Miracle-dz/UniFAR-FTUs huggingface dataset:
An Novel Face Image Super-Resolution Based on Sparse Representation and Wasserstein Distance

Face super-resolution has attracted much attention in recent years. Many algorithms have been proposed. However, these approaches only perform well under the condition that the input is noiseless or has small noise. These existing approaches think the measure of between LR and HR is Euclidean distance. Euclidean distance cannot measure well the condition that the image's pixel values tend to have random fluctuations of varying magnitudes or the image is represented as sparse high-dimensional data. In this paper, we propose a novel sparse representation and Wasserstein distance based face super-resolution approach that seem the measure of between LR and HR as Wasserstein distance. Specifically, we introduce the fused Lasso to the least squares representation of the input LR image in order to obtain a stable sparse representation, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FERET face dataset. Visual and quantitative comparisons show that the proposed face super-resolution method achieves comparable performance to the state-of-the-art methods under noiseless condition, and yields superior super-resolution results when the input LR face image is contaminated by strong noise.
A Survey of Machine Learning Applications for Power System Analytics

Recent advances in computing technologies and the availability of large amounts of heterogeneous data in power grids are opening the way for the application of state-of-art machine learning techniques. Compared to traditional computational approaches, machine learning algorithms could gain an advantage from their intrinsic generalization capability, by also providing accurate short-term power flow forecasts from distributed measurement units, with greater computational efficiency and scalability. Several studies in the literature investigated the use of suitable machine learning models to address different issues in the field of power grid operation and management. Furthermore, the ongoing transition towards smart grids is generating new research opportunities for the real-time application of machine learning algorithms in power systems. In this paper, a literature survey on the application of different machine learning techniques in power systems is presented and critically reviewed, by evaluating the main advantages and criticalities of each technique for the selected applications.
Capacity in LoRaWAN Networks: Challenges and Opportunities

Internet of Things (IoT) concept is growing in last few years and number of IoT devices is increasing rapidly. Individual IoT sensors communicate over network. The LPWAN (Low Power Wide Area Network) networks possess the ability to offer low-cost connection for huge number of low-power devices distributed over large areas. LoRaWAN is a prominent LPWAN solution and in this paper existing research work related to LoRaWAN capacity is surveyed, presented and discussed.
Extensive analysis of band alignment engineering on the open circuit voltage performance of a GaAs/GaSb hetero structure solar cell

Maximum use of solar spectrum is possible through the selection of suitable band gap material in a solar cell. Single junction hetero structure solar cell provides a better opportunity by opting different materials with different band gap in the preparation of such device. But the problem arises with the large lattice mismatch and band discontinuity among these materials, which drastically reduces the open circuit voltage $(\mathrm{V}_{\mathrm{oc}})$ as well as the fill factor of the cell. In this work, band alignment engineering has been introduced for such type of problems. The device is simulated and verified using Silvaco TCAD suite. An extensive study is carried out in terms of SRH, radiative recombination and its effect on $\mathrm{V}_{\mathrm{oc}}$ of the cell for different band offset values. It is found that the reduction in band discontinuity improves the $\mathrm{V}_{\mathrm{oc}}$ of the device.
Proposal Generation by Learning Visual Relationship

Proposal generation aims to generate bounding box candidates that contain objects. The key for proposal generation is to produce very few bounding boxes that can achieve high recall. In this paper, we propose a novel proposal generation algorithm which introduces visual relationship into proposal generator. The intuition is that objects always exist in co-occurrence, which can be clues for generating proposals coherently. Specifically, it is achieved by concatenating a novel relationship network with region proposal network, and jointly training them end-to-end. Experimental results on Visual Relationship demonstrate that our method can achieve better performance compared with state-of-the-art, especially when evaluating at the top limited number of generated proposals.
Teaching development for SMART devices with focus on future key skills

This paper presents teaching use-cases that are currently required by the majority of employers that focus on design and development of software solutions, which make use of SMART devices in any form. The use-cases were selected from topics and ideas that cooperating companies considered promising for their business in the future and were later used as diploma or bachelor’s theses’ topics. Later, we extracted basic knowledge and skills requirements in specific fields like soft skills, project skills, software development skills, quality assurance skills, business skills, data management skills and designed two new subjects (classes) that covered the knowledge. Our evaluation over two years, during which the classes were taught, shows better capabilities of students to adapt to company standards and require less additional education from the employer’s side. We also present the progress and results of a student project that attended both new classes.
Improve the Security of Social Media Accounts

The task of improving of security level of accounts in such social media as Facebook, YouTube and Instagram is considered in the paper. A mathematical model of formation of social media security level indicator is built. According to the obtained mathematical model, a chatbot is developed. The developed chatbot allows to estimate the security level of a social media account. If necessary, it formulates recommendations concerning improvements on social media account security level.
Distant Supervision for Relation Extraction via Retrieval-based Neural Networks

Relation Extraction is an important task in natural language processing (NLP) and Knowledge Base (KB) constructing. Distant Supervision (DS) is a widely used technique for automatically labeling training data by aligning the triples in a KB with free text. However, most methods treat DS relational extraction as a classification problem, which are vulnerable to dataset distribution. In this paper, we propose a retrieval-based method based on piecewise convolutional neural networks (called RPCNN) for DS relation extraction, which calculate relation retrieval scores between input features and relation features before classification. We apply knowledge embedding learning model to learn relation feature vectors from the KB used in DS, which means that our model could leverage the semantic knowledge in the KB to improve the relation extraction. Experimental results on widely used datasets show that our RPCNN model outperform all the baseline systems.
Wind Turbine Fault Diagnosis with Generative-Temporal Convolutional Neural Network

Condition monitoring of wind turbines (WTs) has attracted a great deal of attention due to fast development of WTs. The inherent intermittence of wind energy and locating WTs in remote areas, makes designing proper fault diagnosing method difficult. To address this issue, we have proposed a two-block deep learning based method in this paper, which encapsulates two feature extraction and classification stages in an end-to-end architecture. In the designed method, we have utilized generative adversarial network (GAN) as feature extraction block and temporal convolutional neural network (TCNN) as fault classifier block. The proposed structure benefits from the leverage GAN and TCNN. The simulation results based on real-data from a 3 MGW WT in Ireland, which is obtained from supervisory control and data acquisition system (SCADA) demonstrates that it is a suitable alternative for WTs’ fault classification. To show the superiority of the proposed method, the results are compared with support vector machine (SVM) and feed-forward neural network (FFNN).
Word-Level and Character-Level Mixed Features for Chinese Short Text Classification

In this paper, a novel method is proposed to solve the problem of insufficient representation of single character-level features or word-level features. In view of the short length, sparseness and strong context dependencies of short text, our method takes word-level vectors and character-level vectors as inputs simultaneously, and encodes sentence semantics by two Long Short-Term Memory (LSTMs) or bidirectional Long Short-Term Memory (BiLSTMs). The outputs of the entire sentence combined two outputs from word-level vectors and character-level vectors. For Chinese short text classification, our experiments show that the combination of word embedding and character embedding can complement each other in the sentence semantic representation, which helps to improve the classification performance of Chinese short text.
Modeling of the Inrush Current in Superconducting Transformers Using PSpice

The paper discusses numerical models of single-phase conventional and superconducting transformers. The addition of mathematical relationships made it possible to determine the average and effective current in subsequent periods, and to calculate the amount of heat that is released in the windings when switching the unloaded transformer to the full voltage of the supply network. The waveforms and pulses of the inrush current of 21 MVA transformers with copper and superconducting windings were calculated and compared.
Facial Expression Recognition Based on Spatial Transformer Siamese Networks

As the development of the computer vision, we could employ the facial expressions recognition technique in the human-computer interaction domain, such as chatting robot, psychological counselling robot and so on. In this paper, we propose a novel method to jointly learn the facial expression features based the combination of Siamese networks and spatial transformer networks. We posit that learning the features from both networks may make them more robust to the image variation especially illumination and viewpoint variation. Particularly, we construct the structure of the Spatial Transformer Siamese Networks(STSN) and learn the features robust to the facial expression image variation. The experimental results show that the proposed method outperforms the-state-of-art method on the MDLTI-PIE dataset.
Dual-Pole LSPM Motor with Dahlander Winding for High Inertia Loads

To improve the starting, the synchronization capability and the steady state performance of dual-pole synchronous motors, conventional stator winding arrangement with two separate sets of windings is being replaced with a single set of Dahlander type winding. This reduces the slot size, which ultimately contributes to a higher power density. Meanwhile, the use of single set of winding could effectively reduce the stator resistance, which limits the braking torque as well as the ohmic loss. For verification purposes, the performance characteristic of the dual-pole motor with these two winding structures are compared in terms of the starting and the synchronization capability. Finite Element (FE) simulation results confirm that the motor with Dahlander type winding exhibits a better synchronization capability, in particular, in case studies with higher load inertia.
Trucker Behavior Security Surveillance Based on Human Parsing

With the increase of roads and vehicles, the number of traffic accidents has gradually increased, and one of the important reasons is the driver’s unsafe driving behavior. Some existing methods rely on the method of joint point estimation to identify driving behavior. This kind of method can filter out unrelated components such as background, but the joint point estimation can only give the position information of human behavior, which cannot fully describe the human body, so we try to solve this problem. For one thing, we propose a driver behavior recognition method based on human parsing which can quickly and accurately recognize the driver’s unsafe behavior. For another thing, we build a trucker behavior dataset (TB dataset) with 700 videos and improve a human body segmentation model with higher precision. Finally, we achieve 93.39% and 91.32% accurate for recognizing driving with one-hand and no-hand. At the same time, we get 98.75% accuracy for recognizing driving with turned head by using ResNet50 model.
A High Speed Solar MPPT Controller Design

This paper describes a method for designing high speed solar MPPT controller. The basic MPPT algorithm adopts conventional P&O method. Instead, the voltage controller design method is presented to increase the MPPT tracking speed. The proposed method is based on the frequency domain design which is derived from the state space averaging model of the solar boost converter. The voltage controller can be designed with the requirements of zero steady state error, no overshoot and fast settling time. The theoretical maximum MPPT update speed can be identified using the performance of the voltage controller. The optimal control gains are selected using the MATLAB SISOTOOL library with interactive manners. The performances of the proposed methods are demonstrated using the PSIM simulation.
Medical Images Sequence Normalization and Augmentation: Improve Liver Tumor Segmentation from Small Dataset

Using deep learning for Medical Images Diagnosis automatically is a new trend in recent years. Concretely, the fundamental step in these studies is the automatic segmentation system for human organ, that has the benefit of accuracy in the diagnosis process for medical images (CT images). Traditionally, if there is no module of automatic segmentation system, this process has to be performed by the experience of specialized physicians. More importantly, this process takes much time of physicians, while the automatic segmentation system can alternative efficiently. However, the accuracy and responsiveness are the challenges for a segmentation system with a small organ or small tissue, i.e., a liver tumor, because of the lack of data for deep learning. This paper highlights some augmentation and preprocessing techniques from a small dataset of medical images. We propose a method to improve the module of automatic segmentation, in particular, liver tumor segmentation. For the augmented dataset from 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb), we achieve the dice score of 94.58% for liver segmentation and 45.87% for liver tumor. Furthermore, with a real dataset from a patient, the dice score of our system is over 78% for liver segmentation.
Fuel-Optimal Predictive Control of Satellite Formation Keeping Based on Genetic Algorithm

This paper addresses the keeping control problem of the leader-follower satellite formation by using an economic model predictive control method. The objective function in the designed economic model predictive control is comprised of two parts. One part is an adjustment term which is used to realize the trajectory tracking. The other part is an economic term le which achieves fuel optimization. In addition, genetic algorithm is used to solve the control law which realizes the keeping control of satellite formation. Simulation results show that the proposed control method can achieve fuel optimization control for satellite formation.
Influence of Gas Adsorption on the Impedance of Graphene Oxide

Graphene oxide (GO) and reduced graphene oxide (rGO) films were formed by ultrasonic spray coating method and were studied by micro-Raman spectroscopy (mRS), current-voltage characteristics and impedance spectroscopy. Raman spectra of GO and rGO films shows two typical peaks, with maxima at ~1350 cm-1 and ~1590 cm-1 (D and G bands). One semicircle on the Nyquist curve of gold / graphene oxide / gold structure suggests the existence of homogeneous regions. Adsorption of ethanol, acetone and isopropyl vapors changes electrical conductivity of graphene oxide films in different way. The sensing mechanisms to the organic vapors are discussed.
Process Mining for Decentralized Applications

Distributed Ledger Technologies and particularly Blockchain-based Decentralized Applications entail transaction data from various use cases and usage behaviors in a decentralized manner. In this context, we propose to apply Process Mining as a means to understand and quantify how a Distributed Ledger is de facto utilized. This paper introduces a heuristic Process Mining approach for extracting high level process structures from Distributed Ledgers and Decentralized Applications, which can also represent changes over time. The universal approach is demonstrated on the basis of the complete set of all transactions that happened on the Ethereum Blockchain during its first three years. Events, cases, and activities allow to compile an evolutionary time line, which shows the Blockchain evolving from its early stages to broader adoption. Furthermore, measures of activity significance, confidence, and process complexity are used to compile dependency graphs, which represent process transitions.
Statistical Estimation Applied to Electrocardiographic Imaging

Cardiovascular diseases are among the leading causes of death all over the world. Researchers have been extensively working on methods for early diagnosis and treatment of these diseases, with the goal of decreasing these death tolls. In Electrocardiographic Imaging (ECGI), the electrical activity of the heart is estimated from electrocardiographic measurements obtained from the body surface, using densely distributed electrodes, and a mathematical model of the body. This technique has advantages over classical electrocardiography (ECG) since it provides detailed information about the cardiac electrical activity without invasively recording measurements from the heart. From a mathematical point of view, this estimation problem is called the “inverse problem of ECG. ” However, this problem is ill-posed, and a priori information should be used to regularize the solutions. This paper presents an overview of several regularization methods with emphasis on statistical estimation methods such as Bayesian Estimation and Kalman Filtering. These techniques are advantageous over traditional methods due to their flexibility in incorporating the spatial and temporal information of the heart potentials for solving the inverse ECG problem, and yielding confidence intervals that help assess the accuracy of the solutions.
Stability Analysis and Control of a Grid Connected Wind Turbine Based on Doubly Fed Induction Generator

This paper introduces a new theoretical control strategy that aims to adjust and control the stability of the a grid-connected wind turbine based on Doubly Fed Induction Generator (DFIG). The control approach is realized in the direct rotor reference frame where the power factor is set to unity.The base of the proposed technique is the determination of the linear approximation of the nonlinear model followed by the computation of the transfer function of the DFIG. The Laplace domain is used to study the nature of the zeros of the DFIG.Hence one can separate the unstable zeros or the unwanted ones and design a suitable feedback that treat the instability phenomena or enhance the stability performance. The introduced approach was applied to a wind turbine generator driving a 3.7 kW load connected to grid . MATLAB program was used to simulate and test the performance of the proposed control methods. The results shows that the suggested control design proved to be reliable and effective.
A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition

Image processing technology in hardware and software has a significant development in all aspects of human civilization. Image processing is growing rapidly in various fields such as medical image, Biometrics, Object identification, Object character recognition (OCR), and many more. One biometric technique in an application detects and recognizes faces. Objectives of face recognition include recognizing emotions, identifying offenders, applying to security systems, image processing to access a door as an application of computer and human interaction. The facial recognition system in this research was conducted live streaming using Local Binary Pattern Histogram (LBPH) method. There is a constraint to a light intake received by the camera through a sensor that captures the image. Based on the results of our research to make better facial recognition, we propose using adaptation methods on the intensity of light that is around the image capture object. The method of adaptation in this research is the Contrast Adaptive Limited Histogram (CLAHE).
Noise Analysis of 1.0 THz GaN IMPATT Source

Noise analysis of 1.0 THz Wz-GaN IMPATT source has been carried out by the authors. A two-dimensional avalanche noise model for IMPATT diodes developed by the authors has been used in the present paper to study the avalanche noise characteristics of the said source. Results reveal that the mean-square noise voltage per unit bandwidth (i.e. noise spectral density) of the source lies in the order of 10–16 $\mathrm{V}^{2}\ \mathrm{s}$ and noise measure remains within the range of 7.3440 – 5.8755 dB due to the variation of bias current within the range of 78.54 – 98.17 mA for a fictitious value of zero series resistance. However, around 3 – 7% increase in noise measure has been obtained by considering earlier calculated series resistance values ranging from $1.5779 - 1.7879 \Omega$.
Research on the Determination of Privacy Concerns Degree of Micro-blog Users

By identifying the privacy concern degree of different users, we can provide users with targeted tips on telling the risk of privacy disclosure existed in Micro-blogs. For this purpose, the indexes of 4 aspects for distinguishing between high and low privacy concerns are selected, according to user attributes, potential tendency towards privacy concerns, privacy exposure of following friends and privacy disclosure events, and were processed quantitatively. Then, three machine learning methods of Adaboost-C4.5 decision tree, support vector machine and logical regression are used to establish the determination model and the corresponding algorithm. The compared experimental result showed that, the accuracy rate and recall rate of Adaboost-C4.5 decision tree to determinate privacy concerns is the highest in the three methods.
Investigating Trust Factors in Human-Robot Shared Control: Implicit Gender Bias Around Robot Voice

This paper explores the impact of warnings, audio feedback, and gender on human-robot trust in the context of autonomous driving and specifically shared robot control. We use pre-existing methods for the estimation and assessment of human-robot trust where trust was found to vary as a function of the quality of behavior of an autonomous driving controller. We extend these models and empirical methods to examine the impact of audio cues on trust, specifically studying the impacts of gender-specific audio cues on the elicitation of trust. Our study compares agents with and without human-voiced indicators of uncertainty and evaluates differences in trust with inferred and introspective methods. We find that a person's trust in a robot can be influenced by verbal feedback from the robot agent. Specifically, people tend to lend more trust to agents whose voice is of the same gender as their own.