     

Optimum extrapolated method in a special case with application to sor method

Given a linear system Ax = b and an iterative method x ^{(m + 1)} = Gx ^(m)+k, m = 0,1,2,…(1) to solve it, we determine analytically the optimum extrapolation factor of the extrapolated method of (1), when all the eigenvalues of G have the same modulus (Section 2). Then using the SOR theory in the case of consistently ordered matrices A and applying the results of Section 2 to the extrapolated SOR (ESOR) method, we show (Section 3), that the globally optimum parameters of it (and also of the AOR method) [2, 11, 14] are recovered.     

A multivariable MRAC scheme with application to a nonlinear aircraft model

This paper revisits the multivariable model reference adaptive control (MRAC) problem, by studying adaptive state feedback control for output tracking of multi-input multi-output (MIMO) systems. With such a control scheme, the plant-model matching conditions are much less restrictive than those for state tracking, while the controller has a simpler structure than that of an output feedback design. Such a control scheme is useful when the plant-model matching conditions for state tracking cannot be satisfied. A stable adaptive control scheme is developed based on LDS decomposition of the high-frequency gain matrix, which ensures closed-loop stability and asymptotic output tracking. A simulation study of a linearized lateral-directional dynamics model of a realistic nonlinear aircraft system model is conducted to demonstrate the scheme. This linear design based MRAC scheme is subsequently applied to a nonlinear aircraft system, and the results indicate that this linearization-based adaptive scheme can provide acceptable system performance for the nonlinear systems in a neighborhood of an operating point.     

A manufacturing XML schema definition and its application to a data management system on the shop floor

Digitization for sharing knowledge on the shop floor in the machinery industry has been given much attention recently. To help engineers use digitization practically and efficiently, this paper proposes a method based on manufacturing case data that has a direct relation to manufacturing operations. The data are represented in XML schema, as it can be easily applied to Web-based systems on the shop floor. The definitions were made for eight manufacturing methods including machining and welding. The derived definitions consist of four divisions of metadata, work-piece, process and evaluation. Three divisions except for the “process” division are common to the manufacturing methods. The average number of elements for a manufacturing method is about 200. The represented schema is also used to convey knowledge such as operation standards and manufacturing troubleshooting on the shop floor. Using the definitions, a data management system is developed. It is a Web-based Q&A system, in which the engineers specify the manufacturing case data mainly by selecting from the candidates. Then, the system fills in the blank portions and/or shows messages to help complete the case data. The proposed method is evaluated through practical scenarios of arc welding and machining.     

The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system

This study uses the Received Signal Strength Indication (RSSI) values of RFID to predict the position of picking staff for warehouse management. A proposed feature selection-based back-propagation (BP) neural network that uses an artificial immune system (AIS) (FSBP-AIS) to determine the connecting weights of a neural network learns the relationship between the RSSI values and the position of the picking staff. In addition, the proposed FSBP-AIS is able to determine the representative features, or inputs, during training. Once a picking staff's position is known, this information is used to plan the picking route for picking staff if a new order arrives. The computational results indicate that the proposed FSBP-AIS can provide better predictions than a traditional BP neural network, BP neural network with stepwise regression to determine the important inputs, and regression method.     

A delay-range-partition approach to analyse stability of linear systems with time-varying delays

In this paper, the stability analysis of linear systems with an interval time-varying delay is investigated. First, augmented Lyapunov–Krasovskii functionals are constructed, which include more information of the delay's range and the delay's derivative. Second, two improved integral inequalities, which are less conservative than Jensen's integral inequalities, and delay-range-partition approach are utilised to estimate the upper bounds of the derivatives of the augmented Lyapunov–Krasovskii functionals. Then, less conservative stability criteria are proposed no matter whether the lower bound of delay is zero or not. Finally, to illustrate the effectiveness of the stability criteria proposed in this paper, two numerical examples are given and their results are compared with the existing results.     

A new separation result for a class of quadratic-like systems with application to Euler-Lagrange models

A separation result for some kind of global stabilization via output feedback of a class of nonlinear systems, under the form of some stabilizability by state feedback on the one hand, and some unboundedness observability on the other hand is presented. They allow to design, for any domain of output initial condition, some dynamic output feedback controller achieving global stability. It is also highlighted how disturbance attenuation can further be achieved on the same basis. As an example, the proposed conditions are shown to be satisfied by the class of so-called Euler-Lagrange systems, for which a tracking output feedback control law is thus proposed.     

Fault Detection and Isolation of discrete-time Markovian jump linear systems with application to a network of multi-agent systems having imperfect communication channels

This paper deals with the problem of Fault Detection and Isolation (FDI) for discrete-time Markovian Jump Linear Systems (MJLSs). A geometric property related to the unobservable subspace of an MJLS is first presented and the concept of unobservability subspace is introduced. Sufficient conditions for designing an H_∞-based FDI algorithm for MJLSs subject to input disturbances and measurement noise are presented and developed. Our proposed approach is then applied to the problem of fault detection and isolation in a network of multi-agent systems when imperfect communication channels exist among the agents. A discrete-time communication link with a stochastic packet dropping effect is considered based on the Gilbert–Elliott model and the entire network is modeled as a discrete-time MJLS. Simulation results are presented for formation flight of satellites to demonstrate and verify the effectiveness and performance capabilities of our proposed FDI algorithm.     

A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies

Reliability problems are an important type of optimization problems that are motivated by different needs of real-world applications such as telecommunication systems, transformation systems, and electrical systems, so on. This paper studies a special type of these problems which is called redundancy allocation problem (RAP) and develops a bi-objective RAP (BORAP). The model includes non-repairable series–parallel systems in which the redundancy strategy is considered as a decision variable for individual subsystems. The objective functions of the model are (1) maximizing system reliability and (2) minimizing the system cost. Meanwhile, subject to system-level constraint, the best redundancy strategy among active or cold-standby, component type, and the redundancy level for each subsystem should be determined. To have a more practical model, we have also considered non-constant component hazard functions and imperfect switching of cold-standby redundant component. To solve the model, since RAP belong to the NP-hard class of the optimization problems, two effective multi-objective metaheuristic algorithms named non-dominated sorting genetic algorithms (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are proposed. Finally, the performance of the algorithms is analyzed on a typical case and conclusions are demonstrated.