An Integrated Physiological Model of the Lung Mechanics and Gas Exchange Using Electrical Impedance Tomography in the Analysis of Ventilation Strategies in ARDS Patients

Mouloud Denai, M. Mahfouf, A. Wang, D. A. Linkens, and G. H. Mills, 'An Integrated Physiological Model of the Lung Mechanics and Gas Exchange Using Electrical Impedance Tomography in the Analysis of Ventilation Strategies in ARDS Patients'. Paper presented at the 3rd International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2010), 20 - 23 January 2010, Valencia, Spain.Peer reviewedFinal Published versio

High Performance Multicell Series Inverter-Fed Induction Motor Drive

This document is the Accepted Manuscript version of the following article: M. Khodja, D. Rahiel, M. B. Benabdallah, H. Merabet Boulouiha, A. Allali, A. Chaker, and M. Denai, ‘High-performance multicell series inverter-fed induction motor drive’, Electrical Engineering, Vol. 99 (3): 1121-1137, September 2017. The final publication is available at Springer via DOI: https://doi.org/10.1007/s00202-016-0472-4.The multilevel voltage-source inverter (VSI) topology of the series multicell converter developed in recent years has led to improved converter performance in terms of power density and efficiency. This converter reduces the voltage constraints between all cells, which results in a lower transmission losses, high switching frequencies and the improvement of the output voltage waveforms. This paper proposes an improved topology of the series multicell inverter which minimizes harmonics, reduces torque ripples and losses in a variable-speed induction motor drive. The flying capacitor multilevel inverter topology based on the classical and modified phase shift pulse width modulation (PSPWM, MPSPWM) techniques are applied in this paper to minimize harmonic distortion at the inverter output. Simulation results are presented for a 2-kW induction motor drive and the results obtained demonstrate reduced harmonics, improved transient responses and reference tracking performance of the voltage in the induction motor and consequently reduced torque ripplesPeer reviewe

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

This document is the Accepted Manuscript of the following article: Mohammed Chalouli, Nasr-eddine Berrached, and Mouloud Denai, ‘Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction’, Journal of Failure Analysis and Prevention, Vol. 17 (5): 1053-1066, October 2017. Under embargo. Embargo end date: 31 August 2018. The final publication is available at Springer via DOI: https://doi.org/10.1007/s11668-017-0343-y.Finding reliable condition monitoring solutions for large-scale complex systems is currently a major challenge in industrial research. Since fault diagnosis is directly related to the features of a system, there have been many research studies aimed to develop methods for the selection of the relevant features. Moreover, there are no universal features for a particular application domain such as machine diagnosis. For example, in machine bearing fault diagnosis, these features are often selected by an expert or based on previous experience. Thus, for each bearing machine type, the relevant features must be selected. This paper attempts to solve the problem of relevant features identification by building an automatic fault diagnosis process based on relevant feature selection using a data-driven approach. The proposed approach starts with the extraction of the time-domain features from the input signals. Then, a feature reduction algorithm based on cross-correlation filter is applied to reduce the time and cost of the processing. Unsupervised learning mechanism using K-means++ selects the relevant fault features based on the squared Euclidian distance between different health states. Finally, the selected features are used as inputs to a self-organizing map producing our health indicator. The proposed method is tested on roller bearing benchmark datasets.Peer reviewe

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Power management and control strategies for off-grid hybrid power systems with renewable energies and storage

This document is the Accepted Manuscript of the following article: Belkacem Belabbas, Tayeb Allaoui, Mohamed Tadjine, and Mouloud Denai, 'Power management and control strategies for off-grid hybrid power systems with renewable energies and storage', Energy Systems, September 2017. Under embargo. Embargo end date: 19 September 2018. The final publication is available at Springer via https://doi.org/10.1007/s12667-017-0251-y.This paper presents a simulation study of standalone hybrid Distributed Generation Systems (DGS) with Battery Energy Storage System (BESS). The DGS consists of Photovoltaic (PV) panels as Renewable Power Source (RPS), a Diesel Generator (DG) for power buck-up and a BESS to accommodate the surplus of energy, which may be employed in times of poor PV generation. While off-grid DGS represent an efficient and cost-effective energy supply solution particularly to rural and remote areas, fluctuations in voltage and frequency due to load variations, weather conditions (temperature, irradiation) and transmission line short-circuits are major challenges. The paper suggests a hierarchical Power Management (PM) and controller structure to improve the reliability and efficiency of the hybrid DGS. The first layer of the overall control scheme includes a Fuzzy Logic Controller (FLC) to adjust the voltage and frequency at the Point of Common Coupling (PCC) and a Clamping Bridge Circuit (CBC) which regulates the DC bus voltage. A maximum power point tracking (MPPT) controller based on FLC is designed to extract the optimum power from the PV. The second control layer coordinates among PV, DG and BESS to ensure reliable and efficient power supply to the load. MATLAB Simulink is used to implement the overall model of the off-grid DGS and to test the performance of the proposed control scheme which is evaluated in a series of simulations scenarios. The results demonstrated the good performance of the proposed control scheme and effective coordination between the DGS for all the simulation scenarios considered.Peer reviewedFinal Accepted Versio

Fuzzy and Neural Control of an Induction Motor

This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system

Global Stability of Linearizing Control With a New Robust Nonlinear Observer of The Induction Motor

This paper mainly deals with the design of an advanced control law with an observer for a special class of nonlinear systems. We design an observer with a gain as a function of speed. We study the solution to the output feedback torque and rotor flux-tracking problem for an induction motor model given in the natural frame. We propose a new robust nonlinear observer and prove the global stability of the interlaced controller-observer system. The control algorithm is studied through simulations and applied in many configurations (various set points, flux and speed profiles and torque disturbances), and is shown to be very efficient

Factors Affecting Ra-223 Therapy: Clinical Experience After 532 Cycles From A Single Institution

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Purpose The aim of this study was to identify baseline features that predict outcome in Ra-223 therapy. Methods We retrospectively reviewed 110 patients with metastatic castration-resistant prostate cancer treated with Ra-223. End points were overall survival (OS), progression-free survival (PFS), bone event-free survival (BeFS), and bone marrow failure (BMF). The following parameters were evaluated prior to the first Ra-223 cycle: serum levels of hemoglobin (Hb), prostate-specific antigen (PSA), alkaline phosphatase (ALP), Eastern Cooperative Oncology Group (ECOG) status, pain score, use of chemotherapy, and external beam radiation therapy (EBRT). During/after Ra-223 we evaluated: the total number of radium cycles (Ra-Tot), the PSA doubling time (PSA(DT)), and the use of chemotherapy, EBRT, abiraterone, and enzalutamide. Results A significant reduction of ALP (p < 0.001) and pain score (p = 0.041) occurred throughout the (223) Ra cycles. The risk of progression was associated with declining ECOG status [hazard ratio (HR) = 3.79; p < 0.001] and decrease in PSA(DT) (HR = 8.22; p < 0.001). Ra-Tot, ALP, initial ECOG status, initial pain score, and use of abiraterone were associated with OS (p <= 0.008), PFS (p <= 0.003), and BeFS (p <= 0.020). Ra-Tot, ALP, initial ECOG status, and initial pain score were significantly associated with BMF (p <= 0.001) as well as Hb (p < 0.001) and EBRT (p=0.009). On multivariable analysis, only Ra-Tot and abiraterone remained significantly associated with OS (p < 0.001; p=0.033, respectively), PFS (p < 0.001; p=0.041, respectively), and BeFS (p < 0.001; p=0.019, respectively). Additionally, Ra-Tot (p=0.027) and EBRT (p=0.013) remained significantly associated with BMF. Conclusion Concomitant use of abiraterone and Ra-223 seems to have a beneficial effect, while the EBRT may increase the risk of BMF.431820James E. Anderson Distinguished Professorship EndowmentCancer Center Support Grant (NCI) [P30 CA016672]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP [2014/03317-8

Suggestive Therapeutic Pathways Using Hyper-Heuristics

Therapeutic decision support can be used to promptly assist clinical decision making process. This paper presents a novel approach to interpreting multiple data streams in intensive care environments, the resulting model can be used to correct and maintain patients' health whilst treating underlying illnesses. Rather than simply directing which treatments to be applied, multiple suggestive treatment pathways can be provided allowing several "what-if" scenarios to choose from. Hyper-heuristics are used to guide the treatments and therapeutic pathways selection. Algorithmic validation is made using a human cardiovascular system model parameterised with various post surgery conditionsNon peer reviewe

Recursive Fuzzy Predictions of Future Patient Paths to Support Clinical Decision Making in ICU

Non peer reviewe