control with limited communication and message losses

We propose an H^∞ approach to a remote control problem where the communication is constrained due to the use of a shared channel. The controller employs a periodic time sequencing scheme for message transmissions from multiple sensors and to multiple actuators of the system. It further takes into account the information on the random message losses that occur in the channel. An exact characterization for controller synthesis is obtained and is stated in terms of linear matrix inequalities. Furthermore, an analysis on the loss probabilities of the messages to accomplish stabilization is carried out. The results are illustrated through a numerical example.     

Simplified adaptive estimation

A simplified adaptive scheme is suggested for the estimation of the state vector of linear systems driven by white process noise that is added to an unknown deterministic signal. The design approach is based on embedding the Kalman filter (KF) within a simplified adaptive control loop that is driven by the innovation process. The simplified adaptive loop is idle during steady-state phases that involve white driving noise only. However, when the deterministic signal is added to the driving noise signal, the simplified adaptive control loop enhances the KF gains and helps in reducing the resulting transients. The stability of the overall estimation scheme is established under strictly passive conditions of a related system. The suggested method is applied to the target acceleration estimation problem in a Theater Missile Defence scenario.     

Decision support for network disruption mitigation

Our increasing reliance on networks of all types, coupled with their increasing vulnerability to disruption, makes it critical to better understand risks associated with natural disasters, terrorist attacks, and other incidents. However, choosing how to best protect, reinforce, and improve a network given a limited budget is a complex problem. We have developed an integrated approach that examines the effects of different network disruption scenarios for a variety of performance measures. The developed decision support methodology allows for comprehensive exploration of disruption impacts, statistically and visually, and facilitates examination of “what-if” planning scenarios.     

Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)

The neural network method, a relatively new method in reverse engineering (RE), has the potential to reconstruct 3D models accurately and fast. A neural network (NN) is a set of interconnected neurons, in which each neuron is capable of making autonomous arithmetic and geometric calculations. Moreover, each neuron is affected by its surrounding neurons through the structure of the network. This work proposes a new approach that utilizes growing neural gas neural network (GNG NN) techniques to reconstruct a triangular manifold mesh. This method has the advantage of reconstructing the surface of an n-genus freeform object without a priori knowledge regarding the original object, its topology or its shape. The resulting mesh can be improved by extending the MGNG into an adaptive algorithm. The proposed method was also extended for micro-structure modeling. The feasibility of the proposed method is demonstrated on several examples of freeform objects with complex topologies.     

Application of reinforcement learning to the game of Othello

Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforcement learning, a machine learning technique for solving sequential decision making problems with large state spaces. We describe how reinforcement learning can be combined with a function approximation method to avoid both the curse of dimensionality and the curse of modelling. To illustrate the usefulness of this approach, we apply it to a problem with a huge state space—learning to play the game of Othello. We describe experiments in which reinforcement learning agents learn to play the game of Othello without the use of any knowledge provided by human experts. It turns out that the reinforcement learning agents learn to play the game of Othello better than players that use basic strategies.     

Typology of teacher perception toward distance education issues – A study of college information department teachers in Taiwan

The popularity of distance education has grown rapidly over the last decade in Taiwan’s higher education, yet many fundamental teaching–learning issues are still in debate. While teacher–student interaction is a key success factor in distance education, little work has been done on the teachers. The intent of this research was to clarify teacher’s perceptions on key distance education issues and to develop a discernible typology of different groups of teachers based on their perceptions. Because there may be a gulf between teachers and the technology used in distance education, the target of this study was teachers in information related departments in Taiwan’s colleges, who were more familiar with current technology. Factor analysis and cluster analysis were used to derive the typology. Five higher-level issue constructs emerged from the factor analysis: learning effect, customization, administrative challenges, geographic and resource integration, and instructional design challenges. Four groups of teachers, namely the skeptics, the optimists, the mild-promising group, and the outlier, were identified using cluster analysis of teachers’ perceptions on these five higher-level issue constructs. The profiles of the four groups of teachers were summarized and implications were discussed, which should provide useful insights to the policy makers of higher education on distance education decisions.     

Existence and global exponential stability of periodic solutions of recurrent cellular neural networks with impulses and delays

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness and global exponential stability of periodic solutions for recurrent neural networks with impulsive perturbations and delays. Further, by using numerical simulation method, the influences of the impulsive perturbations on the inherent oscillations are investigated.     

Parameter augmentation and the q-Gosper algorithm

We develop a method for deriving new basic hypergeometric identities from old ones by parameter augmentation. The main idea is to introduce a new parameter and use the q-Gosper algorithm to find out a suitable form of the summand. By this method, we recover some classical formulas on basic hypergeometric series and find extensions of the Rogers–Fine identity and Ramanujan’s summation formula. Moreover, we derive an identity for a summation.     

Stability of iterative processes with errors for a system of nonlinear -accretive variational inclusions in Banach spaces

In this paper, by using the concept of (A,η)-accretive mappings and the new resolvent operator technique associated with (A,η)-accretive mappings, we introduce and study a system of general mixed quasivariational inclusions involving (A,η)-accretive mappings in Banach spaces, and construct a new perturbed iterative algorithm with mixed errors for this system of nonlinear (A,η)-accretive variational inclusions in q-uniformly smooth Banach spaces. Our results improve and generalize the corresponding results of recent works.