New results on stability analysis of delayed recurrent neural networks based on the integral quadratic constraints approach

This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov–Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Formulation and characterisation of tetracycline-containing bioadhesive polymer networks designed for the treatment of periodontal disease.

This study described the drug release, rheological (dynamic and flow) and textural/mechanical properties of a series of formulations composed of 15% w/w polymethylvinylether-co-maleic anhydride (PMVE-MA), 0-9% w/w polyvinylpyrrolidone (PVP) and containing 1-5% w/w tetracycline hydrochloride, designed for the treatment of periodontal disease. All formulations exhibited pseudoplastic flow with minimal thixotropy. Increasing the concentration of PVP sequentially increased the zero-rate viscosity (derived from the Cross model) and the hardness and compressibility of the formulations (derived from texture profile analysis). These affects may be accredited to increased polymer entanglement and, in light of the observed synergy between the two polymers with respect to their textural and rheological properties, interaction between PVP and PMVE-MA. Increasing the concentration of PVP increased the storage and loss moduli yet decreased the loss tangent of all formulations, indicative of increased elastic behaviour. Synergy between the two polymers with respect to their viscoelastic properties was observed. Increased adhesiveness, associated with increased concentrations of PVP was ascribed to the increasing bioadhesion and tack of the formulations. The effect of increasing drug concentration on the rheological and textural properties was dependent on PVP concentration. At lower concentrations (0, 3% w/w) no effect was observed whereas, in the presence of 9% w/w PVP, increasing drug concentration increased formulation elasticity, zero rate viscosity, hardness and compressibility. These observations were ascribed to the greater mass of suspended drug in formulations containing the highest concentration of PVP. Drug release from formulations containing 6 and 9% PVP (and 5% w/w drug) was prolonged and swelling/diffusion controlled. Based on the drug release, rheological and textural properties, it is suggested that the formulation containing 15% w/w PMVE-MA, 6% w/w PVP and tetracycline hydrochloride (5% w/w) may be useful for the treatment of periodontal disease.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

WFI Optimised PWGPS for Wireless IP Networks

The paper describes the design and analysis of a packet scheduler intended to operate over wireless channels with spatially selective error bursts. A particularly innovative aspect in the design is the optimization of the scheduler algorithm to minimize the worst-case fairness index (WFI) for real-time IP traffic.

Performance analysis of an improved power-saving medium access protocol for IEEE 802.11 point coordination function WLAN

Wireless enabled portable devices must operate with the highest possible energy efficiency while still maintaining a minimum level and quality of service to meet the user's expectations. The authors analyse the performance of a new pointer-based medium access control protocol that was designed to significantly improve the energy efficiency of user terminals in wireless local area networks. The new protocol, pointer controlled slot allocation and resynchronisation protocol (PCSAR), is based on the existing IEEE 802.11 point coordination function (PCF) standard. PCSAR reduces energy consumption by removing the need for power saving stations to remain awake and listen to the channel. Using OPNET, simulations were performed under symmetric channel loading conditions to compare the performance of PCSAR with the infrastructure power saving mode of IEEE 802.11, PCF-PS. The simulation results demonstrate a significant improvement in energy efficiency without significant reduction in performance when using PCSAR. For a wireless network consisting of an access point and 8 stations in power saving mode, the energy saving was up to 31% while using PCSAR instead of PCF-PS, depending upon frame error rate and load. The results also show that PCSAR offers significantly reduced uplink access delay over PCF-PS while modestly improving uplink throughput.

Quantum-state transfer in imperfect artificial spin networks

High-fidelity quantum computation and quantum state transfer are possible in short spin chains. We exploit a system based on a dispersive qubit-boson interaction to mimic XY coupling. In this model, the usually assumed nearest-neighbor coupling is no longer valid: all the qubits are mutually coupled. We analyze the performances of our model for quantum state transfer showing how preengineered coupling rates allow for nearly optimal state transfer. We address a setup of superconducting qubits coupled to a microstrip cavity in which our analysis may be applied.

Damping torque analysis for DC bus implemented damping control

Damping torque analysis is a well-developed technique for understanding and studying power system oscillations. This paper presents the applications of damping torque analysis for DC bus implemented damping control in power transmission networks in two examples. The first example is the investigation of damping effect of shunt VSC (Voltage Source Converter) based FACTS voltage control, i.e., STATCOM (Static Synchronous Compensator) voltage control. It is shown in the paper that STATCOM voltage control mainly contributes synchronous torque and hence has little effect on the damping of power system oscillations. The second example is the damping control implemented by a Battery Energy Storage System (BESS) installed in a power system. Damping torque analysis reveals that when BESS damping control is realized by regulating exchange of active and reactive power between the BESS and power system respectively, BESS damping control exhibits different properties. It is concluded by damping torque analysis that BESS damping control implemented by regulating active power is better with less interaction with BESS voltage control and more robust to variations of power system operating conditions. In the paper, all analytical conclusions obtained are demonstrated by simulation results of example power systems.

Modeling energy consumption in error-prone IEEE 802.11-based wireless ad-hoc networks

In the IEEE 802.11 MAC layer protocol, there are different trade-off points between the number of nodes competing for the medium and the network capacity provided to them. There is also a trade-off between the wireless channel condition during the transmission period and the energy consumption of the nodes. Current approaches at modeling energy consumption in 802.11 based networks do not consider the influence of the channel condition on all types of frames (control and data) in the WLAN. Nor do they consider the effect on the different MAC and PHY schemes that can occur in 802.11 networks. In this paper, we investigate energy consumption corresponding to the number of competing nodes in IEEE 802.11's MAC and PHY layers in error-prone wireless channel conditions, and present a new energy consumption model. Analysis of the power consumed by each type of MAC and PHY over different bit error rates shows that the parameters in these layers play a critical role in determining the overall energy consumption of the ad-hoc network. The goal of this research is not only to compare the energy consumption using exact formulae in saturated IEEE 802.11-based DCF networks under varying numbers of competing nodes, but also, as the results show, to demonstrate that channel errors have a significant impact on the energy consumption.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

On velocity-based local model networks for nonlinear identification

This paper exposes the strengths and weaknesses of the recently proposed velocity-based local model (LM) network. The global dynamics of the velocity-based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub-models are continuous-time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub-models. In this paper, a velocity-based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity-based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity-based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.