Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable Stator Frequency Control of Stand-Alone DFIG with Diode Rectified Output

Variable frequency operation of stand-alone doubly fed induction generator (DFIG) is analyzed in this paper, in order to extend the operating speed range of the generator and optimize the power conversion efficiency. In a stand-alone configuration, the stator feeds a three-phase diode rectifier and a stand-alone electrical load. The dc output voltage of the generator is regulated by applying stator flux-oriented control scheme in synchronous reference frame. The losses in the power electronics components depends on the current flowing through the switches and as such also influences the power rating of the converter. In addition, the speed range of the DFIG is also determined by the converter rating. Thus, in the present paper to extend the speed range, for the same converter rating, the slip of the system is limited which can be achieved by setting the stator frequency free. Since the stator voltage amplitude is imposed by the dc-link voltage which is regulated to a constant reference value, a variation in stator flux magnitude results in a frequency variation. Therefore, the stator frequency can be scheduled in such a way that the output power is maximized. After the dc output voltage and inner d-q controller are designed, simulation tests have been run to find the optimal stator frequency for a fixed range of speed and consequently used as a look up table to improve the power conversion efficiency of the generator system for a limited rotor side converter rating (approximately 22%) in a 2.5:1 speed range at constant dc output voltage and power.
A gaze/SSVEP based Wheelchair Command

The goal of this manuscript is to present a new hybrid system based on the fusion of gaze data and Steady State Visual Evoked Potentials (SSVEP) to command a powered wheelchair. The motivation behind this work is the shortages raised from the individual use of gaze based and SSVEP based wheelchair command techniques. The proposed framework is based on two modules : a gaze module to select command and activate the flashing stimuli. An SSVEP module to validate the selected command. An experimental protocol was set up and the prototype was tested on five paraplegic subjects. The results showed that the run accuracy reached an average of 98%. Navigation performance was assessed based on navigation time, obstacles collisions and optimal path deviation.
Retrofitting Realities: Affordances and Limitations in Porting an Interactive Geospatial Visualization from Augmented to Virtual Reality

As Augmented Reality (AR) and Virtual Reality (VR) applications become more mainstream, developers now have a number of design decisions that must be carefully considered before choosing a device for an interactive visualization with big data. Unfortunately, understanding the true affordances and limitations of each device, and how these affect the resultant potential to support visual analytics, is still more of a black art than a science. In this paper, we highlight key design decisions and technical challenges in the context of a case study to port an interactive geospatial visualization from an AR device, the Microsoft Hololens, to a mobile VR device, the Google Daydream. Our results show that careful leveraging of backend cloud services can allow for interactive visualizations of big data to scale well across devices.
Bug Prediction Model using Code Smells

The term ‘Code Smells’ was first coined in the book Refactoring: Improving the design of existing code by M Fowler in 1999. Code smells are poor design choices which have the potential to cause an error or failure in a computer program. The objective of this study is to use code smells as a candidate metric to build a bug prediction model. In this study we have built a bug prediction model using both source code metrics and code smell based metrics proposed in the literature. We used Naive Bayes, Random Forest and Logistic Regression as our candidate algorithms to build the model. We have trained our model against multiple versions of 13 different Java based open source projects. The trained model was used to predict bugs in a particular version of a project, within a particular project and among different projects. We were able to demonstrate, that code smell based metrics can significantly improve the accuracy of a bug prediction model when integrated with source code metrics. Random Forest algorithm based model showed higher accuracy within a version, within a project and among projects when compared to other algorithms.
Computational Intelligence Strategies for Effective Collaborative Decisions.

Matrix Factorization models have proven a remarkable achievements in data and image processing. It however has a wholesome computational complexity. In view of that, a hybrid fusion strategy that maximizes feature engineering and improves generalization thereby solving cold start problem of collaborative recommendation is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate model for collaborative filtering (CF) modelling. And feed forward neural networks enhances feature engineering. In this paper, we propose a fusion-based recommendation system to address both cold start and scalability. We propose to use a multimodal approach which represent a multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a dimensional feature space where item-user resemblance and their preferred items is maximized. From our experiments, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem, for optimal recommendation decision making (Abstract)
Research on target recognition of underwater robot

In recent years, target recognition and detection technology, which is a very important research direction in the field of computer vision, is widely used in human life. The technology has been relatively mature for the recognition of targets such as people and objects on land. However, due to some conditions, it is relatively rare in the marine field. The main reason for the analysis is that underwater taxonomy and localization are affected by multiple factors such as illumination uniformity and obstruction, and underwater video acquisition is also relatively difficult. These issues have long been the focus of attention. Therefore, effective classification and recognition of targets in underwater video is of great significance for the intelligentization of marine equipment. This paper mainly locates and classifies the images of seacucumber, scallop and seaurchin. In this paper, the two methods of Faster-RCNN and YOLOv3 are used for experiments. The results show that the MAP and recall of YOLOv3 are higher than Faster-RCNN, and the recognition speed is relatively fast.
Quality Assurance of a Hybrid Online Course through Iterative Process Control

The online hybrid teaching mode had been used to improve learning and evaluation efficiency. The Kung-Fu style competency education with online-support laboratory system prompts students to own their learning as the pace and/or the path of learning. Student could move on to the next project only after they've mastered the core concept. To further guarantee the quality of teaching, it is necessary to provide efficient and accurate progress control for the process iteration. This paper elaborated how to build the quality assurance mechanism to 1) track students' progress, 2) store the behavior data, 3) support the quantitative data analysis, and 4) provide the teaching interaction services. The teaching assistant system could not only improve the work efficiency of the iterative process control course, but also improve the utilization of teaching resources. We used this quality assurance mechanism in the last course iteration, which effectively guarantee the quality of teaching.
Fuzzy Weighted Centroid Localization With Virtual Node Approximation in Wireless Sensor Networks

Due to their low cost, various range-free localization techniques are widely applied to estimate device locations in wireless sensor networks (WSNs), especially where other communication signals, such as those from a global positioning system, are absent. Among range-free techniques, the centroid algorithm has gained popularity because of its simplicity and low computational cost, rendering it suitable for power-sensitive sensor nodes or nodes. WSN topologies are unpredictable because sensor nodes are often deployed at arbitrary locations, making it difficult for sufficient numbers of known (anchor) nodes to cover all unknown nodes. Consequently, both centroid algorithm and its enhanced versions [e.g., weighted centroid (WC) algorithms] yield relatively high localization inaccuracy. In this paper, to successfully form anchor-node triangles, we propose a low-cost technique to determine the number of virtual anchor nodes or virtual nodes together with their positions. Specifically, we develop and provide proof for accurate approximate unknown node sides. We also improve localization accuracy by adding virtual nodes that collaborate with physical anchor nodes to provide the necessary coverage of the unknown nodes. We address estimations of unknown node locations by applying a fuzzy-based centroid localization method to prioritize anchor nodes by assigning different fine-tuned weighted factors. The results show that the proposed algorithm outperforms state-of-the-art fuzzy-based localization techniques for WC algorithms.
Demonstration of 1200V Scaled IGBTs Driven by 5V Gate Voltage with Superiorly Low Switching Loss

Functional trench-gated 1200V-10A class Si-IGBTs, designed based on a three dimensional (3D) scaling concept, were fabricated, and 5V gate voltage switching operation has been demonstrated for the first time. 33% reduction of turn-off loss and 100mV improvement of on-state voltage were achieved, while keeping 1.2kV forward blocking voltage.
Performance Optimization of a Fuzzy Entropy Based Feature Selection and Classification Framework

In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those methods include the combinations of three ideal vector calculations, three maximal similarity classifiers and three fuzzy entropy functions. Different feature removal orders based on the fuzzy entropy values were also compared. The proposed method was evaluated on three publicly available biomedical datasets, including Wisconsin Breast Cancer(WBC), Wisconsin Diagnostic Breast Cancer(WDBC) and Parkinsons. From the experiments, we concluded the optimized combination of the ideal vector, similarity classifier and fuzzy entropy function for feature selection. The optimized framework was also compared with other six classical filter-based feature selection methods. The proposed method was ranked as one of the top performers together with the Correlation and ReliefF methods. The proposed method achieved classification accuracies of 96.97%, 94.85% and 78.23% for the WBC, WDBC and Parkinsons datasets respectively. More importantly, the proposed method achieved the most stable performance for all three datasets when the features being gradually removed. This indicates a better feature ranking performance than the other compared methods.
Stepwise PathNet: Transfer Learning Algorithm to Improve Network Structure Versatility

Transfer learning can train a neural network for a target task's small dataset using a source task's pre-trained network; however, catastrophic forgetting, where the knowledge of the pre-trained network disappears during transfer learning, is problematic in this setting. PathNet was proposed to address this problem. However, PathNet can only be applied to modular neural network cases:thus, a non-modular pre-trained neural network is unavailable. Consequently, PathNet cannot be used to improve network structure versatility. Therefore, we propose Stepwise PathNet to improve versatility by considering the layers of a non-modular pre-trained neural network as modules. The performances of the proposed Stepwise and original PathNet methods were compared using the CIFAR-10 dataset (10 classes, and 60,000 images), and the results confirm the proposed method's potential to stabilize learning curves and accelerate learning to 45%.
A 2-D Exact Subdomain Technique in Switched Reluctance Machines Taking Into Account of Finite Soft-Magnetic Material Permeability

This paper presents a two-dimensional (2-D) exact subdomain technique in polar coordinates considering the iron relative permeability in 6/4 conventional switched reluctance machine (SRM) supplied by sinusoidal waveform of current. The general solutions of magnetic vector potential are obtained by applying the interfaces conditions (ICs) in both directions (i.e., r- and a edge ICs). The magnetic field is used to predict the iron core losses by using an analytical approach based on the Bertotti’s model and the flux variation locus (FVL). Finally, the electromagnetic performances results have been performed and compared with the 2-D finite-element (FE) method (FEM). The comparisons with FEM show good results of the proposed approach.
Granger Causality Analysis Based on Quantized Minimum Error Entropy Criterion

Linear regression model (LRM) based on mean square error (MSE) criterion is widely used in Granger causality analysis (GCA), which is the most commonly used method to detect the causality between a pair of time series. However, when signals are seriously contaminated by non-Gaussian noises, the LRM coefficients will be inaccurately identified. This may cause the GCA to detect a wrong causal relationship. Minimum error entropy (MEE) criterion can be used to replace the MSE criterion to deal with the non-Gaussian noises. But its calculation requires a double summation operation, which brings computational bottlenecks to GCA especially when sizes of the signals are large. To address the aforementioned problems, in this letter, we propose a new method called GCA based on the quantized MEE (QMEE) criterion (GCA-QMEE), in which the QMEE criterion is applied to identify the LRM coefficients and the quantized error entropy is used to calculate the causality indexes. Compared with the traditional GCA, the proposed GCA-QMEE not only makes the results more discriminative, but also more robust. Its computational complexity is also not high because of the quantization operation. Illustrative examples on synthetic and EEG datasets are provided to verify the desirable performance and the availability of the GCA-QMEE.
DBCC: Leveraging Link Perception for Distributed Beacon Congestion Control in VANETs

Under the IEEE 802.11p-based dedicated short range communication modules, vehicular safety applications rely on periodical broadcasts of safety beacons by each vehicle. However, the channel can be easily congested by high-frequency periodic beacons when the vehicle density becomes heavy. In this paper, through real-trace-based empirical study on vehicle-to-vehicle communication, we find that nonline-of-sight (NLoS) condition is the key factor on link performance degradation and blindly sending more packets in harsh NLoS conditions can hardly succeed but increase interferences to neighboring vehicles. Inspired by this, we propose a distributed beacon congestion control (DBCC) scheme to control beacon activities with considering link conditions, i.e., vehicles with more neighbors and better conditions of links with its neighbors, will be assigned with higher beacon rates. In DBCC, we first utilize two machine learning methods, i.e., naive Bayes and support vector machines, to train the features and output a classifier model which conducts online NLoS link condition prediction. With link status information, we then formulate a link-weighted safety benefit maximization (L-SBM) problem of the rate-adaptation under a TDMA broadcast MAC, which is proved to be NP-hard. A greedy heuristic algorithm for L-SBM is then proposed and the performance of the algorithm is evaluated. Extensive trace-driven simulations demonstrate the efficiency of DBCC design; particularly, the rate of beacon transmissions can be effectively controlled without exceeding the resource limit and the rate of transmission/reception collisions are greatly reduced.
Intermixing of motional currents in suspended CNT-FET based resonators

Here, we report the intermixing of piezoresistive and conduction modulation current in a carbon nanotube field effect transistor (CNT - FET) based resonator. We show that due to static displacement of the nanotube, as a result of electrostatic actuation, the motional current at the resonance frequency consist of both current components. For instance at a DC gate bias of 1.3 V, 3/4 of the motional current is conduction modulation current while the rest arises from piezoresistive effects. The intermixing effect due to asymmetry influences the fundamental harmonic response as well as the physical nature of the electrical signal being sensed; both of which are important for understanding frequency harmonics in nanoresonators and developing efficient readout schemes for nanoscale sensors.
The Fast Multipole Method for Sparse Solution of Linear Inverse Scattering Problems

The sparse solution for the linear inverse problems provide useful results for many fundamental engineering applications such as radar imaging. The studies in the literature has shown that the computational methods for the sparse solution tend to be slow as the imaging problem gets electromagnetically large, therefore the image reconstruction gets harder for the existing computational resources. The fast multipole method (FMM) can reduce the number of operations and the memory requirementfor the solution of the system. In this study, we apply the FMM to accelerate the sparse solution of the inverse scattering problem.
Power Management Scheme of a Photovoltaic System for Self-Powered Internet of Things

Energy efficiency is especially important for many Internet of Things (IoT) devices, such as prototyping boards and Bluetooth Low Energy (BLE) beacons. In this paper, we design and implement the power management of a photovoltaic system for a solar-powered BLE beacon, using discrete components. The main goal is to increase energy efficiency and extend the lifespan of the beacon that contains a battery and a solar cell as power sources. The introduced design can be further implemented in a printed circuit board with optimal components to increase energy efficiency and provide a reliable power supply system.
The Application of Deep Neural Network in Speech Enhancement Processing

To solve the problem that Non-stationary noise is difficult to remove during speech enhancement process when using Fourier transform, this essay will put forward a speech enhancement algorithm based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and Deep Neural Network (DNN). Firstly, preprocessing the original signal by EEMD, and decomposing a series of time-frequency information of the IMF component to meet the time-variation requirement better; Secondly, adjusting the weight of the IMF component by DNN and then synthesize it to enhanced the speech; Finally, comparing the differences of speech enhancement performance between using EEMD alone, using Fourier transform and EEMD as a preprocessing. The results show that the enhanced algorithm using EEMD as a preprocessing improves the scores of PESQ and STOI by 0.745 and 0.169 respectively, effectively improving the speech quality and intelligibility.
Parallel-Plane Breakdown Fields of 2.8-3.5 MV/cm in GaN-on-GaN p-n Junction Diodes with Double-Side-Depleted Shallow Bevel Termination

We report homoepitaxial GaN p-n junction diodes with novel beveled-mesa structures. The n-layers and p-layers, the doping concentrations of which are comparable, were prepared. We found that electric field crowding does not occur in the structure using TCAD simulation. The fabricated devices showed the breakdown voltages of 180–480 V, small leakage currents, and excellent avalanche capabilities. The breakdown voltages increased at elevated temperature. At the breakdown, nearly uniform luminescence in the entire p-n junctions was observed in all the devices. These results are strong evidences that the uniform avalanche breakdowns occurred in the devices. We carefully characterized the depletion layer width at the breakdown, and the parallel-plane breakdown electric fields of 2.8-3.5 MV/cm were obtained, which are among the best of the reported non-punch-through GaN vertical devices.
A Miniature 92–95 GHz On-Chip Slow-Wave Branch-Line Coupler

A miniature 92–95 GHz on-chip branch-line coupler (BLC) employing a slow-wave microstrip line is proposed herein. The ground metal plane beneath the signal line is periodically slotted, and the coupler can be highly miniaturized owing to the short wavelength characteristic of the transmission line with slow-wave structure. The size of the slow-wave BLC is $\mathbf{250 \ \mu m\times 250\ \mu m}$, which is 39% of the size of conventional BLCs.
Sensorless Control of Direct Drive PMSG Wind-Power Generator

For the purpose of sensorless low speed control of direct-drive permanent magnet synchronous generator (PMSG)based wind power generation system, this paper presents a technique based on the rotating high-frequency voltage-signal injection. To improves the efficiency of the method and eliminate the phase shift introduce by the traditional filters, bandpass, low pass or high pass filters are substituted by Kalman filters. The effectiveness of the proposed sensorless low speed control is shown by numerical simulations using Matlab/Simulink environment. The simulation results show that the control scheme provides an accurate estimate of the position of the PMSG-wind turbine and shaft speed.
SSVEP-Based BCI in Virtual Reality - Control of a Vacuum Cleaner Robot

Brain-Computer Interfaces (BCIs) allow communication and control of the environment without the use of peripheral muscles. One of the standard BCI paradigms is based on steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. In this article, a VR SSVEP-based steering simulation is presented and evaluated in comparison to a standard desktop version. Three control classes were used to control the application. The experimental task was to steer a virtual vacuum robot and collect 10 dust piles (for this, at least 31 command classifications were required). Participants were instructed to complete the task twice, using a head mounted display (HMD) and the laptop screen for visual stimulation. All participants were able to complete the task in both scenarios. Mean accuracies of 98.91% and 97.48% and mean ITRs of 23.96 and 20.71 bits/min were achieved for the HMD and desktop control, respectively. On average, the number of commands needed to complete the task in the online experiment was 32.00 and 32.75, for the HMD and desktop scenario, respectively.