A Novel Speech Enhancement Method Using Fourier Series Decomposition and Spectral Subtraction for Robust Speaker Identification

Wireless Personal Communications - This paper presents a novel speech enhancement approach by combining Fourier series expansion and spectral subtraction. This approach is implemented in speaker...     

Energy efficient model predictive building temperature control

Many systems used in buildings for heating, ventilating, and air-conditioning waste energy because of the way they are operated or controlled. This paper explores the application of model predictive control (MPC) to air-conditioning units and demonstrates that the closed-loop performance and energy efficiency can be improved over conventional approaches. This work focuses on the problem of controlling the vapor compression cycle (VCC) in an air-conditioning system, containing refrigerant which is used to provide cooling. The VCC considered in this work has two manipulated variables that affect operation: compressor speed and the position of an electronic expansion valve. The system is subject to constraints, such as the range of permissible superheat, and also needs to regulate temperature variables to set points. An MPC strategy is developed for this type of system based on linear models identified from data obtained from a first-principles model of the VCC. The MPC strategy incorporates economic measures in the objective function as well as control objectives. Tests are carried out on a simulated VCC system that is linked to a simulation of a realistic building that is developed in the U.S. Department of Energy Computer Simulation Program, EnergyPlus. The MPC demonstrated significantly better tracking control relative to conventional approaches (a reduction of 70% in terms of the integral of squared error for step changes in the temperature set-point), while reducing the VCC energy requirements by 16%. The paper describes the control approach in detail and presents results from the tests.     

Robust model predictive control and fault handling of batch processes

This work considers the control of batch processes subject to input constraints and model uncertainty with the objective of achieving a desired product quality. First, a computationally efficient nonlinear robust Model Predictive Control (MPC) is designed. The robust MPC scheme uses robust reverse‐time reachability regions (RTRRs), which we define as the set of process states that can be driven to a desired neighborhood of the target end‐point subject to input constraints and model uncertainty. A multilevel optimization‐based algorithm to generate robust RTRRs for specified uncertainty bounds is presented. We then consider the problem of uncertain batch processes subject to finite duration faults in the control actuators. Using the robust RTRR‐based MPC as the main tool, a robust safe‐steering framework is developed to address the problem of how to operate the functioning inputs during the fault repair period to ensure that the desired end‐point neighborhood can be reached upon recovery of the full control effort. The applicability of the proposed robust RTRR‐based controller and safe‐steering framework subject to limited availability of measurements and sensor noise are illustrated using a fed‐batch reactor system. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Multimedia Tools and Applications - One of the known problems in security systems is to identify persons based on certain signatures. Biometrics have been adopted in security systems to identify...     

Fatigue Failure of Hydrocarbon Piping System

This paper presents a case study of hydrocarbon piping made of ASTM A376 TP321 stainless steel. The feedstock was of a two-phase flow hydrocarbon with small amount of sulfur. The pipe was found to be leaked at the weldment after 8 years of service. A combination of techniques including visual inspection, emission spectroscopy, radioscopy, scanning electron microscopy, and optical microscopy was applied to identify the possible cause of failure. The results showed that the cracks originated at the external wall of the pipe and were a consequence of synergy of fatigue loading during service due to improper piping support and stress concentrations as a result of the welding process. Deformation twinning and strain hardening may also contribute to the failure. According to failure and stress analysis done in this work, it is recommended that careful control of cyclic loading must be improved by, for example, implementation of a better spring constant for piping support system. In addition, a process for stress raiser-free welded structure should be of concern to achieve complete failure prevention in the future.     

Integrating data‐based modeling and nonlinear control tools for batch process control

A data‐based multimodel approach is developed in this work for modeling batch systems in which multiple local linear models are identified using latent variable regression and combined using an appropriate weighting function that arises from fuzzy c‐means clustering. The resulting model is used to generate empirical reverse‐time reachability regions (RTRRs) (defined as the set of states from where the data‐based model can be driven inside a desired end‐point neighborhood of the system), which are subsequently incorporated in a predictive control design. Simulation results of a fed‐batch reactor system under proportional‐integral (PI) control and the proposed RTRR‐based design demonstrate the superior performance of the RTRR‐based design in both a fault‐free and faulty environment. The data‐based modeling methodology is then applied on a nylon‐6,6 batch polymerization process to design a trajectory tracking predictive controller. Closed‐loop simulation results illustrate the superior tracking performance of the proposed predictive controller over PI control. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Clustering and power management for virtual MIMO communications in wireless sensor networks

Multi-input multi-output (MIMO) is a well-established technique for increasing the link throughput, extending the transmission range, and/or reducing energy consumption. In the context of wireless sensor networks (WSNs), even if each node is equipped with a single antenna, it is possible to group several nodes to form a virtual antenna array, which can act as the transmitting or receiving end of a virtual MIMO (VMIMO) link. In this paper, we propose energy-efficient clustering and power management schemes for virtual MIMO operation in a multi-hop WSN. Our schemes are integrated into a comprehensive protocol, called cooperative MIMO (CMIMO), which involves clustering the WSN into several clusters, each managed by up to two cluster heads (CHs); a master CH (MCH) and a slave CH (SCH). The MCH and SCH collect data from their cluster members during the intra-cluster communications phase and communicate these data to neighboring MCHs/SCHs via an inter-cluster VMIMO link. CMIMO achieves energy efficiency by proper selection of the MCHs and SCHs, adaptation of the antenna elements and powers in the inter-cluster communications phase, and using a cross-layer MIMO-aware route selection algorithm for multi-hop operation. We formally establish the conditions on the transmission powers of CHs and non-CHs that ensure the connectivity of the inter-cluster topology. Simulations are used to study the performance of CMIMO. The simulation results indicate that our proposed protocol achieves significant reduction in energy consumption and longer network life time, compared with non-adaptive clustered WSNs.     

Chebyshev approximation problem of multidimensional IIR digital filters in the frequency domain

This paper investigates the problem of simultaneous approximation of a prescribed multidimensional frequency response. The frequency responses of multidimensional IIR digital filters are used as nonlinear approximating functions. Chebyshev approximation theory and the notion of line homotopy are used to reveal the approximation properties of this set of IIR functions. A sign condition is derived to characterize a convex stable domain in this set. This sign condition can be incorporated into the optimization of the Chebyshev simultaneous approximation. The generally sufficient global Kolmogorov criterion is shown to be a necessary condition, for the characterization of best approximation, in the considered set of approximating functions. Thus, it can be incorporated, as a stopping constraint, in the design of the optimal filter. Moreover, the local Kolmogorov criterion is shown to be also necessary for the set of approximating functions. Finally, it is proved that the best approximation is a global minimum.

     

Data‐driven model predictive quality control of batch processes

The problem of driving a batch process to a specified product quality using data‐driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data” problem by integrating a previously developed data‐driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed‐loop simulations of a nylon‐6,6 batch polymerization process with limited measurements. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2852–2861, 2013