Miracle-dz/UniFAR-FTUs huggingface dataset:
Gyro Torque Suppression for AMB-Rotor of Magnetically Suspended Control Moment Gyro with Disturbance Observer

The high-speed rotor will be disturbed by the gyro torque, when the magnetically suspended control moment gyro (MSCMG) outputs control torque. In order to prevent the influence of gyro torque on the stability of magnetic levitation rotor system of MSCMG, this paper proposes a disturbance suppression method based on disturbance observer (DOB). The model of magnetic levitation rotor system based on decentralized control is established and the principle of the DOB is analyzed. Then, a suitable low-pass filter is designed to ensure that the DOB is robust enough to model uncertainty. Finally, simulation results prove that the peak displacement of the high-speed rotor is reduced by 46.3 % with the DOB-based controller when the gimbal suddenly moves at the maximum speed,
Rational Engineering of the Dielectric Properties of Thin Silica Layers with a Single Plane of AgNPs

Interface properties of dielectrics are of outmost importance when dealing with phenomena as charge injection or secondary electron emission. When the dielectric thickness scales down to the nm range, a deviation from the bulk material properties occurs; therefore, a possibility to tune the surface properties comes by modifying dielectrics at its sub-surface. This paper focuses on the precision of plasma-based processes in order to elaborate thin silica dielectric layers with a single plane of silver nanoparticles (AgNPs) at its sub-surface and thus to control their behavior under electrical stress. An illustration of the modulated properties of such nanostructured dielectrics is given through current measurements and electron emission.
Factors contributing to the development of aluminum resistance in the Madeiran maize germplasm

Forty genotypes representing maize genetic diversity from the Island of Madeira were screened for resistance to aluminum (Al) in nutrient solution. Seeds of maize were obtained from local farmers from field plots spread around the island in a range of altitudes from 99 to 1000 m above sea level. The content of ionic aluminum and the pH of soils cultivated with maize were determined. The mean pH value of all examined soils was 4.9, while the mean ionic Al content was 0.76 cmol kg–1. Seventy-two hours (h) exposure to 100 and 200 μM Al followed by a 48 h recovery period of 4-day old seedlings revealed significant differences in Al resistance in the Madeiran maize germplasm. Root survival and regrowth exceeding 80 % of the seedlings were observed in 22 and 8 of the tested genotypes screened at 100 and 200 μM Al in nutrient solution. High Al resistance among Madeiran maize genotypes appeared not to be associated with the lower pH of soil, and did not correlate with the amount of soil Al3+ or the altitude at the site of collection. The high level of Al resistance of the maize cultivars indicates an initial genetic trait enhanced by open pollination of maize. Zur Entstehung von Aluminiumresistenz von Maissorten aus Madeira beitragende Faktoren Vierzig reprasentative Maisherkunfte von der Insel Madeira wurden hinsichtlich ihrer Resistenz gegenuber Aluminium (Al) in Nahrlosung untersucht. Das verwendete Maissaatgut wurde von einheimischen Landwirten zur Verfugung gestellt. Die Felder reprasentieren verschiedene Standorte der Insel, die sich hinsichtlich Hohenlage (99–1000 Meter uber dem Meeresspiegel), pH-Wert und Al3+-Gehalt der Boden unterschieden. Der mittlere pH-Wert aller untersuchten Standorte war 4,9. Der mittlere Al-Ionen Gehalt der untersuchten Boden betrug 0,76 cmol kg–1. Die “eriochrome cyanine“-Technik wurde angewandt, um die Auswirkung der Al-Ionen auf das Langenwachstum der Wurzeln zu bestimmen. Vier Tage alte Keimlinge wurden in Nahrlosung fur 72 Stunden (h) 100 bzw. 200 μM Al ausgesetzt, gefolgt von einer 48-stundigen Regenerationsphase. Die Herkunfte wiesen grose Unterschiede in der Al-Resistenz auf. Bei 22 bzw. 8 von 40 Herkunften uberlebten mehr als 80 % der Keimlinge den Al-Stress und zeigten erneutes Wurzelwachstum nach Behandlung mit 100 bzw. 200 μM Al. Die Al-Resistenz der Maisherkunfte stand nicht mit dem pH-Wert des Bodens in Verbindung und war auch nicht mit der Menge von Al3+ oder der Hohenlage korreliert. Die Al-Resistenz der Maisherkunfte beruht wahrscheinlich auf einer ursprunglichen genetischen Eigenschaft und wurde vermutlich durch die offene Pollenubertragung verbreitet.
A Fuzzy Based Decision Support System for Irrigation Process in Precision Agriculture

Productivity level has been no doubt increased in every field, let it be agriculture. New technologies have replaced traditional work culture. But still a lot of work is left in order to perform precision agriculture, which means more production using less products or manpower. Many researchers have proposed many decision support systems to achieve this target. These automatic agricultural decision-making systems improve the decision making of farmers and also will manage and maintain the agricultural field in a better way. In this paper, we have proposed a Decision support system for the irrigation process in order to make precision agriculture. The system takes values from sensors and deals with parameters like temperature, humidity, PH value etc. We have used a fuzzy controller and implementation is performed on Matlab.
Adaptive Neural Backstepping Control for a Class of Strict Feedback Nonlinear Full-State Constrained System with Sensor and Actuator Faults

The aim of the current article is dealing with the adaptive neural fault tolerant control subject for a class of strict feed-back nonlinear full state constrained systems with faults in actuators and sensors. The faults which are taken into account in the current study are bias, drift, loos of accuracy, and misfortune of impression faults. In order to reduce the computational effort, only one parameter law is updated at each step. Besides, it is guaranteed that the states stay inside their constraint sets based on Barrier Lyapaunov Functions (BLF). In order to reach stability and tracking performance of the system, the controller parameter adaptive law was designed according to Lyapunov stability theory. It was found that, the Lyapunov theory demonstrates that the devised method can guarantee the closed loop stability of the control system, and all signals within the closed-loop framework are semi-globally uniformly bounded and the boundary of states are never damaged and the following blunder can converge to small desired value by the proper choose of design parameters. The simulation study have shown that the proposed control strategy was proven to be effective.
CWAI-CNER: Chinese entity recognition based on adaptive incorporation of characters and words

Chinese Named Entity Recognition (CNER) is an important sub topic in the field of Chinese Natural Language Processing, which plays an important role in multi tasks. However, it’s difficult to determine the boundaries of entities in Chinese texts because the Chinese words are not naturally separated, which further causes the task of CNER much more difficult. In addition, the mainstream Named Entity Recognition (NER) is based on sequence tagging, which causes the cost of training set labeling very high, so many NER tasks are limited by training sets’ deficiency. In this work, we propose a new CNER method based on adaptive incorporation of characters and words–CWAI to solve the problem of words information loss caused by lacking of words boundaries, which uses convolution neural network (CNN) to capture the local semantics for every character, and then adaptively calculates the weights of potential words that match a lexicon for each character based on attention mechanism between characters and words. And for the problem of limited model effects due to insufficient training set, we combined our model with pre-trained models to solve that.
Adaptive Noise Suppression for Wake-Word Detection by Temporal-Difference Generalized Eigenvalue Beamformer

This paper proposes an adaptive noise suppression method for wake-word detection by a temporal-difference generalized eigenvalue (TDGEV) beam-former. To emphasize wake-word utterances, which are leading phrases with short duration, the proposed method is based on a generalized eigenvalue beamformer regarding current and past spatial covariance matrices for speech and noise, respectively. It can emphasize wake-words with small distortion and suppress any noises regardless of directions of arrival (DoAs) and noise sources. We perform experiments of wake-word detection with and without beamformers using test data including wake-word utterances from various DoAs. The results show that the proposed TDGEV method reduces false rejects with 32.9% relative error rate reduction.
Motor Imagery EEG Signal Classification Algorithm Based on Riemannian Space

Brain computer interface (BCI) can directly control external devices by providing additional signal pathways, which has a broad application prospect in medical rehabilitation and other aspects. It is a long-term research hotspot in this field to find an efficient classification algorithm and reduce the training cost. Most researchers are currently improving the traditional algorithm in order to achieve better results, but the improvement is limited. Therefore in this paper, we proposed a novel EEG signal classification algorithm based on Riemannian space, not on traditional Euclidean space. In this proposed algorithm, sample covariance matrix is used as the feature of EEG signal, and Riemannian mean and distance are used to classify the matrix, which realizes the application of manifold learning in BCI. Experimental results show that this method can effectively distinguish EEG signals, and the accuracy is better than that of traditional methods.
Highlighting Nerves in Images for Ultrasound Guided Regional Anesthesia

Surgery is a treatment given to the patient, when patient is having injuries in the body parts. While doing the surgery the patient will undergo pain and discomfort, to reduce the pain of patient anesthesia is given before surgery. There are many types of anesthesia procedure called local, regional and general. Local anesthesia is given to specific part of the body; general anesthesia is where the patient is completely senseless. Regional anesthesia is procedure, where anesthetist does injection near a class of nerves to numb the area of human body that requires surgery. There is difference in structure of every person. This makes doctor hard to detect nerve in internal organ. Ultrasound image provide information about structure of body which can be used for regional anesthesia. Ultrasonography is the safest method compared to the other imaging techniques like Xray, CT, and MRI; because it is not based on ionizing radiation. In this paper, we simplify the issue of detection of nerve in ultrasound image. For input, image such as ultrasound images of brachial plexus are used. Initially in preprocessing, filtering technique is used to remove noise from ultrasound image and then segmentation is carried out using active contour model. Performance of segmentation is evaluated using dice coefficient and Jaccard coefficient. The nerve location in ultrasound image is detected to help the doctors in giving regional anesthesia.
A Preview of Augmented Reality as Pioneering Method in Education

In Today's fast paced world, constant changes occur in everything around us especially technologies and this triggers the education sector to embrace the advancements. One such major distinctive approach is 3D Augmented Reality (AR). AR strengthens & supplements what can be seen, heard, smelled and sensed and allows the users to interact with the real & virtual worlds simultaneously. This paper reviews on how AR has been used in various genres in the field of education. Also, the benefits, the barriers faced and tests results over how effective is AR in acquiring knowledge is discussed. Amongst the varied published papers, the documents picked in this survey are from prominent journals, special issue articles and conferences that have implied ‘Marker based augmented reality’. The paper will give an overview about the prevalent methodology used in developing the AR based applications. Adding to the aforesaid data, the paper also gives information on, how in pandemic situation, AR has brought a new dimension to online classes and has become the head-turner.
Exemplar-based Learning for Recognition & Annotation of Human Actions

Human action recognition is an active research topic in computer vision. It is a challenging task to model various actions, varying with time resolution, visual appearance and others. For each action category, a large collection of similar actions is learned. This requires training a neural network with a large number of videos. Each action is described as a set of similarities between its instances and candidate exemplars. Then the most discriminative video is chosen. The experiment results on a publicly available dataset known as the KTH dataset. The project is expected to separate the human from the background in the video and identify what action is performed by him/her.
Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Decentralized cooperative localization (DCL) is a promising method to determine accurate multirobot poses (i.e., positions and orientations) for robot teams operating in an environment without absolute navigation information. Existing DCL methods often use fixed measurement noise covariance matrices for multirobot pose estimation; however, their performance degrades when the measurement noise covariance matrices are time-varying. To address this problem, in this article, a novel adaptive recursive DCL method is proposed for multi-robot systems with time-varying measurement accuracy. Each robot estimates its pose and measurement noise covariance matrices simultaneously in a decentralized manner based on the constructed hierarchical Gaussian models using the variational Bayesian approach. Simulation and experimental results show that the proposed method has improved cooperative localization accuracy and estimation consistency but slightly heavier computational load than the existing recursive DCL method.
Two-stage Asynchronous Iterative Solvers for multi-GPU Clusters

Given the trend of supercomputers accumulating much of their compute power in GPU accelerators composed of thousands of cores and operating in streaming mode, global synchronization points become a bottleneck, severely confining the performance of applications. In consequence, asynchronous methods breaking up the bulk-synchronous programming model are becoming increasingly attractive. In this paper, we study a GPU-focused asynchronous version of the Restricted Additive Schwarz (RAS) method that employs preconditioned Krylov subspace methods as subdomain solvers. We analyze the method for various parameters such as local solver tolerance and iteration counts. Leveraging the multi-GPU architecture on Summit, we show that these two-stage methods are more memory and time efficient than asynchronous RAS using direct solvers. We also demonstrate the superiority over synchronous counterparts, and present results using one-sided CUDA-aware MPI on up to 36 NVIDIA V100 GPUs.
Updated experiences of DGA with natural esters and current state of international guides

Historically, transformers used mineral oil as dielectric fluid. With the new environmental trends, the use of natural esters as a dielectric fluid in transformers is increasing.Its biodegradable characteristics make it a possible substitute for mineral oil. Nevertheless, its application in power transformers is limited due to the lack of history in service. Traditional diagnostic techniques for mineral oils, such as dissolved gas analysis (DGA), must be readjusted for its application in natural esters. This work aims to present an updated review of experiences of DGA with natural esters, highlighting the main differences regarding mineral oils. In addition to the experiences reported in the specialized literature, these differences are treated by comparing and analyzing some of the current guides for the interpretation of gases generated in mineral oil and ester liquids used in transformers, such as IEEE C57.104 and IEEE C57.155 respectively.
Audio Future Block Prediction with Conditional Generative Adversarial Network

Signal processing is a vast subfield of electrical and computer science where audio signal processing has secured a remarkable position to restore corrupted or missing audio blocks. However, generating possible future audio block from the previous audio block is still a new idea that can help to reduce both audio noise and partially missing an audio segment. In this paper, a generative adversarial network (GAN) along with a pipeline is proposed for the prediction of possible audio after an input audio sequence. The proposed model uses short-time Fourier transformation of audio to make it an image. The image is then fed to a conditional GAN to predict the output image. After that, Inverse short-time Fourier transform is then applied to that predicted image, generating the predicted audio sequence. For a small audio sequence prediction, the proposed methodology is quite fast, robust and has achieved a loss of 0.43. So it is may work well if deployed on a voice call and broadcasting applications.
A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory- recurrent neural networks (RNN- LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well- diagnosis performance effectively.
Hedgehog defects in mixtures of a nematic liquid crystal and a non-nematogenic component

A theoretical study of the core structure and composition of a hedgehog defect in a mixture of a nematic liquid crystal and a second non-nematogenic component is presented. Fractionation of the components between the bulk and the defect core is rigorously considered by using solution thermodynamics. Two complementary approaches are used to analyze this problem: a multiscale model based on a Landau–de Gennes free energy functional that is solved numerically, and a macroscopic sharp interface phase-equilibrium model, which considers the defect core as an isotropic phase in equilibrium with a distorted nematic phase and that under certain limiting conditions yields equations that reveal the mechanisms that select defect core structure, geometry, and composition. It is found that the non-nematogenic component segregates preferentially to the defect core, and the defect radius increases as the concentration of the second component and the temperature are increased. As previously predicted for pure liquid crystals, close to saturation conditions the radius increases significantly, and a small range of supersaturation or superheating is observed.
Forearm Orientation and Contraction Force Independent Method for EMG-Based Myoelectric Prosthetic Hand

An accurate and robust pattern recognition system is crucial for advanced electromyography-based upper limb prosthetics. Variations in the user’s forearm orientation and contraction force varies the electromyography features for the same movement. It degrades the performance of electromyography-based movement classification. Few attempts reported to increase the classification accuracy under such dynamic factors include utilizing multi-stage classifiers, multi-modal sensory data, high-dimensional data, or set of power spectral descriptors. This work aims to overcome limitations in the existing methods through a novel feature set. The combined effect of forearm orientations and contraction force levels on the classification of six hand movements are considered in this study. In this paper, a novel feature set extracted from electromyography’s wavelet bispectrum, obtained as the combination of continuous wavelet transform and bispectrum, is proposed. The proposed method remains independent of the multiple dynamic factors due to its invariant and uniqueness properties. Linear discriminant analysis classifier with the proposed wavelet bispectrum feature set showed superiority (classification accuracy=86.43±3.67%) over conventional time-domain feature set (classification accuracy=75.92±5.41%) when trained with data from single orientation and force level. When trained with data from appropriate variations (three orientations and medium force), the accuracy with the proposed method increased to 90.35±2.01%. Further, by training with data from all three orientations and force levels, this method achieved an accuracy of 96.11% ±1.34%. The statistical significance of the higher classification accuracy achieved with the proposed feature set compared to other conventional feature sets is proven (repeated measure one-way ANOVA, p < 0.05).
A Bidirectional Onboard Charging System for E-Rickshaw Using Interleaved SEPIC Converter

This work proposes a bidirectional power flow topology for an onboard charger (OBC) of an e-rickshaw. The grid connected OBC is designed, modeled and its performances is studied in detail. This topology has two stages interconnected with the DC link capacitor. Its first stage is a front-end converter. It regulates the voltage at the DC link capacitor and regulates the power quality within the IEC 61000-3-2 standard. Its second stage is DC-DC converter. It regulates the output charging current. Moreover, the bidirectional power flow operation is demonstrated in detail. The vehicle to grid (V2G) operation is regulated by the second stage DC/DC converter. The bidirectional interleaved isolated SEPIC (ii-SEPIC) converter topology is used for OBC. A multistep constant current charging scheme is selected for the operation of the OBC. A 2kW bidirectional OBC is designed. PI controllers and the pulse width modulation switching are designed for both the stages.