A stochastic hybrid system can express complex dynamical systems such as biological systems and communication networks, but computation for analysis and control is frequently difficult. In this paper, for a class of stochastic hybrid systems, a discrete abstraction method in which a given system is transformed into a finite-state system is proposed based on the notion of bounded bisimulation. In the existing discrete abstraction method based on bisimulation, a computational procedure is not in general terminated. In the proposed method, only the behavior for the finite time interval is expressed as a finite-state system, and termination is guaranteed. Furthermore, analysis of genetic toggle switches is also discussed as an application.

Many controllers are implemented on digital platforms as periodic control tasks. But, in embedded systems, an amount of resources are limited and the reduction of resource utilization of the control task is an important issue. Recently, much attention has been paid to a self-triggered controller, which updates control inputs aperiodically. A control task by which the self-triggered controller is implemented skips the release of jobs if the degradation of control performances by the skipping can be allowed. Each job computes not only the updated control inputs but also the next update instant and the control task is in the sleep state until the instant. Thus the resource utilization is reduced. In this paper, we consider self-triggered predictive control (stPC) of mixed logical dynamical (MLD) systems. We introduce a binary variable which determines whether the control inputs are updated or not. Then, we formulate an stPC problem of mixed logical dynamical systems, where activation costs are time-dependent to represent the preference of activations of the control task. Both the control inputs and the next update instant are computed by solving a mixed integer programming problem. The proposed stPC can reduce the number of updates with guaranteeing stability of the controlled system.

Morphemes, which are obtained from morphological parsing, and statistical sub-words, which are derived from data-driven splitting, are commonly used as the recognition units for speech recognition of agglutinative languages. In this letter, we propose a discriminative approach to select the splitting result, which is more likely to improve the recognizer's performance, for each distinct word type. An objective function which involves the unigram language model (LM) probability and the count of misrecognized phones on the acoustic training data is defined and minimized. After determining the splitting result for each word in the text corpus, we select the frequent units to build a hybrid vocabulary including morphemes and statistical sub-words. Compared to a statistical sub-word based system, the hybrid system achieves 0.8% letter error rates (LERs) reduction on the test set.

This letter proposes a simple heuristic to identify the discrete-time switched autoregressive exogenous (SARX) systems. The goal of the identification is to identify the switching sequence and the system parameters of all submodels simultaneously. In this letter the SARX system identification problem is formulated as the l0 norm minimization problem, and an iterative algorithm is proposed by applying the reweighted least squares technique. Although the proposed algorithm is heuristic, the numerical examples show its efficiency and robustness for noise.

Thin film glucose biosensors were fabricated with organic/inorganic hybrid films based on glucose oxidase (GOx) and Prussian Blue nano-clusters. The biosensors composed of hybrid films were characterized by the low operating potential and the advantage to interference-free detection. In this research, we employed two kinds of thin films for GOx immobilization: Langmuir-Blodgett (LB) and self-assembled monolayer (SAM). The LB film immobilizes GOx in its inside through the electrostatic force, while the SAM immobilizes GOx with the covalent bond. The sensors with LB film produced a relatively high current signal, while the non-linear behavior and a low stability were recognized. On the other hand, the sensors with SAM presented a good linear relationship and a very stable performance.

This paper presents a new hierarchical mode segmentation of the observed driving behavioral data based on the multi-level abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical mode segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed. The proposed framework enables us to make a bridge between the signal space and the symbolic space in the understanding of the human behavior.

In this paper, we propose a new modeling method to express discrete-time hybrid systems with parameter uncertainty as a mixed logical dynamical (MLD) model. In analysis and control of hybrid systems, there are problem formulations in which convex polyhedra are computed, but for high-dimensional systems, it is difficult to solve these problems within a practical computation time. The key idea of this paper is to use an interval method, which is one of the classical methods in verified numerical computation, and to regard an interval as an over-approximation of a convex polyhedron. By using the obtained MLD model, analysis and synthesis of robust control systems are formulated.

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.

In this paper, we propose a new approximate algorithm for the model predictive control (MPC) problem with a time-varying reference of hybrid systems. The proposed algorithm consists of an offline computation and an online computation. In the offline computation, candidates of mode sequences are derived. In the online computation, after the mode sequence is uniquely decided among candidates, the finite-time optimal control problem, i.e., the quadratic programming problem, is solved. So by applying the proposed algorithm, the computational amount of the online computation is decreased. First, the MPC problem with a time-varying reference is formulated. Next, the proposed algorithm is explained, and the accuracy of the obtained approximate solution is discussed. Finally, the effectiveness of the proposed method is shown by a numerical example.

In this paper, we study performance evaluation of workflow-based information systems. Because of state space explosion, analysis by stochastic models, such as stochastic Petri nets and queuing models, is not suitable for workflow systems in which a large number of flow instances run concurrently. We use fluid-flow approximation technique to overcome this difficulty. In the proposed method, GSPN (Generalized Stochastic Petri Nets) models representing workflows are approximated by a class of timed continuous Petri nets, called routing timed continuous Petri nets (RTCPN). In RTCPN models, each discrete set is approximated by a continuous region on a real-valued vector space, and variance in probability distribution is replaced with a real-valued interval. Next we derive piecewise linear systems from RTCPN models, and use interval methods to compute guaranteed enclosures for state variables. As a case study, we solve an optimal resource assignment problem for a paper review process.

Hybrid FEC/ARQ, which is a mixture of forward error correction (FEC) and automatic repeat request (ARQ), is a well-known technique aiming for packet-loss recovery to guarantee quality of service (QoS) for real-time communications. In this paper, focusing on layered video transmission over wireless network environment, we propose a proactive retransmission scheme for hybrid FEC/ARQ. In the proposed scheme, a receiver host periodically sends probe packets to a sender host in order to check wireless channel state. If the sender host does not receive any probe packet during a pre-specified interval, it regards the wireless channel as being in burst loss state, and it proactively retransmits packets expected to be lost during the burst loss period. The buffer management associated with layered video coding is also taken into consideration. The performance of the proposed scheme is investigated by simulation. Numerical examples show that the proposed scheme transmits packets of the base layer more successfully than the conventional FEC/ARQ.

This paper proposes a notion of a controllability measure of discrete-time piecewise affine systems, which is a natural extension of the controllability gramian of linear systems. Although this measure is calculated in a probabilistic way, it may be applied to control of biological systems for providing a policy to experiments for pharmaceutical developments. Thus an application to gene regulatory control of luminescence in the marine bacterium modeled by the piecewise affine system is discussed in this paper.

This paper deals with stability analysis of hybrid systems. Such systems are characterized by a combination of continuous dynamics and logic based switching between discrete modes. Lyapunov theory is a well known methodology for the stability analysis of linear and nonlinear systems in control system literature. Construction of Lyapunov functions for hybrid systems is generally a difficult task, but once these functions are defined, stabilization of the system is straight-forward. The sum of squares (SOS) decomposition and semidefinite programming has also provided an efficient methodology for analysis of nonlinear systems. The computational method used in this paper relies on the SOS decomposition of multivariate polynomials. By using SOS, we construct a (some) Lyapunov function(s) for the hybrid system. The reduction techniques provide numerical solution of large-scale instances; otherwise they will be practically unsolvable. The introduced method can be used for hybrid systems with linear or nonlinear vector fields. Some examples are given to demonstrate the capabilities of the proposed approach.

This letter describes the first derivatives estimation of nonlinear parameters through an embedded identifier in the hybrid system by using a feed-forward neural network (FFNN). The hybrid systems are modelled by the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is used to identify the full dynamics of the hybrid system. Moreover, the partial derivatives of an objective function J with respect to the parameters are estimated by the proposed identifier. Then, it is applied for the identification and estimation of the non-smooth nonlinear dynamic behaviors due to a saturation limiter in a practical engineering system.

The output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to non-smooth nonlinearities from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures have been used. A nonlinear least squares method, which is the Gauss-Newton optimization algorithm, is used in this paper. The gradient required in the Gauss-Newton method can be computed by applying trajectory sensitivities from the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in a multi-machine power system (MMPS).

In this paper, the interpolation line search (ILS) algorithm to find the desirable step length in a numerical optimization method is investigated to determine the optimal saturation limits with non-smooth nonlinearities. The simple steepest descent algorithm is used to illustrate that the ILS algorithm can provide adequate reductions in an objective function at minimal cost with fast convergence. The power system stabilizer (PSS) with output limits is used as an example for a nonlinear controller to be tuned. The efficient computation to implement the ILS algorithm in the steepest descent method is available by using the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The simulation results are given to show the performance improved by the ILS algorithm.

Discretely controlled continuous systems are characterised by their interacting continuous and discrete dynamics, where the continuous subsystem usually represents the system to be controlled and the discrete part describes the controller that switches the continuous system among different operation modes. This paper analyses systems where a perpetual switching of the operation mode has to occur in order to maintain the system's state in a prescribed operating region. It is shown how the analysis of the overall hybrid system can be simplified by using an embedded map that determines the behaviour at the switching instants.

If some differences of perceptions arise between populations, then strategies which are regarded as the same strategy in a population may be perceived distinguishably in the other populations. To discuss such a situation, replicator dynamics for multi-population hypergames has been proposed. However, it is assumed that players' perceptions are given and fixed. In this paper, we consider that each population has various interpretation functions and choose one of them depending on payoffs, and we propose a hybrid system representation of replicator dynamics with changes of interpretation functions. Moreover, we apply our proposed model to a well-known example of a hypergame "Soccer Hooliganism" and show that behaviors converging to heteroclinic orbits can appear by the changes of the interpretation functions.

We describe a simulation method and design for a stand-alone hybrid power supply system composed of a wind turbine generator and photovoltaic modules. The system has been developed to supply power for telecommunications equipment in areas with no commercial power sources. We also report a comparison of the simulation results with actual measured data. The results show that the hybrid system can function effectively as a power supply for telecommunications equipment.

We study analysis of hybrid systems under incomplete knowledge. The class of hybrid systems to be considered is assumed to have the form of a rectangular hybrid automaton such that each constant in invariants and guards is given as a parameter. We develop a method based on symbolic computation that computes an approximation of the discrete behavior of the automaton. We also show an implementation on a constraint logic programming language.