Real-Time Multiple Event Detection and Classification Using Moving Window PCA

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load) and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, thereby increasing overall situational awareness of the power system. Numerical case studies using both real data, collected from the UK power system, and simulated case studies, constructed using DigSilent PowerFactory, for islanding events, as well as both loss of load and generation dip events, are used to demonstrate the reliability of the proposed method.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

The Effects of Molecular Anions on the Chemistry of Dark Clouds

We have investigated the role of molecular anion chemistry in pseudo-time-dependent chemical models of dark clouds. With oxygen-rich elemental abundances, the addition of anions results in a slight improvement in the overall agreement between model results and observations of molecular abundances in Taurus molecular cloud 1 (TMC-1 (CP)). More importantly, with the inclusion of anions, we see an enhanced production efficiency of unsaturated carbon-chain neutral molecules, especially in the longer members of the families C(n)H, C(n)H(2), and HC(n)N. The use of carbon-rich elemental abundances in models of TMC-1 (CP) with anion chemistry worsens the agreement with observations compared with model results obtained in the absence of anions.

MODULATED BACKSCATTER MICROWAVE SECURITY SENSOR

A system capable of deployment as a microwave security sensor which can automatically reject background clutter is presented. The principle of operation is based on analog homodyne detection using 1. Q single side-band down conversion of an AM backscattered modulating signal envelope. A demonstrator is presented which operates with a carrier frequency of 2 GHz and 500 KHz backscattered signal. When deployed in a multipath rich open plan office environment the S/N ratio obtained at the detection output was better than 20 dB at 20 in range with 20 dBm EIRP in a 2 MHz detection bandwidth despite the presence of time varying and static clutter. (C) 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2492-2495, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24636

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Convergence and tracking analysis of a variable normalised LMF (XE-NLMF) algorithm

The least-mean-fourth (LMF) algorithm is known for its fast convergence and lower steady state error, especially in sub-Gaussian noise environments. Recent work on normalised versions of the LMF algorithm has further enhanced its stability and performance in both Gaussian and sub-Gaussian noise environments. For example, the recently developed normalised LMF (XE-NLMF) algorithm is normalised by the mixed signal and error powers, and weighted by a fixed mixed-power parameter. Unfortunately, this algorithm depends on the selection of this mixing parameter. In this work, a time-varying mixed-power parameter technique is introduced to overcome this dependency. A convergence analysis, transient analysis, and steady-state behaviour of the proposed algorithm are derived and verified through simulations. An enhancement in performance is obtained through the use of this technique in two different scenarios. Moreover, the tracking analysis of the proposed algorithm is carried out in the presence of two sources of nonstationarities: (1) carrier frequency offset between transmitter and receiver and (2) random variations in the environment. Close agreement between analysis and simulation results is obtained. The results show that, unlike in the stationary case, the steady-state excess mean-square error is not a monotonically increasing function of the step size. (c) 2007 Elsevier B.V. All rights reserved.

The finding of reduced estimated glomerular filtration rate is associated with increased mortality in a large UK population

BACKGROUND: CKD as defined by KDIGO/KDOQI has been shown to affect ~ 8.5% of the UK population. The prevalence of CKD in the UK is similar to that in the USA, yet incident dialysis rates are dramatically different. This retrospective cohort study investigates the association between reduced kidney function and mortality in a large UK population. METHODS: All serum creatinine results covering Northern Ireland's 1.7 million population were collected between 1 January 2001 and 31 December 2002. Estimated glomerular filtration rates (eGFR) were calculated for all serum creatinine measurements using four-variable MDRD equation (IDMS aligned). Patients were followed up for both all-cause and cardiovascular mortality data until the end of December 2006. Patients on renal replacement therapy were excluded. Subgroup analysis in the 75 345 subjects enrolled within a parallel primary care study permitted additional survival analysis with adjustment for traditional cardiovascular risk factors. RESULTS: A total of 1 967 827 serum creatinine results from 533 798 patients were collected. During the period of follow-up, 59 980 deaths occurred. In multivariate survival analysis, using eGFR as a time-varying covariate, a graded association between CKD (defined by eGFR) and all-cause mortality was identified. Compared with participants with an eGFR of > 60 mL/min/1.73 m(2), the adjusted hazard ratios (and 95% confidence intervals) for participants with an eGFR of 45-59 mL/min/1.73 m(2) was 1.02 (0.99-1.04), an eGFR of 30-44 mL/min/1.73 m(2) was 1.44 (1.40-1.47), an eGFR of 15-29 mL/min/1.73 m(2) was 2.12 (2.05-2.20) and an eGFR of

Robust Estimation of HDR in fMRI using H∞ Filters

Estimation and detection of the hemodynamic response (HDR) are of great importance in functional MRI (fMRI) data analysis. In this paper, we propose the use of three H 8 adaptive filters (finite memory, exponentially weighted, and time-varying) for accurate estimation and detection of the HDR. The H 8 approach is used because it safeguards against the worst case disturbances and makes no assumptions on the (statistical) nature of the signals [B. Hassibi and T. Kailath, in Proc. ICASSP, 1995, vol. 2, pp. 949-952; T. Ratnarajah and S. Puthusserypady, in Proc. 8th IEEE Workshop DSP, 1998, pp. 1483-1487]. Performances of the proposed techniques are compared to the conventional t-test method as well as the well-known LMSs and recursive least squares algorithms. Extensive numerical simulations show that the proposed methods result in better HDR estimations and activation detections.

Neonatal control of sucking pressures

Human newborns appear to regulate sucking pressure when bottle feeding by employing, with similar precision, the same principle of control evidenced by adults in skilled behavior, such as reaching (Lee et al., 1998a). In particular, the present study of 12 full-term newborn infants indicated that the intraoral sucking pressures followed an internal dynamic prototype - an intrinsic tau-guide. The intrinsic tau-guide, a recent hypothesis of general tau theory is a time-varying quantity, tau(g), assumed to be generated within the nervous system. It corresponds to some quantity (e.g., electrical charge), chang ing with a constant second-order temporal derivative from a rest level to a goal level, in the sense that tau(g) equals tau of the gap between the quantity and its goal level at each time t. (tau of a gap is the rime-to-closure of the gap at the current closure-rate.) According to the hypoth esis, the infant senses tau(p), the tau of the gap between the current intraoral pressure and its goal level, and regulates intraoral pressure so that tau(p) and tau(g) remain coupled in a constant ratio, k; i.e., tau(p) = k tau(g). With k in the range 0-1, the tau-coupling would result in a bell-shaped rate of change pressure profile, as was, in fact, found. More specifically, the high mean r(2) values obtained when regressing tau(p) on tau(g), for both the increasing and decreasing suction periods of the infants' suck, supported a strong tau-coupling between tau(p) and tau(g). The mean k values were significantly higher In the Increasing suction period, indicating that the ending of the movement was more forceful, a finding which makes sense given the different functions of the two periods of the suck.

Bit error rate performance enhancement of a retrodirective array over a conventional fixed beam array in a dynamic multipath environment

In this paper, we provide experimental evidence to show that enhanced bit error rate (BER) performance is possible using a retrodirective array operating in a dynamically varying multipath environment. The operation of such a system will be compared to that obtained by a conventional nonretrodirective array. The ability of the array to recover amplitude shift keyed encoded data transmitted from a remote location whose position is not known a priori is described. In addition, its ability to retransmit data inserted at the retrodirective array back to a spatially remote beacon location whose position is also not known beforehand is also demonstrated. Comparison with an equivalent conventional fixed beam antenna array utilizing an identical radiating aperture arrangement to that of the retrodirective array are given. These show that the retrodirective array can effectively exploit the presence of time varying multipath in order to give significant reductions in BER over what can be otherwise achieved. Additionally, the retrodirective system is shown to be able to deliver low BER regardless of whether line of sight is present or absent.

An improved elastic-viscoplastic soil model

This paper develops an improved and accessible framework for modelling time-dependent behaviour of soils using the concepts of elasticity and viscoplasticity. The mathematical description of viscoplastic straining is formulated based on a purely viscoplastic and measurable phenomenon, namely creep. The resulting expression for the viscoplastic strain rates includes a measure of both effective stress and the corresponding volumetric packing of the soil particles. In this way, the model differs from some earlier viscoplastic models and arguably provides a better conceptual description of time-dependent behaviour. Analytical solutions are developed for the simulation of drained and undrained strain-controlled triaxial compression tests. The model is then used to back-analyze the measured response of normally consolidated to moderately overconsolidated specimens of a soft estuarine soil in undrained triaxial compression. The model captures aspects of soil behaviour that cannot be simulated using time-independent elastic–plastic models. Specifically, it can capture the dependence of stress–strain relationships and undrained shear strength on strain rate, the development of irrecoverable plastic strains at constant stress (creep), and the relaxation of stresses at constant strain

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Temporal characterization and in vitro comparison of cell survival following the delivery of 3D-conformal, intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT)

A phantom was designed and implemented for the delivery of treatment plans to cells in vitro. Single beam, 3D-conformal radiotherapy (3D-CRT) plans, inverse planned five-field intensity-modulated radiation therapy (IMRT), nine-field IMRT, single-arc volumetric modulated arc therapy (VMAT) and dual-arc VMAT plans were created on a CT scan of the phantom to deliver 3 Gy to the cell layer and verified using a Farmer chamber, 2D ionization chamber array and gafchromic film. Each plan was delivered to a 2D ionization chamber array to assess the temporal characteristics of the plan including delivery time and 'cell's eye view' for the central ionization chamber. The effective fraction time, defined as the percentage of the fraction time where any dose is delivered to each point examined, was also assessed across 120 ionization chambers. Each plan was delivered to human prostate cancer DU-145 cells and normal primary AGO-1522b fibroblast cells. Uniform beams were delivered to each cell line with the delivery time varying from 0.5 to 20.54 min. Effective fraction time was found to increase with a decreasing number of beams or arcs. For a uniform beam delivery, AGO-1552b cells exhibited a statistically significant trend towards increased survival with increased delivery time. This trend was not repeated when the different modulated clinical delivery methods were used. Less sensitive DU-145 cells did not exhibit a significant trend towards increased survival with increased delivery time for either the uniform or clinical deliveries. These results confirm that dose rate effects are most prevalent in more radiosensitive cells. Cell survival data generated from uniform beam deliveries over a range of dose rates and delivery times may not always be accurate in predicting response to more complex delivery techniques, such as IMRT and VMAT.

Approaching near-capacity on a multi-antenna channel using successive decoding and interference cancellation receivers

In this paper, we address the problem of designing multirate codes for a multiple-input and multiple-output (MIMO) system by restricting the receiver to be a successive decoding and interference cancellation type, when each of the antennas is encoded independently. Furthermore, it is assumed that the receiver knows the instantaneous fading channel states but the transmitter does not have access to them. It is well known that, in theory, minimum-mean-square error (MMSE) based successive decoding of multiple access (in multi-user communications) and MIMO channels achieves the total channel capacity. However, for this scheme to perform optimally, the optimal rates of each antenna (per-antenna rates) must be known at the transmitter. We show that the optimal per-antenna rates at the transmitter can be estimated using only the statistical characteristics of the MIMO channel in time-varying Rayleigh MIMO channel environments. Based on the results, multirate codes are designed using punctured turbo codes for a horizontal coded MIMO system. Simulation results show performances within about one to two dBs of MIMO channel capacity.