Miracle-dz/UniFAR-FTUs huggingface dataset:
Battery internal resistance estimation using a battery balancing system based on switched capacitors

Battery Management Systems (BMS) are key components in battery storage systems in order to guarantee their safe operation and improve their performance, reliability and efficiency. BMS monitor critical parameters in the battery as state-of-charge (SOC), state-of-health (SOH) or temperature. Direct measurement of these parameters is either impossible (e.g. SOC or SOH) or costly and unreliable (e.g. temperature), so they are often estimated. The internal resistor of the battery has already been successfully used to estimate these parameters. BMS can also include the function of balancing (equalizing) cells of a battery pack. Among all available equalizing systems, those based on switched capacitors are interesting due to their simplicity and easy scalability, making them a research focus for several years. This paper proposes an internal resistance (IR) estimation method for LiFePO4 batteries using signals naturally produced by a switched capacitor equalizer (SCE). The method operates online and without interfering with the regular operation of the equalizer.1
An Improved Version of a Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets (MEANDS-II)

In the present paper, the application of ManyObjective Evolutionary Algorithms (many-MOEA) was evaluated in a widely-known discrete problem: the Multi-objective Knapsack Problem (MKP). A characteristic of most chosen instances is to define high cardinality search spaces, since they challenge a recently proposed many-MOEA in the literature called MEANDS. We propose two modifications in order to improve the performance offered by MEANDS and to increase its applicability. One refers to the use of the Pareto dominance relaxation transformation known as CDAS. Another modification refers to the multiple subpopulations of non-dominated solutions, which were not restricted in size in the original version. We used the widely-known crowding distance metric to keep the subpopulation size within a pre-set threshold. As a result we arrived at a new version of the model called MEANDS-II. Experiments reported herein with MKP under formulations of 4 to 6 objectives show that MEANDS-II exceeds its predecessor, improving its performance as the complexity of the instance increases, both in number of objectives and number of items. Additionally, we compared the MEANDS-II performance against two well-known methods (NSGA-III and MOEA/D), showing a very competitive method in both convergence and diversity.
Expansion of the Junction Temperature Measurement via the Internal Gate Resistance to a wide range of Power Semiconductors

The temperature dependent internal gate resistance Rgi of MOS devices is suitable for online $T$j acquisition in operating application circuits. Recent publications have shown various measurement approaches and tested them for specific DUTs. In this work the issues arising when applying the Rgi-method to DUTs with a low Rgi and to SiC MOSFETs are described. If a device exhibits a low Rgi the measurement error due to parasitic resistance becomes relevant. For SiC MOSFETs frequency dependent effects, which can presumably be attributed to charge trapping at the gate oxide, have a significant influence on the measured data.
Interactive Game Application for Garbage Disposal using Image Processing and Location Based Services

Waste disposal is one of the major contemporary topics which is being discussed globally. The main reason for this issue to have such prominence is due to the fact that people are not aware how far-reaching this topic has become. In this paper we propose ScrapWrap, an interactive game for garbage disposal where the main objective is to make people aware how imperative it is to dispose garbage and assist people to have a change of attitude towards this complication. In Scrapwrap, people will be awarded considering several facts such as for notifying littered areas, for cleaning the area, for verifying the cleaned area, and for arranging cleaning campaigns. A chat-based community is created within ScrapWrap so that players could interact with each other, and this strategy is useful when arranging campaigns or scavenger hunt activities to clean the waste. An assistive chat bot is introduced to solve the problems a player might have, and to remind the event details and guide the player throughout the game flawlessly. The long-term goal of introducing Scrapwrap is to let people be aware of the importance of garbage disposal in an interactive manner and to improve the cleanliness of the surroundings.
High-Speed Avalanche Photodiodes With Wide Dynamic Range Performance

In this work, we demonstrate a novel top-illuminated avalanche photodiode (APD) with high-speed and wide dynamic range performance for coherent lidar applications, which needs simultaneous processing of weak light reflections from the object and a strong optical local-oscillator (LO) signal at the receiving end. By taking advantage of a partially depleted p-type In0.53Ga0. 47As absorbing layer and a thin In0.52Al0. 48As multiplication layer (88 nm), the demonstrated APDs exhibit a wide optical-to-electrical bandwidth (16 GHz) and high responsivity (2.5 A/W at 0.9 Vbr) near the saturation output current which is as high as >8 mA. Furthermore, the measured low excess noise (k = 0.14) characteristics of this device ensure its wide dynamic range performance. In addition, it should be noted that the measured nonlinear behaviors of these APDs are quite different from those of typical fast p-i-n photodiodes (PDs), which always show serious degradation in their O-E bandwidths at their saturation output. On the other hand, the measured bias dependent 3-dB O-E bandwidth of the demonstrated APDs can be pinned at 16 GHz without any degradation at around the saturation current output. Such distinct nonlinear APD behaviors can be attributed to the space-charge-screening effect induced gain reduction under high-power operation.
Backpropagation through Simulation: A Training Method for Neural Network-based Car-following

Learning human’s car-following behavior needs not only well-designed models but also effective training or calibration methods. Comparing with the vast amount of efforts on car-following modeling in literature, training methods are less studied. This research proposes a training method (BPTS - Backpropagation through Simulation) to reduce the long-term error of neural network-based car-following models, with multiple experimental validations. The training method uses a recurrent framework with simulation to generate long-term predictions for generic car-following models, and use gradient backpropagation to reduce accumulative error. The proposed training method can also calibrate other car-following models besides neural network-based models. In experimental validation, our studies yielded more than 30% error reduction in long-term (20 s) prediction for feed-forward Artificial Neural Network (ANN) and Long short-term memory (LSTM) models, and reduces the error on vehicle position by more than 1.0 meters, at the cost of that short-term (0.2 s) prediction error slightly increases. The proposed training method dramatically reduces the long-term prediction error of neural network-based car-following models.
Reuse of a Damaged Permanent Magnet Synchronous Motor for Torque Ripple and Acoustic Noise Elimination using a Novel Repetitive Observer

A permanent magnet synchronous motor (PMSM) has its bearing damaged and is irreversibly demagnetized due to overcurrent. Due to the changes in its electromagnetic configuration, the motor suffers from extremely high torque ripple, which produces intolerable speed ripple and acoustic noise. The aim of this paper is to evaluate if a repetitive observer (RO) can be used to smooth the rotational speed and suppress acoustic noise. The RO is functionally the same as a repetitive controller (RC), but is preferred due to its simplicity of tuning. Its effectiveness of reducing acoustic noise will be evaluated in this paper for the first time. Experimental test results show that the speed ripple and acoustic noise are reduced significantly. The work opens the possibility of reusing the damaged motor and still achieve high performance. The RO can also be applied to enhance the fault tolerance capability of a healthy motor.
Walking With Hand Motion/Force Control and CoM/VRP and Trunk Compliance for Disturbance Accommodation

Walking control along preplanned footholds is ensured with the well-known DCM/VRP stabilization approach. To accommodate an external disturbance, the proposed walking controller uses CoM/VRP compliance, i.e. modification of the original DCM trajectories in accordance with the measured/estimated disturbance. The disturbance can also be accommodated with the compliance in the trunk. The class of disturbances considered does not require the re-planning of the footholds. While walking, the hand(s) are allowed to contact the environment for postural robustness and/or to perform a desired hand motion/force control task, such as surface polishing or cleaning. The resulting hand reaction wrenches are also accommodated with the CoM/VRP compliance. Angular momentum damping with arm swinging can be employed to increase the robustness at the feet contacts. The proposed controller is quite efficient from the computational point of view since it does not involve a general iterative solver. It is shown via simulations that the robot is able to walk dynamically in a stable way, undisturbed by the external perturbations and the hand reaction wrenches.
Record GmSAT/SSSAT and PBTI Reliability in Si-Passivated Ge nFinFETs by Improved Gate-Stack Surface Preparation

This article reports Si-passivated Ge nFinFETs with significantly improved Gm<sub>SAT</sub>/SS<sub>SAT</sub> and positive bias temperature instability (PBTI) reliability enabled by an improved replacement metal gate (RMG) high-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> last process. SiO<sub>2</sub> dummy gate oxide (DGO) deposition on Ge fin is shown to form (Si<sub><italic>x</italic></sub>)Ge<sub>1-<italic>x</italic></sub>O<sub><italic>y</italic></sub>, which is, compared to a pure SiO<sub>2</sub>, more difficult to remove completely during the dry clean prior to the gate-stack formation. By extending the DGO removal clean, improved PBTI reliability, reduced <inline-formula> <tex-math notation="LaTeX">${D}_{{\text {IT}}}$ </tex-math></inline-formula>, and increased electron mobility are demonstrated. Moreover, by suppressing the Ge channel oxidation through the choice of less-oxidizing DGO or inserting an Si-cap layer prior to the DGO deposition, a greatly improved long-channel electron mobility is obtained at a scaled fin width. Finally, together with the PBTI maximum <inline-formula> <tex-math notation="LaTeX">${V}_{{\text {OV}}}$ </tex-math></inline-formula> of 0.13 V, the best Gm<sub>SAT</sub>/SS<sub>SAT</sub> of 5.4 is achieved, which is today’s record value among the sub-100-nm-<inline-formula> <tex-math notation="LaTeX">${L}_{g}$ </tex-math></inline-formula> n-channel Ge Fin and gate-all-around nanowire FETs. These results clearly show the importance of the pre-gate-stack channel surface preparation on the scaled Ge FinFETs to benefit from a previously optimized Si-passivated Ge gate-stack.
A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Accident analyses have revealed that pilots can fail to process auditory stimuli such as alarms, a phenomenon known as inattentional deafness. The motivation of this research is to develop a passive brain computer interface that can predict the occurence of this critical phenomenon during real flight conditions. Ten volunteers, equipped with a dry-EEG system, had to fly a challenging flight scenario while responding to auditory alarms by button press. The behavioral results disclosed that the pilots missed 36% of the auditory alarms. ERP analyses confirm that this phenomenon affects auditory processing at an early (N100) and late (P300) stages as the consequence of a potential attentional bottleneck mechanism. Intersubject classification was carried out over frequency features extracted three second epochs before the alarms’ onset using sparse representation for classification (SRC), sparse and dense representation (SDR) and more conventional approach such as linear discriminant analysis (LDA), shrinkage LDA and nearest neighbor (1NN). In the best case, SRC and SDR gave respectively a performance of 66.9% and 65.4% of correct mean classification rate to predict the occurrence of inattentional deafness, outperforming LDA (60.6%), sLDA (60%) and 1 NN (59.6%). These results open promising perspectives for the implementation of neuroadaptive automation with as ultimate goal to enhance alarm stimulation delivery so that it is perceived and acted upon.
Physiological Pattern of Emotion in Elderly Based on Pulse Rate Variability Features: A preliminary study of e-Health monitoring system

e-Health monitoring systems have been developed widely in many forms of applications. Monitoring physical condition of patients from a distance is the basic definition of e-health. However, there have been only few studies that explored the implementation of e-health in monitoring human emotions. It's been widely known that emotion is one of the fundamental aspects that affecting human health conditions, especially the negative emotions such as sad and angry. In a health monitoring system, the influence of emotion to the human health can be monitored by using physiological signals such as heartbeat data, Oxygen in blood level, Galvanic skin level, pulse rate or blood pressure. Due to that reason, this study aimed to analyze physiological patterns of emotion in elderly based on pulse rate variability features by using pulse sensor as a form of e-health monitoring system in a lab scale. In this study, 25 elderly participants were involved and were stimulated by a video that conveys happiness, sadness, and anger content. The experiment was done in three phases: the baseline phase, video stimulation phase, and recovery phase. Pulse sensor was applied to capture the physiological signal of 25 participants during those 3 phases. Statistical feature extraction was used to calculated mean, maximum, minimum and standard deviation values from pulse rate signals. Furthermore, the paired t-test was used to analyze mean value between baseline and video stimulation phase. The results showed that sad emotion increased pulse rate significantly compared to happy and angry emotions. It was reported that 9 participants reached the pulse rate level more than 100 bpm when experiencing sadness. It implies that sad emotion has a higher potential for Sinus Tachycardia to happen to elderly people.
Active Switching with SiC MOSFETs

The work in this paper presents a methodology to overcome the coupled nature of dv/dt and di/dt to gate drive resistance and stray commutation inductance for a SiC MOSFET. The SiC MOSFET exhibits almost ideal switching behavior, corresponding closely to the simplified device equations. Designing a circuit however to a give specific di/dt and dv/dt, particularly under varying load conditions, becomes difficult and tends to proceed by trial and error. By adding feedback control and using a gate current drive rather than a simple resistor, these relationships can be decoupled and designs maybe improved. This paper presents both Active Voltage Control and Active Current Control using a gate current drive and feedback circuits, and presents new experimental results for a state of the art 900V, 35A SiC MOSFET. On board measurements are used throughout. It is demonstrated that near ideal switching behavior can be achieved, which may be modified at will within the bounds of the gate drive capability and stability constraints.
Audio-Visual Speech Recognition System Using Recurrent Neural Network

An audio-visual speech recognition system (AVSR) integrates audio and visual information to perform speech recognition task. The AVSR has various applications in practice especially in natural language processing systems such as speech-to-text conversion, automatic translation and sentiment analysis. Decades ago, researchers tend to use Hidden Markov Model (HMM) to construct speech recognition system due to its good achievements in success recognition rate. However, HMM’s training dataset is enormous in order to have sufficient linguistic coverage. Besides, its recognition rate under noisy environments is not satisfying. To overcome this deficiency, a Recurrent Neural Network (RNN) based AVSR is proposed. The proposed AVSR model consists of three components: 1) audio features extraction mechanism, 2) visual features extraction mechanism and 3) audio and visual features integration mechanism. The features integration mechanism combines the output features from both audio and visual extraction mechanisms to generate final classification results. In this research, the audio features mechanism is modelled by Mel-frequency Cepstrum Coefficient (MFCC) and further processed by RNN system, whereas the visual features mechanism is modelled by Haar-Cascade Detection with OpenCV and again, it is further processed by RNN system. Then, both of these extracted features were integrated by multimodal RNN-based features-integration mechanism. The performance in terms of the speech recognition rate and the robustness of the proposed AVSR system were evaluated using speech under clean environment and Signal Noise Ratio (SNR) levels ranging from −20 dB to 20 dB with 5 dB interval. On average, final speech recognition rate is 89% across different levels of SNR.
Non-Intrusive Appliance Identification with Appliance-Specific Networks

Non-Intrusive Load Monitoring (NILM) is a technique for load identification and energy disaggregation. The problem is usually formulated as a single-channel blind source separation. NILM algorithms aim to identify the operating characteristics of individual appliances from aggregate power measurement. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power, but these methods often suffer from overfitting and don't generalize well. In this paper, we propose a novel NILM method that leverages advances in both supervised and unsupervised learning techniques. The proposed method consists of three stages: a) a Bayesian non-parametric learning-based approach is used to extract appliance states; b) synthetic minority oversampling technique (SMOTE) is employed to mitigate the heavy imbalance in switching events present in the NILM problem; and c) lightweight long short-term memory (LSTM) networks are employed for status classification for each appliance. We argue that making the differences before and after the switching event as the input to the networks can reduce complexity of network training and makes the proposed method robust to multi-appliance scenarios. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving better performance when compared to recent methods. Furthermore, an ablation study is conducted to demonstrate the effectiveness of each module of our method.
Three-dimensional Visible Light Positioning: an Experimental Assessment of the Importance of the LEDs’ Locations

This paper assesses the accuracy of a three-dimensional Visible Light Positioning (VLP) algorithm for two different Light Emitting Diode (LED) configurations using the same four LEDs, but mounted at different locations on the ceiling. The two configurations are both simulated and measured at 22801 test points. It is observed that a classic square LED configuration results in position ambiguities, causing errors up to several meters. Alternatively, a star-shaped LED configuration is able to uniquely reconstruct the photodiode’s location. For LEDs at a height of approximately 3 m above the receiver, median errors of 12.7 cm and maximal errors of 21.1 cm are experimentally obtained, showcasing the applicability of 3D VLP for drone navigation.
High-Performance Schottky-Barrier Field-Effect Transistors Based on Monolayer SiC Contacting Different Metals

Schottky-barrier field-effect transistors (SBFETs) based on monolayer SiC contacting four metals (Ag, Au, Al, and Pd) have been proposed using density functional theory and ab initio simulations. The n-type Schottky contacts could be formed between monolayer SiC and Ag, Au electrodes with electron Schottky barrier heights (SBHs) of 0.7 and 1.1 eV, respectively, whereas the p-type Schottky contacts were formed between monolayer SiC and Al, Pd electrodes with hole SBHs of 1.0 and 0.1 eV, respectively. When the gate length was equal to 5.1 nm, SiC-SBFETs with four metals could all overcome the short-channel effect due to the wide bandgap of SiC. In addition, the SiC-SBFET with Pd electrode provided the best performance and could satisfy the performance requirement of high-performance transistor with gate length of 5.1 nm outlined by the International Technology Roadmap for Semiconductors (ITRS). Our study gives an insight into the monolayer SiC–metal interfaces and, thus could be developed into a viable 5-nm “More than Moore” device in the future.
Attention-based Gated Recurrent Unit for Links Traffic Speed Forecasting

With urge of demands on efficient transport planning policies along with surge of travel flow volumes due to fast urbanization, traffic speed forecasting becomes a canonical and thriving research domain. Furthermore, the vehicles speed plays a critical role in the level of congestion. Traffic speed estimation then helps transport authorities as well as network users to handle congestion over road infrastructures or at least provides a global picture of daily passenger flow. In this work, we propose the first methodology to forecast the future traffic speed over the road segments (i.e. links) exclusively based on traffic flow data using floating car data. For this study, we pre-process over one million vehicles flow for several network links spread all over the Greater Paris. A attention-based recurrent neural network is used to capture the correlation between the temporal sequences of traffic flow and that of speed. The attention layer learns patterns from weights of near-term traffic flow, thus extracts the inherent interdependency of traffic speed to many factors (e.g. incidents, rush hour, land use, etc.) in non-free-flow conditions. The results demonstrate the efficiency of the proposed model in traffic speed forecasting excluding additional data such as historic traffic speed and network graph contrary to cutting-edge work in the field. This is a substantial property since it allows avoiding the cumbersomeness in data mixing and facilitating resource availability.
Event-triggered-based Adaptive Output Feedback Control with Prescribed Performance for Strict-feedback Nonlinear Systems

This paper mainly investigates the problem of event-triggered output feedback control for a class of strict feedback systems that satisfies a prescribed performance. With the help of introducing the prescribed performance functions, the constraint conditions on the tracking error can be obtained, which can converge to an arbitrarily small residual set, meanwhile the maximum overshoot is less than a pre-specified value. For the case of unmeasurable state, a state observer is used as approximation model to estimate them. Moreover, in order to reduce the computation burden, the event-triggering mechanism is successfully combined with backstepping. To guarantee the prescribed performance for tracking error and the boundedness property for all signals in the closed loop, an effective event-triggered adaptive output feedback control method is developed based on the relative threshold strategy, which needs to update only one parameter online. In simulation, the comparative results are provided to illustrate the validity of the control scheme.