Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of the Remaining Useful Life Prediction for Proton Exchange Membrane Fuel Cells

With the advantages of high efficiency, light weight and non-pollution, Proton Exchange Membrane Fuel Cells (PEMFCs) can be used in portable devices, transportation and distributed power supply systems. Nevertheless, the durability and cost are two key barriers for their large-scale commercialization in light-duty vehicle transportation applications. It is necessary to predict the future State of Health (SoH) and future behaviors. Then the operating parameters can be optimized with time or Condition-based Maintenance (CBM) can be activated to extend its life. Prognostics have the ability to estimate the Remaining Useful Life (RUL) before the failure occurs. It seems to be a great solution to deal with the durable issue of PEMFCs. PEMFCs have wide range of applications. Besides, PEMFCs have the property of multi-physics, multi-scales, and nonlinearity. Moreover, the degradation phenomenon has a relationship with the mission profiles and the external disturbances. Then all the degradation mechanisms of the various fuel cells components can hardly be completely understood. Three kinds of prognostic methods are commonly distinguished: model-based, data-driven, and hybrid method. Developing novel and efficient methods to improve the prognostic accuracy, to decrease the computational burden, and to reinforce the robustness and dynamics are what should be done in the next steps.
Experimental Study on the Influence of Different Opening Speed on Post-Arc Current in DCCB

The pre-charged capacitor method, used in the active current injection circuit breaker in DC system, is an effective approach to realize the current injection. However, in practical applications, the performances of post-arc among different opening speed are still unknown, which have been an obstacle that blocks the design of MV DCCB. To solve this problem, high resolution measuring equipment was used to investigate the performances of vacuum circuit breaker in post-arc period. The experimental results show that the peak value of the post-arc current can be up to 6A and the post-arc current decays to zero within 10μs after current-zero. Besides, with the increase of opening speed, the fluctuation of post-arc charge increases continuously, while the average remains basically unchanged.
Asynchronous Integration of Real-Time Simulators for HIL-based Validation of Smart Grids

As the landscape of devices that interact with the electrical grid expands, also the complexity of the scenarios that arise from these interactions increases. Validation methods and tools are typically domain specific and are designed to approach mainly component level testing. For this kind of applications, software and hardware-in-the-loop based simulations as well as lab experiments are all tools that allow testing with different degrees of accuracy at various stages in the development life-cycle. However, things are vastly different when analysing the tools and the methodology available for performing system-level validation. Until now there are no available well-defined approaches for testing complex use cases involving components from different domains. Smart grid applications would typically include a relatively large number of physical devices, software components, as well as communication technology, all working hand in hand. This paper explores the possibilities that are opened in terms of testing by the integration of a real-time simulator into co-simulation environments. Three practical implementations of such systems together with performance metrics are discussed. Two control-related examples are selected in order to show the capabilities of the proposed approach.
Inferring Emotions from Touching Patterns

In this paper, we propose a feature-based model to recognize emotions via touching patterns of individuals playing a game on a typical tablet. In this work, novel features, such as Angular Velocity/Acceleration, Angle, Curl, Area and number of strokes within a time window, are introduced and the gold-standard of the data is determined automatically via subjects' facial expressions. The results show that the approach is promising and the model is able to recognize all the six basic emotions, with a performance of 71.92 % ±0.51. In addition, the recognition of valence and arousal reaches correlation coefficients equal to 0.76 and 0.78 respectively.
Joint Channel and Power Allocation Based on Generalized Nash Bargaining Solution in Device-to-Device Communication

In this paper, a new generalized Nash bargaining framework is proposed for joint channel and power allocation in device-to-device (D2D) communication underlying cellular networks. In the considered system, cellular users (CUs) must jointly determine to share radio resources with D2D users (DUs) and to charge the DUs accordingly. The proposed framework aims to maximize the overall throughput of the communication system by guaranteeing the minimum rate of each CU and proportional fairness and efficient power allocation among the CUs and DUs. To make this NP-hard problem more tractable, it is decomposed into two sub-problems: channel assignment and power allocation. First, an optimal channel assignment strategy is derived using a max-weighted max-flow algorithm. Then, the optimal power allocation strategy for both DUs and CUs is analyzed using the Lagrangian multiplier method. Numerical results are presented to show the throughput performance characteristics of different resource allocation solutions. Comparisons between the proposed and traditional policies show the significant effect of fairness on the transmission performance.
Novel Three-Phase Multi-Level Inverter with Reduced Components

A new multilevel converter topology is proposed in this paper. Low component count and compact design are the main features of the proposed topology. Furthermore, the proposed converter is a capacitor-, inductor-, and diode-free configuration, allowing reducing the converter footprint, increasing the lifetime and simplifying the control strategy. Further, a comparative study is carried out to highlight the merits of the proposed circuit as compared to existing multilevel topologies. Finally, simulation results for the three-level version using different modulation strategies are presented.
Thermal Impact on Powertrain Efficiency Improvement for Two Wheels Electric Vehicle

The energy required by a two wheels electric vehicle (TWEV) to complete a trip is lower than common electric cars or internal combustion vehicles. However, there are considerable losses along the electric driving chain. Those losses added to a limited energy storage cause an impact over the TWEV autonomy. This appears to be the main factor, which limits the large-scale market penetration of TWEV. This paper aims to analyze the multiphysic behavior of the complete power-chain in order to study its effect on its energetic losses. Even when many dynamics model oriented to hardware design approach can represent the come multiphysic behavior of one or two elements of the power train, the approach proposed in this paper presents a balanced representation of all power chain able to be used in real-time optimization. This study will help to improve the capabilities of an onboard TWEV efficiency estimator system which uses a longitudinal force model. As a conclusion, the error of autonomy estimation is compared with thermal considerations and without them according to different operating points.
Combining Gated Convolutional Networks and Self-Attention Mechanism for Speech Emotion Recognition

Discrete speech emotion recognition (SER), the assignment of a single emotion label to an entire speech utterance, is typically performed as a sequence-to-label task. The predominant approach to SER to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we introduce new gated convolutional networks and apply them to SER, which can be more efficient since they allow parallelization over sequential tokens. We present a novel model architecture that incorporates a gated convolutional neural network and a temporal attention-based localization method for speech emotion recognition. To the best of the authors' knowledge, this is the first time that such a hybrid architecture is employed for SER. We demonstrate the effectiveness of our approach on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus. The experimental results demonstrate that our proposed model outperforms current state-of-the-art approaches.
Concept for fast breast cancer ultrasound screening in addition to mammography – first clinical results

In breast cancer screening, x-ray mammography is the standard imaging modality. For clarification of suspicious findings, handheld ultrasound (US) is used as an adjunct modality. It is so far not included in screening due to the lack of standardization, high costs and the risk of increased false positives within screening population. First attempts to standardize US with automated breast US are still not completely fulfilling clinical needs, e.g. workflow and reading are claimed not to be fast enough. Therefore, various groups introduced new concepts of fast US screening, providing standardized images in the same orientation as the mammography images, leading to a speed-up of reading and reporting.To acquire US images in an x-ray mammography environment, an automated breast US transducer was inserted into a modified compression paddle. After mammography or tomosynthesis, an automated breast US scan was performed. We investigated new concepts to optimize workflow speed and approaches to achieve nearly full US coverage of the breast in a clinical study.We successfully integrated automated US scanning in a clinical workflow with less than one minute for the additional scan. Compared to the workflows of automated breast US and handheld US, the proposed scanning concept provides a high time and cost saving potential. Preliminary results indicate a high breast area coverage of the US scan. The first clinical results of our method demonstrated a proof of concept for a technical solution to include US screening into the standard breast cancer screening workflow. The standard workflow is changed minimally and the additional time investment is low since no patient repositioning or system adjustments are needed between both scanning modalities.
Energy Management in Grid Assisted BESS Integrated Solar PV Based Smart Charging Station

This paper presents the energy management in a solar PV based charging station integrated with battery energy storage system (BESS). The provision of optional exchange of power by the DC grid with the utility grid has been created here. Power flow through the utility grid is controlled using a voltage source converter (VSC). The loads considered are the Electric Vehicles (EVs) batteries. Load demand was met by the maximum power obtained from the solar PV (Photo Voltaic) using Maximum Power Point Tracking (MPPT) controller. An automated control of energy flow among solar PV, BESS and load battery is proposed using DC-DC converters. The dip in DC microgrid voltage, because of excess load demand beyond the available combined maximum power from solar PV and BESS, is compensated by delivering deficient power from utility grid. PSCAD simulation results are presented here to validate the proposed scheme.
Comprehensive Study on Chip-Integrated Germanium Pin Photodetectors for Energy-Efficient Silicon Interconnects

Optical interconnects are promising alternatives to copper-based wirings in on-chip communications. Recent advances in integrated group-IV nanophotonics should address a range of challenges related with speed, energy consumption, and cost. Monolithically integrated germanium pin photodetectors on silicon-on-insulator (SOI) waveguides are indispensable devices in this buoyant research field. Here, we comprehensively investigate the opto-electrical properties of hetero-structured pin photodetectors. All photodetectors were fabricated on top of 200-mm SOI substrates using industrial-scale semiconductor manufacturing processes. Under a low-bias voltage supply of 1 V, pin photodetectors exhibit dark-currents from 5 nA to 100 nA, dark current densities from 0.404 A/cm2 to 0.808 A/cm2, responsivities in a range of 0.17 A/W to 1.16 A/W, and cut-off frequencies from 7 GHz to 35 GHz, respectively. Such achievements make them promising for use in power-efficient optical links operating at 40 Gbps, with a device energy dissipation of only few fJ per bit.
Neural Network Based Prediction and Analysis for NB-IoT Network Location

Traditional studies focused on the position of mobile device for the narrow-band Internet of Things (NB-IoT)are facing two problems: the lack of a realistic model simulating the behavior of mobile devices and the failure to consider the impacts from the surrounding complicated environment. In this paper, a position system model is introduced to describe the distribution of the mobile devices, and the surrounding enviroment like buildings and fading channel is analyzed by NS3. Compared to previous works, the feed-forward neural network is used to train the position of mobile device in a complex environment, and the trained positioning model is calculated. Futhermore, under cellular network, a comparison is carried out considering the different number of mobile devices. The trained neural network model will perform position prediction based on the test data, and will be examined using appropriate error measurement metrics. The results show that the positioning prediction model obtained through neural network training has been improved greatly.
Data-Driven Sensor Array Subsampling for Plane-Wave Ultrasound Imaging

Ultrafast plane-wave ultrasound imaging offers very high frame rates but entails large volumes of backscattered data collected by a sensor array over multiple plane-wave emissions at different angles. We propose a simple method for reducing the total amount of sampled data (subject to subsequent beamforming and coherent compounding). First, we acquire the zeroangle data in full, and then perform deterministic subsampling of the remaining nonzero-angle data. Our subsampling patterns are angle-specific and derived based on the zero-angle data.
A Novel Multilevel T-Type Indirect Matrix Converter for Three-phase Open-end AC Loads

In this work a novel topology featuring an Indirect Matrix Converter (IMC) supplying a three-phase open-end load is proposed to allow multiple levels of voltage while reducing common mode voltages (CMV) at load terminals. The topology features a T-Type three phase inverter connected to the grid neutral terminal, allowing phase to phase or phase to neutral voltages to be supplied to the load. A comparison with the already known IMC open-end load scheme, based on two voltage source inverters (VSI), is performed achieving promising results in terms of Common Mode Voltage reduction at the load terminals, while achieving current waveforms with lower distortion. The improvement of CMV waveforms is useful for motor drives, reducing drive bearing currents and increasing overall lifespan.
High Quality Ultrafast Imaging with Transmit Extrapolation

Plane wave imaging (PWI) and diverging wave imaging (DWI) can achieve high frame rate by coherently compounding beamsum data from broad transmit beams at different angles. However, many transmits are still needed as image quality degradation occurs with reduced number of transmit angles in PWI/DWI. Specifically, a reduced transmit angular span results in degraded lateral resolution in the synthetic transmit beam. We propose a transmit extrapolation (TEX) technique, which employs a linear prediction scheme to predict unavailable beamsum data based on beamsum data available from a limited number and span of PW/DW transmit angles. The original and predicted beamsum data are then coherently compounded to achieve image quality superior to that with only the original beamsum data, thereby overcoming the inherent trade-off between frame rate and image quality. We evaluate our TEX technique on the PICMUS dataset. Our results suggest that TEX has potential to enable ultrafast imaging without compromising image quality.
Measuring elastic nonlinearity in a soft solid using a tilted acoustic radiation force for shear wave excitation

Excitation of multiple wave modes using shear wave elastography can result in additional information about the tissue’s material characteristics and, potentially, improve disease diagnosis. Theoretically, tilting the acoustic radiation force excitation axis with respect to the material’s symmetry axis should excite several wave modes in the material. In this work, we have experimentally demonstrated proof of concept in a uniaxially stretched phantom, while increasing the stretch level. Tilted acoustic radiation force experiments showed a clearly visible second wave mode across the stretch direction for larger stretches (>160%).
Mobile Application for Bumps Detection and Warning Utilizing Smartphone Sensors

It is important to detect speed bumps and potholes to protect vehicles from damage when driving over them. Because of the increasing number of accidents resulting from passing over bumps at high speeds, there is a need for a mobile application that detects those bumps and warns drivers before reaching them. We propose a mobile application that uses smartphones sensors to tackle this problem. The app works on reading data captured by smartphone sensors and analyzing them. Using a smartphone, we do not need vehicle-mounted sensors. GPS and accelerometer sensors are the main sensors used to capture data. Using those sensors, we obtain data of position, speed, and acceleration, which are needed for detecting bumps and their location and later the marked bump location, is used for warning drivers. In contrast to most applications in this field, this work uses internal sensors of smartphone, resulting in an inexpensive system available at the hands of every user. Experimental results show basic functionality of the system.
Polyaniline-Based Nanocomposites for Environmental Remediation

With growth in civilisation and industrialisation, there is an increase in the release of toxic heavy metal ions and dyes into water system, which is of public concern. As a result, appropriate treatment methods have to be implemented in order to mitigate and prevent water pollution. The discovery of nanotechnology has led to the development and utilisation of various nanoadsorbent for the removal of pollutants from water. PANI nanostructures and nanocomposites are noble adsorbents that have gained popularity in addressing water pollution issues and have been reported in literature. In this chapter, the main focus is on the synthesis of PANI nanocomposites and nanostructures and their application as efficient adsorbents for water treatment. Detailed discussions on different synthetic routes and characterisation have been dedicated to applications of these materials and are compared for the adsorptive removal of heavy metal ions and dyes from water.
Operation of Nine-Phase Induction Machine under Single-Phase Open-Winding Fault Condition using Dodecagonal SVPWM and Hexagonal SVPWM

The advancement in power electronics enhanced the use of multiphase induction machines (IM) in many industrial applications. Multiphase induction machines (IM) provide better performance and are more reliable compared to conventional 3-phase IMs. However, research is still in progress to improve the reliability of multiphase IM under the fault condition. This paper presents two different techniques, namely dodecagonal space vector pulse width modulation (SVPWM) and hexagonal SVPWM, for the operation of the nine-phase IM under single-phase open-winding fault. The effectiveness of the proposed techniques is verified by using ANSYS FEM simulation and experimentation on laboratory prototype.
Synopsis of IEEE Std C95.1™-2019 “IEEE Standard for Safety Levels With Respect to Human Exposure to Electric, Magnetic, and Electromagnetic Fields, 0 Hz to 300 GHz”

The newly released IEEE Std C95.1™-2019 defines exposure criteria and associated limits for the protection of persons against established adverse health effects from exposures to electric, magnetic, and electromagnetic fields, in the frequency range 0 Hz to 300 GHz. The exposure limits apply to persons permitted in restricted environments and to the general public in unrestricted environments. These limits are not intended to apply to the exposure of patients by or under the direction of physicians and care professionals, as well as to the exposure of informed volunteers in scientific research studies, or to the use of medical devices or implants. IEEE Std C95.1™-2019 can be obtained at no cost from the IEEE Get Program https://ieeexplore.ieee. org/document/8859679.
Prediction of Psychological Flexibility with multi-scale Heart Rate Variability and Breathing Features in an “in-the-wild” Setting

Psychological flexibility (PF) has recently emerged as an important determinant in pain related outcomes. It is related to pain adaptation, social functioning and emotional well-being. A recent study indicates PF being a significant predictor of heart rate variability (HRV) and mediating relationship between HRV and pain interference. In recent years, HRV has been studied using non-linear dynamics approaches which better quantify the fractal behavior of the inter-beat interval time series. In this study, we propose the use of multi-scale HRV features for predicting PF. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. We show that fusion of breathing signal features further improves the performance showing the complementarity of two feature sets. We achieve an overall improvement of 4.54% F1-score over benchmark HRV features. These results indicate the importance of non-linear features for PF measurement. An accurate measurement of PF can help in developing pain and distress intervention methods by unobtrusive measurement of physiological signals using wearable sensors in real life conditions.
A Novel Two-Dimensional Displacement Estimation for Angled Shear Wave Elastography

This study aimed to estimate angled tissue motion for shear wave compounding applications. Shear wave elastography produces the quantitative elasticity biomarker for assessing the health status of tissues. In sheer wave compounding, steered shear waves are generated with different angles, and individual angle elasticity maps are averaged to improve tissue stiffness reconstruction. When shear waves are steered and the tissue motion is generated in multiple directions, traditional one dimensional (1D) displacement estimation fails in capturing actual shear wave amplitude and direction. This study investigated the use of two dimensional (2D) kernel to track angular shear wave motion, which resulted in the underestimation of displacement values. Consequently, a new method named as 2D proposed (2D-P) was used to calculate both axial and lateral motion components separately using 1D axial and lateral kernels. Final results indicated that, the proposed scheme produced an average improvement of 2.01 µm and 4.4 µm compared with the 1D axial cross correlation and 2D cross correlation based methods, respectively.
Radiation-enhanced nanobubble therapy: Monitoring treatment effects using photoacoustic imaging

In this work we demonstrate the potential of using nanobubbles (NBs) as radiation therapy enhancers for maximizing tumoral cell death. Photoacoustic (PA) imaging can be used to probe the mechanism of action of this treatment due to its ability to examine the oxygenation of tumors. In vivo experiments were performed in mice bearing prostate cancer tumors and the NB therapies were compared with conventional microbubble (MB) treatments combined with radiation. Our preliminary results show that NB combined with a single dose of 8 Gy radiation induce 40% tumor cell death compared to 20% observed with MB treatments. PA imaging suggest that NBs have an extravascular effect.