Intramasseteric Solitary Myofibroma: A Case Report.

Solitary myofibroma or infantile myofibroma is a rare spindle cell neoplasm that generally affects infants before the age of 2 years but cases in young children and adults have been described. Although the location of infantile myofibroma in the oral and maxillofacial region has been described, the intramasseteric location of the lesion is very uncommon. A thorough assessment of histopathological and immunohistochemical characteristics is necessary to have a correct diagnosis. Treatment relies on surgical resection. In this article, we present a rare clinical case of a 15-year-old patient with a myofibroma of the masseteric muscle and its management.

Double voltage vector model predictive control for grid-connected cascade H-bridge multilevel inverter with fixed switching frequency.

Model predictive control (MPC) is an outstanding control method which can achieve superior dynamic response, nonlinear control and multi-objective collaborative control. However, because of unfixed switching frequency, the harmonic spectrum of the output current is dispersed, which would make it difficult to design the output filter. In this work, a double voltage vector model predictive control (DVV-MPC) algorithm for grid-connected cascade H-bridge (CHB) multilevel inverter is presented. The algorithm not only has the advantages of MPC but also produces fixed the switching frequency of the inverter. It is realized by a modulation stage with two adjacent voltage vectors in one switching cycle. And the duty cycle of the associated voltage level is obtained by minimizing an additional cost function. This method is suitable for the cascades of n cells inverter. For reducing the computation burden and eliminating the computation delay, adjacent regions prediction method and time-delay compensation method are applied to the inverter. Finally, in this paper, the proposed strategy is applied to a single-phase grid-connected 7-level CHB inverter. Simulation and experiment are executed to show the advantages of the proposed control algorithm in dynamic state and steady state.

High-consistency proton exchange membrane fuel cells enabled by oxygen-electron mixed-pathway electrodes via digitalization design.

Proton exchange membrane (PEM) fuel cell has been regarded as a promising approach to the decarbonization and diversification of energy sources. In recent years, durability and cost issues of PEM fuel cells are increasingly significant with the rapid increase of power density. However, the failure to maintain the cell consistency, as one major cause of the above issue, has attracted little attention. Therefore, this study intends to figure out the underlying cause of cell inconsistency and provide solutions to it from the perspective of multi-physics transport coupled with electrochemical reactions. The PEM fuel cells with electrodes under two compression modes are firstly discussed to fully explain the relationship of cell performance and consistency to electrode structure and multi-physics transport. The result indicates that one main cause of cell inconsistency is the intrinsic conflict between the separated transport and cooperated consumption of oxygen and electron throughout the active area. Then, a mixed-pathway electrode design is proposed to reduce the cell inconsistency by enhancing the mixed transport of oxygen and electron in the electrode. It is found that the mixing of pathways in electrodes at under-rib region is more effective than that at the under-channel region, and can achieve an up to 40% reduction of the cell inconsistency with little (3.3%) sacrificed performance. In addition, all the investigations are implemented based on a self-developed digitalization platform that reconstructs the complex physical-chemical system of PEM fuel cells. The fully observable physical information of the digitalized cells provides strong support to the related analysis.

Fundamental frequency sequence amplitude estimator for power and energy applications.

A grid-synchronization-based fundamental frequency positive-sequence (FFPS) and negative-sequence (FFNS) amplitudes estimation technique is proposed for unbalanced and distorted grid. In this technique, the sequence amplitudes are extracted by extracting the phase-angle of the FFPS and FFNS components. The extracted phase-angles have DC and double frequency AC components. The AC component is filtered out by using a Moving Average Filter (MAF) of appropriate window length. From the extracted phase-angle, the unknown frequency can be estimated by using a suitable controller. A frequency-fixed equidistant samples-based pre-loop filter is also applied to eliminate the effect of measurement offset. The proposed technique has a very simple structure and is easy to tune. Small-signal modeling-based stability analysis and gain tuning procedure are also provided. The proposed technique strikes a good balance between fast convergence and disturbance rejection capability. Comparative numerical simulation and experimental results with similar other techniques demonstrate the suitability and performance enhancement by the proposed technique.

Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis.

The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach.

Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning.

Atomic force microscopy (AFM) plays an important role in nanoscale imaging application. AFM works by oscillating a microcantilever on the surface of the sample being scanned. In this process, estimating the amplitude of the cantilever deflection signal plays an important role in characterizing the topography of the surface. Existing approaches on this topic either have slow dynamic response e.g., lock-in-amplifier or high computational complexity e.g., Kalman filter. In this context, gradient estimator can be considered as a trade-off between fast dynamic response and high computational complexity. However, no constructive tuning rule is available in the literature for gradient estimator. In this paper, we consider small-signal modeling and tuning of gradient estimator. The proposed approach greatly simplifies the tuning procedure. Numerical simulation and experimental results are provided to demonstrate the suitability of the proposed tuning procedure.

[Rhinoscleroma of the cavum with expression in cervical lymph nodes: about a case]. / Rhinosclerome du cavum avec expression ganglionnaire cervicale: à propos d'un cas.

Rhinoscleroma is a specific granulomatous and chronic disorder with insidious evolution. It is causes by pathogen Klebsiella rhinoscleromatis. It mainly occurs in the nasal cavities and positive diagnosis is sometimes problematic. We report the case of a 19 year old female patient presenting with rhinoscleroma considered atypical due to its rare nasopharyngeal localization and its exceptional association with cervical lymphadenopathy in the right submandibular angle region. Anatomopathological exam revealed Mikulicz's cells, thus enabling the diagnosis. The patient underwent antibiotic therapy with ciprofloxacin for 16 weeks associated with washing of nasal cavities with physiological saline solution. Patient's outcome was favorable during the 14-month follow-up period.

An imbalance fault detection method based on data normalization and EMD for marine current turbines.

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method.

Load estimator-based hybrid controller design for two-interleaved boost converter dedicated to renewable energy and automotive applications.

This paper is devoted to the development of a hybrid controller for a two-interleaved boost converter dedicated to renewable energy and automotive applications. The control requirements, resumed in fast transient and low input current ripple, are formulated as a problem of fast stabilization of a predefined optimal limit cycle, and solved using hybrid automaton formalism. In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller. Mathematical proofs are provided with simulations to illustrate the effectiveness and the robustness of the proposed controller despite different disturbances. Furthermore, a fuel cell system supplying a resistive load through a two-interleaved boost converter is also highlighted.

[An unusual cause of high dysphagia: schwannoma of the preepiglottic space]. / Une cause inhabituelle de dysphagie haute: schwannome de la l oge hyo-thyro-épiglottique.

Schwannomas are well encapsulated mesenchymal tumors of peripheral nerves, with slow growth. The laryngeal schwanomma is exceptional, we present a case not described in the literature of shwannoma in the pre epiglottic space. A 50-year-old woman, who had a history of foreign body sensation for 4 years ago, progressing to high dysphagia and hoarseness. Direct laryngoscopy in suspension demonstrated a regular submucosal mass in the supraglottic space, reducing pharyngo-laryngeal space. CT and MRI concluded with a benign tumor of the preepiglottic space. External surgical excision was performed and a primary tracheotomy was required. The histological examination with an immunochemical study confirmed a benign schwannoma. There was no complication postoperatively. The outcome was excellent after 2 years of follow-up.

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes.

The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines.

A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets.

Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems.

The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework.

Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement.

This paper deals with the fault ride-through capability assessment of a doubly fed induction generator-based wind turbine using a high-order sliding mode control. Indeed, it has been recently suggested that sliding mode control is a solution of choice to the fault ride-through problem. In this context, this paper proposes a second-order sliding mode as an improved solution that handle the classical sliding mode chattering problem. Indeed, the main and attractive features of high-order sliding modes are robustness against external disturbances, the grids faults in particular, and chattering-free behavior (no extra mechanical stress on the wind turbine drive train). Simulations using the NREL FAST code on a 1.5-MW wind turbine are carried out to evaluate ride-through performance of the proposed high-order sliding mode control strategy in case of grid frequency variations and unbalanced voltage sags.

Cervical lymph node metastasis in chromophobe renal cell carcinoma: a case report and review of the literature.

The metastasis of chromophobe renal cell carcinoma to head and neck region, described herein, has never been reported before to our knowledge. A 56-year-old woman with a history of nephrectomy, that revealed chromophobe renal cell carcinoma six years before, presented left cervical mass. Imaging showed with left cervical lymphadenopathies and thyroid nodule. Surgery with histopathological examination confirmed that it was a left central and lateral jugular lymph node metastasis of chromophobe renal cell carcinoma treated postoperatively by antiangiogenic therapy. The patient was successfully treated by surgery and antiangiogenic drugs with stabilization and no recurrence of the metastatic disease. The case and the literature reported here support that chromophobe renal cell carcinoma can metastasize to the head and neck region and should preferentially be treated with surgery and antiangiogenic therapy because of the associated morbidity and quality-of-life issues.

An improved fault-tolerant control scheme for PWM inverter-fed induction motor-based EVs.

This paper proposes an improved fault-tolerant control scheme for PWM inverter-fed induction motor-based electric vehicles. The proposed strategy deals with power switch (IGBTs) failures mitigation within a reconfigurable induction motor control. To increase the vehicle powertrain reliability regarding IGBT open-circuit failures, 4-wire and 4-leg PWM inverter topologies are investigated and their performances discussed in a vehicle context. The proposed fault-tolerant topologies require only minimum hardware modifications to the conventional off-the-shelf six-switch three-phase drive, mitigating the IGBTs failures by specific inverter control. Indeed, the two topologies exploit the induction motor neutral accessibility for fault-tolerant purposes. The 4-wire topology uses then classical hysteresis controllers to account for the IGBT failures. The 4-leg topology, meanwhile, uses a specific 3D space vector PWM to handle vehicle requirements in terms of size (DC bus capacitors) and cost (IGBTs number). Experiments on an induction motor drive and simulations on an electric vehicle are carried-out using a European urban driving cycle to show that the proposed fault-tolerant control approach is effective and provides a simple configuration with high performance in terms of speed and torque responses.