Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Scale Vehicle Detection in High-Resolution Aerial Images With Context Information

Recently, unmanned aerial vehicles (UAV) are widely used in many fields due to the low cost and high flexibility. One of the most popular applications of UAV is vehicle detection in aerial images which plays an important role in traffic surveillance and urban planning. Although, many deep learning based detectors have achieved state-of-the-art (SOTA) performance in natural images, the significant variation in object scales caused by the altitude change of the UAV platform brings great challenges to these detectors for precise localization of vehicles in aerial images. To improve the detection performance for vehicles with different scales, we propose a novel detection algorithm which consists of three stages. In the first stage, to reduce the distortion of vehicles during image resizing and keep more information of aerial images, we utilize an image cropping strategy to divide the image into two patches. In the second stage, we combine the original image and two patches into a batch and detect vehicles with a Convolutional Neural Network (CNN). For feature representation in our detector, we propose Scale-specific Prediction to strengthen the multi-scale features of vehicles with context information. In the final stage, to fuse detections and suppress false alarms, we propose an Outlier-Aware Non-Maximum Suppression. Extensive experiments are conducted to demonstrate the superiority of the proposed algorithm by comparison with other SOTA solutions.
Active Disturbance Rejection Control for DC/DC Converters in MTDC Systems

In this paper we demonstrate theoretically the stability of MTDC systems with multiple ADRC-controlled converters. In particular, the ADRC model, consisting of a linear quadratic Gaussian (LQG) structure with reference trajectory generator, is applied for the voltage regulation of the DC/DC source-side converters in MTDC systems. Small-signal stability analysis demonstrates the ability of the local ADRC converter controllers to estimate and track the system dynamics, cancel internal and external disturbances and thus stabilise the system. The stability analysis shows theoretically the robustness of the ADRC model to uncertain load and converter models, and the ability to mitigate different interactions in the system. Time domain simulations show the ability of the linear ADRC model to deal with the non-linearities of the MTDC systems. Finally, experimental results demonstrate the easy hardware implementation of the ADRC model for the voltage regulation of DC/DC converters, and its ability to track the system dynamics fast and accurately for the tight DC voltage regulation.
A Novel Fault Detection Method for Running Gear Systems Based on Dynamic Inner Slow Feature Analysis

Process and sensor noise often affect the monitoring of slowly changing faults in high-speed trains, which seriously increases the difficulty of slow-change fault detection (FD). By introducing the idea of dynamic inner into the framework of multiple statistics, a novel dynamic inner slowness feature analysis (DISFA) is proposed in this article. This method considers both dynamic and static conditions and improves the detection speed of slow-change faults of running gears, and improves the detection rate of slow-change faults. Compared with other traditional methods, this method is more sensitive to slow changes, can effectively analyze fault-related data, and improve the detection efficiency of slow-changing faults. First, the validity of the method proposed in this article is proved by mathematical deduction, then it is verified by actual operation devices.
Vibration Analysis of a PMSM through FEM Multiphysics Simulation with Experimental Validation

Vibratory characterization of electrical machines is an important task that should be performed early in the design stages. Treat it as an afterthought may lead to high vibration and noise levels incompatible with the application requirements.In this paper, multiphysics FEM simulation is used to model a PMSM (Permanent Magnet Synchronous Machine) from both, the magnetic and vibratory point of view.A 2D magnetic simulation is carried out to get the excitation forces over the stator. In parallel, a 3D modal simulation will reproduce an Experimental Modal Analysis (EMA), identifying machine’s vibration modes. Finally, the Maxwell forces obtained by the magnetic simulation will be used as input to run an Operation Modal Analysis (OMA) which output is the vibration levels in the machine’s housing.Magnetic, EMA and OMA simulations has been validated through prototype testing. A detailed comparison between simulation results and experiments is presented.
An Optimal Genetic Algorithm with Support Vector Machine for Cloud Based Customer Churn Prediction

At present times, Customer Churn Prediction (CCP) becomes a familiar research area and is mainly solved by the use of machine learning models. The advancements in Internet of Things (IoT) and cloud computing platform allows collecting data about the customers to carry out CCP. This paper introduces an optimal genetic algorithm (OGA) with support vector machine (SVM) model for CCP. Initially, the OGA is derived by the use of double chain-quantum-genetic-algorithm. Next, the derived OGA is applied for optimizing the parameters of SVM namely C and ϒ. The outcome of the OGA-SVM model is tested against a benchmark dataset from telecom industry. The experimental outcome indicated that the OGA-SVM shows excellent results by offering better sensitivity of 94.50, specificity of 66.06, accuracy of 90.27, F-Score of 94.30 and kappa value of 61.17 correspondingly.
Multiclass Brain Tumor Classification Using Hyperspectral Imaging and Supervised Machine Learning

Hyperspectral Imaging (HSI) can be used as a non invasive medical diagnostic method when used in combination with Machine Learning (ML) algorithms. The significant captured data in HSI can be useful for classifying different types of brain tissues, since they gather reflectance values from different band widths below and beyond the visual spectrum. This allows ML algorithms like Support Vector Machines (SVM) and Random Forest (RF) to classify brain tissues such as tumors. Predicted results can be used to create visualizations and support neurosurgeons before injuring any tissue. This way neurosurgeons can be more precise, reducing any possible damages on healthy tissues. In this work, a proposal for the classification of in-vivo brain hyperspectral images using SVM and RF classifiers is presented. A total of four hyperspectral images from four different patients with glioblastoma grade IV (GBM) brain tumor have been selected to train models and, therefore, classify them. Five different classes have been defined during experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Results obtained suggest that SVM usually performs better than RF, generally achieving up to 97% of mean accuracy (ACC). However, RF performance had better results than SVM when classifying images used during training, obtaining almost 100% of mean ACC for all 5 classes described. This study shows the robustness of SVM and the potential of RF for real-time brain cancer detection.
Learning and Predictive Energy Consumption Model based on LSTM recursive neural networks

This paper presents a new model for learning and predicting energy consumption based on recurrent neural networks. Specifically, the Long Short Time Memory (LSTM) networks. In this model, we first calculate the moving average of the energy consumption according to a window, well-chosen in accordance with the nature of the data, in order to build an approximate output of the model. Then we use a deep neural network model that combines a multitude of different types of layers to learn how to predict energy consumption in any context. To implement this model, we used the TensorFlowJS Framework in web, mobile or embedded application context. By comparing the prediction results with those obtained by the moving average, we conclude that our model has learned perfectly well how to make good predictions and we can trust it in a different context.
High-Speed and Energy-Efficient Ring-Oscillator for Analog-to-Digital Conversion

Highly dependence of the power consumption with respect to the voltage supply makes current finer CMOS technologies become supplied with lower-than-1 V. Voltage-controlled-oscillator based analog-to-digital converters implemented with ring-oscillators scales properly with that requirement. However, conventional implementations of ring-oscillators limit the oscillation frequency due to the lack of available voltage to feed high currents. In this manuscript, we propose a novel circuit for a ring-oscillator that overcomes this issue. With only two devices between the supply nodes a delay cell is built. This allows us to reduce the voltage supply for certain oscillation requirements. In addition, the lower number of devices connected to the output nodes supposes lower parasitic capacitance and a reduction in the minimum achievable time delay, which increases the potential resolution. The proposed circuit is theoretically described and validated by simulation in a 65-nm CMOS process. Comparisons to the conventional implementations are made, showing improvements in terms of resolution, power, and area.
Motions Analysis for Stair Climbing by Two or Three Steps and cross over an obstacle for a Quadruped Robot

This paper aims to analyze the range of motions and characteristics of a quadruped robot for stair climbing and crossing over an obstacle. Humans have a higher range than quadruped animals due to longer legs. We can make fewer steps and reach the same distance, can climb stairs by two or three steps and not only one, and cross over high obstacles thanks to our leg range. However, stability is different because quadruped has four contact points, whereas humans have only. Quadrupeds maintain walking stability because their gravity center falls within the triangle of support from the three legs when one is up, but in the same condition, humans encounter a complex challenge to maintain equilibrium. We propose using these inherent characteristics of a quadruped robot for motion analysis of stair climbing by two or three steps and cross over a big obstacle while maintaining high stability in a quadruped robot. We aboard in the discussion significant aspects of leg dimensions and joint ranges with geometrical analysis to find the optimal position of the feet. The motion effectiveness was verified experimentally by implementing the motions from the preliminary tests to a quadruped robot.
Performance Analysis of Substrate Integrated Waveguide with different Dielectric Materials for X-band applications

A Substrate Integrated Waveguide design is made by constructing a defined pattern of cylindrical holes called Vias connecting the upper metallic layer passing through the substrate to metallic ground. Different substrate material exhibits different properties and performance varies accordingly. In the paper, we investigate the effects of different substrate used for the designing of Substrate Integrated Waveguide. The substrate materials used for the investigation are FR-4, RT5SS0 and Rogers RO3003 having dielectric constant $\varepsilon r$=4.3,2.2 and 3 respectively CST microwave studio software is used for designing and simulation of substrate integrated waveguide. designing process include the designing of a simple Micro-strip patch antenna followed by development of cylindrical hollow structure of accurate dimensions obtained by calculations from existing equations and conditions.
Transformer Based Multi-Grained Attention Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review. Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information. And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context. In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts. It is capable of extracting the syntactic structure features and syntax information of aspect and context. In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations. We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model.
Fabric defect detection using deep learning: An Improved Faster R-approach

In view of the various types of fabric defects, complex textures and the lack of existing defect detection technology, this paper proposes a new fabric defect detection technology based on deep learning. Specifically, based on the Faster R-CNN network model, a deep residual network is used instead of the traditional VGG-16 for feature extraction. In order to increase detailed shallow features, a feature pyramid model of different scales is constructed as the input of the RPN network. And the number of anchors are increased to adapt to small object detection scenarios. In addition, the regularized softmax classifier is used for training to improve the network convergence ability and classification accuracy. Experimental results on fabric defect dataset show that the improved model has fast convergence speed and excellent model performance. The average precision on the defect dataset has reached 94.66%, which is 4.35% higher than the original model.
Degradation Assessment of SiC MOSFETs under the Repetitive Short Circuit Ageing with Different Gate-Source Voltage Bias

A novel on-the-fly repetitive short circuit (RSC) ageing system under different gate-source voltage bias (VGS) is designed. The static behaviors of SiC planar-gate MOSFET C2M0080120D and SiC trench-gate MOSFET SCT3080KL under the low and high VGS biases RSC ageing are quantitatively and experimentally compared for the first time. A bidirectional threshold voltage shift with different degradation rates in both types of devices is observed. The smaller change of SiC trench-gate MOSFET SCT3080KL is observed compared to SiC planar-gate MOSFET C2M0080120D under low and high VGS biases RSC ageing.
Motor Imagery EEG-based Control of Intelligent Wheelchair Using Deep Belief Network Coupled with OVO-CSP Algorithm

Aiming at the shortcomings of low recognition rate and the cumbersome feature extraction of traditional machine learning methods during the process of controlling wheelchair with electroencephalography(EEG) signals, this paper presents a scheme for controlling intelligent wheelchair with multi-class motor imagery (MI) EEG based on deep learning framework. The representative model of deep learning deep belief network (DBN) was used to classify the MI EEG. Firstly, the improved OVO-CSP algorithm is used to extract the features of multi-class EEG. Then, five kinds of MI EEG signals are trained and classified by DBN. Finally, the comparison with traditional machine classification methods such as SVM and BP Neural Network proves the effectiveness and validity of the proposed method. The results show that the deep belief network can better extract the essential characteristics of multi-class EEG and improve the classification accuracy.
Region segmentation of sheep ribs based on fully convolutional neural network

The accurate segmentation of the lamb rib area is one of the key technologies for the research of split intelligent sorting robots. In order to accurately segment the lamb ribs on the conveyor belt, this paper takes lamb ribs as the research object and proposes a lamb rib image segmentation model based on fully convolutional neural networks (FCN). First, the lamb rib image is collected, and after preprocessing, the lamb rib image data set is established. Then, based on VGG16 as the basic network to build FCN8s model, and used the Tensorflow deep learning framework to achieve model training. Finally, by introducing precision (PA), mean pixel precision (MPA), average cross-combination ratio (MIoU) three image semantic segmentation standards, the segmentation performance of the FCN model is evaluated. The results show that for the lamb rib data set, the FCN8s model has an average merge ratio (MIoU) of 84.26%. Compared with level set and FCM, Miou of fcn8s model is improved by 6.47% and 11.12% respectively.
Real-Time Thermal Imaging Using Augmented Reality and Accelerated 3D Models

We present an innovative method to allow real-time visualisation of temperature fields on power electronic circuits using augmented reality. A real-time time-domain simulation incorporating the Block Arnoldi model order reduction techniques is implemented in the Unity game engine. Traditional methods run too slowly whereas the developed system allows an example model with 9232 thermal nodes to be dynamically simulated in real-time at a speed fast enough to allow for a smooth augmented reality visualisation. The aim is to show the feasibility of the technology and that it could be applied to visualising electromagnetic fields in future work.
Switching Output Feedback Control of LTI Systems

For linear time-invariant (LTI) systems, we consider a switching control composed of a collection of static output feedback controllers and an output dependent switching law so that the closed-loop system is asymptotically stable when the controllers are switched under the switching law. Both certain and uncertain LTI systems are dealt with. Multiple Lyapunov functions and a piecewise observer are proposed to implement the switching law, and sufficient conditions are derived for the existence condition and the computation of multiple Lyapunov functions and static output feedback gains.
High Voltage Direct Drive Generators with Multiphase Single Layer Fractional Slot Concentrated Windings

This paper presents design considerations for a high voltage direct drive generator for an high voltage DC offshore wind park application. The paper investigates 3-phase and 6-phase fractional slot concentrated windings for high voltage surface mounted permanent magnet machines. The paper focuses on q=2 /5, 50 pole-pairs and q=4 /11, 55 pole-pairs machines in the single layer configuration with different number of phases. The analysis has been executed by both analytical and finite element analysis methods. The paper reports the results of the comparison in terms of torque performance, power factor and losses, aiming to demonstrate the advantages and limitations of the use of fractional slot concentrated windings single layer multiphase generators for high voltage wind turbines. It is found that single layer multiphase configurations can improve the performance of the generator in terms of torque density, torque ripple and efficiency, but the low power factor can be a limiting factor for the exploitation of this technology for high voltage high power generators for wind turbine applications. In this paper, a solution to improve the PF without sacrificing the efficiency is proposed.
Synchronization of nonlinear multi-agent systems via exact feedback linearization

Synchronization of a multi-agent system composed of identical nonlinear agents admitting exact feedback linearization is presented. The agents may not be fully linearizable, however, if they are minimum-phase systems, synchronization is achieved. Both leader-following as well as the consensus problem are treated. In the latter case, it is guaranteed that the average dynamics equals to the dynamics of one agent. The results are illustrated by an example.
Predicting Students Performance in Online Courses using Classification Techniques

Data Mining techniques have become widely used for several types of tasks, such as data classification. Data classification aims to classify data into set of classes based on trained model called classifier. Such techniques are used to extract important information needed to make decisions in different scopes, one of them is Education. This paper, applies several classification techniques on an academic dataset for online courses, to find the best classifier to predict the student performance, using certain features that might affect this performance. Classifiers used are Decision Tree, Artificial Neural Network, Support Vector Machine, and K-Nearest Neighbors. Experiments used real data and evaluated the models based on four performance measures: accuracy, precision, recall, and F-measure. Results show that Decision Tree and Artificial Neural Network achieved best results among other classifiers.
A Decentralized Frequency Regulation Scheme in AC Microgrids

Microgrids (MGs) are becoming active segments of future power systems. Frequency regulation in islanded MGs is of high importance to guarantee stable and reliable operation under different loading conditions. This paper proposes a decentralized frequency regulation of AC MGs, which relies on the local information. Comparing to the conventional approaches, the proposed scheme does not require a communication system for frequency regulation, resulting in higher reliability and lower costs. Simulation results verify the effectiveness of the proposed frequency control strategy.
Effects of Rotor Flux Barrier Design on Torque Ripple and High Speed Performance of Synchronous Reluctance Machines

Synchronous reluctance machines (SynRMs) suffer from high torque ripple, low power factor and poor high speed performance due to their singly excited nature. To overcome these challenges, rotor geometries are optimized to get a low torque ripple and ferrite magnets are inserted in the flux barriers to get a higher power factor and a better high speed performance. This study aims to compare the torque ripple, power factor and high speed performance of machines with different rotor designs that have the same q-axis iron-to-air ratio and flux barrier shape but various spatial iron and air distribution. Only the SynRMs without permanent magnets (PMs) are considered, since PMs can be added to all the designs to further enhance their performances. Firstly, rotor geometries with selected iron and air distributions are individually designed in terms of low torque ripple by using finite element analysis (FEA) models. Then, the torque-speed and power-speed characteristics of the selected designs are compared. It has been shown that the iron and air distributions influence the machine high speed characteristics much more than their maximum torque outputs. Designs with around 20% more power output at maximum speed can be realized only by changing the iron and air distribution within layers. This is a very useful finding in the process of designing SynRMs with a long extended speed range with or without PMs.