Miracle-dz/UniFAR-FTUs huggingface dataset:
Computationally Efficient Design Procedure for Single-Layer IPM Machines

Interior Permanent Magnet machines are widely used in several applications thanks to their optimal tradeoff between torque and flux weakening capability figures. Their design process massively relies on the use of optimization algorithms coupled with Finite Elements Analysis, as dictated by the high number of input parameters and the inadequateness of design equations. This paper proposes a faster design flowchart, based on analytical models for V-type IPM machines. The proposed design procedure is validated against FEA results, referring to the known benchmark of the Toyota Prius 2010 electric motor. Although the precision of the results is not comparable to that obtainable with FEA, the proposed closed-form model is useful and insightful during the preliminary stages of the design. The limits of accuracy of the proposed equations are commented critically. Guidelines are given on how to embed the presented approach into a comprehensive design procedure.
An Improved Modular Charge Equalization Structure for Series Cascaded Battery

To extend battery lifetime and maximize battery utilization capabilities in series cascaded battery stacks, Charge Equalization (CE) is essential. To achieve this goal, efficiency, minimum added costs and elements, CE time, operating frequency, maximum number of possible serial connections, element ratings, and control simplicity, are the most important concerns. In this paper, an improved modular active CE structure for all possible operation modes is presented. The topology has the benefit of minimum added elements, simple and effective control algorithm, suitable efficiency as well as modularity. Presented simulation results, as well as comprehensive analysis of the circuit, verify the authenticity of the proposed structure. The obtained results show that the batteries are balanced with suitable speed and efficiency.
Human Ear Recognition Using HOG with PCA Dimension Reduction and LBP

Human ear recognition has attracted more attention in the past several years. In terms of the redundant information of Histogram of Oriented Gradient (HOG), this paper proposes a human ear recognition method in which the principal component analysis (PCA) was carried out on the HOG dimension reduction, then fused with local binary pattern (LBP) features. Using minimum distance classification for classification, experiments on USTB human ear image library shows that the proposed method improves the recognition rate and speeds up the training and detection.
An Air-Cooled YASA Motor for in-Wheel Electric Vehicle Applications

In-wheel motors present a range of opportunities for innovation in electric vehicle design as the torque produced at each of two or four wheels can be controlled individually. A high aspect ratio (large radius, short axial length) motor is required to fit within the wheel. Due to its location, liquid cooling of the in-wheel motor is difficult and undesirable, but a high power density is required to reduce the mass-which is particularly important as it is unsprung-and fit the space envelope. Furthermore, a high torque density is required to eliminate the need for a gearbox. These constraints create a real challenge for the design of a machine for this application. An axial field machine using a Yokeless and Segmented Armature (YASA) topology is designed to fit these requirements as such a machine has clear advantages when considering the high aspect ratio. A soft magnetic composite (SMC) material is utilised to carry the flux in its non-planar path without incurring excessive losses or requiring an impractical and difficult to manufacture lamination design. A novel cooling arrangement involving heat-spreading elements on each armature segment is employed to improve heat dissipation and hence power density. The design, analysis, manufacturing and testing of the motor is described in this paper to verify the concept against the requirements outlined above.
Finite element simulation of isothermal regions in serpentine shaped PCB electrodes of a micro-PCR device

Polymerase chain reaction (PCR) is a method or device which is widely used in biology labs. Its main job is copying DNA segments. Micro-PCR is the term referring to the small-scale version of this device. It has several advantages including the minute reagent consumption and faster response times. One of the challenges in micro-PCR technology is creating uniform-temperature regions. Thin film heaters have been used in literature to create the needed temperature zones. In this work, printed circuit board (PCB) technology has been used to create meander-shaped micro heaters on FR-4 substrate. Two separate temperature zones have been designed on the substrate. In order to improve temperature uniformity in these areas, a uniform copper layer has been designed on the back of the substrate. Thanks to the double-sided PCB technology, having a uniform copper layer on the back of the substrate is trivial. To study the effect of the copper layer on temperature uniformity, a steady state thermal model has been developed using Ansys®Workbench. Based on the simulation results, the created model can successfully predict temperature distribution in these heat generation areas. Besides, it is shown that the copper layer will lead to more uniformity in temperature distribution.
Chart Classification Using Simplified VGG Model

Data visualization is created due to the need to show vast amount of data in a more transparent way. Data visualization often contains key information that is not listed anywhere in the text and allows the reader to find out important information and longer-term memory. On the other hand, Internet search engines have a problem with filtering data visualization and associating visualization and query that the user has entered. With the use of data visualization, all blind people and people with impaired vision are left off. This paper uses machine learning for classifying charts into 10 categories. The total accuracy achieved across all categories is 81.67%.
Detection of Shockable Rhythm during Chest Compression based on Machine Learning

Recently, more and more evidence has shown that reducing the interruption of chest compression to increase the duration of blood flow perfusion is the key to improve the successful rate of CPR. Many researchers have been devoted to designing algorithms that can identify shockable ECG rhythms during chest compression. Most of these studies focused on designing filters that can eliminate CPR artifacts from the corrupted ECG signals. Although some adaptive filtering algorithms have suppressed the artifacts of chest compression to a great extent, the residual artifacts of CPR still can not be ignored. In this paper, we constructed a robust shockable rhythm detection algorithm based on machine learning, which can distinguish accurately different types of ECG signals even under the condition of severe CPR artifacts interference. A total of 21 metrics were extracted from the ECG signals by a large number of retrospective studies of the existing shockable detection algorithms. After feature selection, 13 metrics were selected to participate in BP neural network construction. The performance of this network was evaluated on constructed corrupted ECG signals, sensitivity and specificity could be maintained above 99% and 95% respectively even at a particularly high corruption level.
Detecting PCB Assembly Defects Using Infrared Thermal Signatures

A Printed Circuit Board assembly (PCBA) testing approach using infrared thermal signatures is presented. The concept of thermal signature for PCBAs is introduced. Based on this concept, the testing method is able to: - detect assembly defects such as presence, polarity, value and solder (shorts and opens) and in some cases component health state - classify the components mounted on the PCB into a number of classes (e.g. – fault free(reliable), functional (less reliable), faulty ). According to the thermal signature of each component on the PCBA, PCBAs can be also classified in the same number of classes. In this article a special focus is put on capacitor defects especially capacitor value defects. Therefore, they will be the main components tested. The fault detection indicator used in this proof of concept is a statistical mean squared error measure (MSE).
Research on online user comments in artificial intelligence times

In this era of artificial intelligence, the online user comment in the e-commerce platform contains a lot of valuable information. Mining and analyzing these online comments can help enterprises and organizations better understand the clients’ needs, attitudes and concerns. Based on studies on word of mouth, automatic tag generation, and deep learning technologies, this paper uses Long Short-Term Memory (LSTM) as a hidden layer neuron, introduces the attention mechanism, captures information in text sequences, understands user comment text, and builds a model of automatic tags generation. The model can be used to help consumers make smarter shopping decisions and help merchants make better business decisions.
Microstrip Antenna Design H-Shaped Planar Array 4 Elements Using Circular Slot for Fixed WiMAX Network 3.5 GHz Frequency

Fixed WiMAX network access requires Customer Premise Equipment (CPE) that uses small and easy to integrate antennas. This research will design a 4 element microstrip antenna using an H-shaped patch with the addition of a circular slot in the center. Antennas are used for fixed WiMAX CPE applications at 3.5 GHz (3.4–3.6 GHz) working frequency. The substrate used is FR4 with dielectrics constant 4.4 and Duroid with dielectrics constant 2.2. The feeding technique used is microstrip line. Determination of antenna dimension is done by using mathematical equation. Result design using software simulation obtained VSWR antenna using FR4 was 1.013, return loss −43.803 dB and gain 4.7083. While VSWR antenna using Duroid substrate was 1.015, return loss −42.5923 dB, and gain was 8,486 dB. Characterization of rectangular and circular slot sizes affects the working frequency range. Antenna with FR4 substrate has optimal working frequency on rectangular stripe characterization of 4 mm, 4.5 mm, and 5 mm width with 2–3 mm slot size variation. Duroid substrate design antennas have optimal working frequency with rectangular piece characterization of 5 mm, 5.5 mm, and 6 mm and variations of 4 – 5 mm slot size.
Extract Fault Signal via DWT and Penetration of SVM for Fault Classification at Power System Transmission

Power transmission lines are extremely important for the power system to deliver energy of electricity from the plant to the load. The short circuit of fault often occurs in the transmission line and may lead to disconnecting the power supply to the load. This study implements a hybrid technique that is Discrete Wavelet Transformation (DWT) and Support Vector Machine (SVM) for classification of fault in the transmission line. The DWT was created to extract the detailed signal of transient D8 and D9 (order of 4) at 50 kHz for sampling frequency. The value of Root Mean Square (RMS) will be determined by the coefficients D8 and D9 for training and test data using SVM technique. Furthermore, SVM is utilized to detect the fault for each phase and the ground is discovered in the type of fault. The SVM technique has been run using parameter C and kernel scale to achieve the great results of classification of the fault. Type of simulating fault has a variation of location of the fault, fault of resistance and initial angle. The training and test data run for the Test System of Riau, Indonesia. The test result for the classification of fault reaches the highest accuracy of 100%.
Finite Control Set-Model Predictive Control for the Dual Fed Common Dc-Link Open-End Winding PMSM Drive

This paper proposes a Finite Control Set-Model Predictive Control (FCS-MPC)for a dual-fed common dc-link Open-End Winding Permanent Magnet Synchronous Motor (OEW-PMSM). The common dc-link configuration significantly simplifies and reduces the costs of the topology but allows the Zero-Sequence Current (ZSC)to flow freely in the system. The Zero-Sequence Voltage (ZSV)produced by the two two-level Voltage Source Converters (VSCs)and the non-sinusoidal machine back-electro magnetic force (back-EMF)are responsible for the flow of additional current and the reduction of the overall system efficiency. The FCS-MPC is considered for the first time in this application as a simple solution to achieve both machine control and the ZSC suppression simultaneously. The main drawback of the FCS-MPC is the high amount of calculation necessary, here a different formulation of the FCS-MPC is proposed to significantly reduce the computational effort required.
Development of a Non-Invasive Procedure to Early Detect Neonatal Sepsis using HRV Monitoring and Machine Learning Algorithms

Heart rate variability (HRV) monitoring has shown to be promising to early diagnose neonatal sepsis and therefore the objective is to develop a minimally invasive and cost-effective tool, based on HRV monitoring and machine learning (ML) algorithms, to predict sepsis risk in neonates within the first 48 hours of life. Seventy-nine new-borns, with less than 48 hours of life and with a gestational age between 36 and 41 weeks, borned in the Consorci Hospital General Universitari of València were enrolled after the tutor's authorization. Fifteen of them were diagnosed with sepsis. Electrocardiogram signal was monitored and recorded for 90 minutes and HRV parameters were calculated. Clinical data was extracted from the electronic medical record and sepsis was confirmed by central laboratory analyses. Supervised ML algorithms were evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Significant differences were observed in the power spectrum density at very low and low frequency bands and in long-term non-linear components. The AUC revealed that Adaptive boosting was the ML model with greater sensitivity and specificity (AUC=0.94) followed by Bagged Trees (AUC=0.88) and Random Forest (AUC=0.84). In conclusion, HRV and Adaptive Boosting algorithm can be used to identify new-borns with higher risk of suffering neonatal sepsis during their first 48 hours.
Speeding Up the Dissipating Energy Flow Based Oscillation Source Detection

This paper enhances the Dissipating Energy Flow (DEF) method for forced oscillation source detection. The development focuses on improving the speed of the DEF method for oscillation source detection employed by ISO-NE. This is accomplished by using multi-signal modal estimation and statistical correlation to achieve robustness and detection accuracy. The effectiveness of the proposed method is tested on simulated and actual oscillation data showing that detection and localization can be achieved with shorter data windows. In addition, the utilization of the DEF as an energy filter to minimize false alarm rates is examined. Finally, real PMU data is used to evaluate the consistency of the DEF method and track the actual direction the Dissipating Energy (DE) flows in the power network.
A comparative study of system-level PMSM models with either current or flux-linkage state variables used for vibro-acoustic computation

This paper explores different high-fidelity, system-level models for permanent magnet synchronous machines. The main attributes examined are electromagnetic torque and forces representing the main contribution sources for noise and vibration in electrical powertrains. First, two types of electromagnetic models extracted from finite element analysis are presented: the current and flux-linkage state variable models. Afterwards, two force transformation models used for system-level vibro-acoustic computation are explored and a spectral convergence test is proposed. Procedures regarding model implementation, such as look-up table derivation/inversion are discussed and design trade-offs are analyzed. Finally, the different model results are benchmarked against a finite element model and the real-time performance is presented.
VAE Based- NCF for Recommendation of Implicit Feedback

the recommendation system is one of the core tasks of data mining. It divided into the recommendation system for explicit feedback and the recommendation system for implicit feedback. In recent years, many researchers have combined deep learning with recommendation system of explicit feedback and achieved excellent results. But it is very rare for implicit feedback. In this paper, we apply deep learning to the recommendation system for implicit feedback, and propose a new model that is combined with Neural Collaborative Filtering and Variable automatic-encoder. We use the MovieLens dataset to evaluate our proposed model. Experimental results show that the proposed model effectively improves the accuracy and quality of the recommended results, the Precision is 0.715 and the NDCG is 0.436 without manual parameters.
Comparison of inner and outer rotor configurations in slotless PM machines with PCB windings

For low power applications, two kinds of topologies and configurations of PM machines exist, the slotted and slotless topologies and the inner and outer rotor configurations. When searching for high torque densities, e.g. in robotic applications, it has been shown that the outer rotor configuration outperforms the inner rotor configuration in the slotted topology. In this paper, these two configurations are compared but for the slotless topology based on both an optimized motor constant and a nominal torque. It is shown that the inner rotor configuration is superior in terms of motor constant compared to the outer rotor configuration. On the contrary, in terms of nominal torque, the outer rotor configuration outperforms the inner rotor configuration because of its higher heat dissipation capabilities.
Semantic-Embedding and Shape-Aware U-Net for Ultrasound Eyeball Segmentation

Segmentation of eyeball region from ultrasound images is a new research direction for the diagnosis of ophthalmic diseases. Despite the advantages of convenience and cheapness, ultrasound images bring more noise and fuzzy contour compared with other medical images. Existing methods fail to give a segmentation with reasonable eyeball shape, especially when the contour is ambiguous. In this paper, we propose a novel framework based on convolutional neural network, named semantic-embedding and shape-aware U-Net, to deal with the segmentation in blurred images. A signed distance field is used as label instead of the traditional binary mask label to add shape prior in network. The applying of semantic embedding modules fuses semantic information between different stages of the network. Experimental results show that our method improves the ability to segment image with blurred edges and outperforms existing methods in the accuracy of segmentation.
Multi-Task Embedded Convolutional Neural Network for Hyperspectral Image Classification

Convolutional neural network (CNN) is a powerful visual model which has a significant performance in various visual recognition issues, and attracted considerable attention in recent years. Due to the strong correlation between bands and small sample set (3S) problem, hyperspectral image classification issue remains a challenging problem. In this paper, we construct a novel multi-task framework with embedded convolutional neural network to obtain stronger discriminative capability for hyperspectral image classification. A variance of CNN, which is named Network in Network (NIN), is used to construct three sub-model. Here 1 × 1 convolutional filter is adopted and average pooling layer is employed to replace the full connect layer. The input of the first sub-model is spatial-spectral schroedinger Eigenmapes (SSSE) which can provide fused information of spatial and spectral information. Meanwhile, due to the significant capability of extracting spatial texture of the uniform local binary (ULBP), the histogram feature extracted from ULBP is used as the second sub-model input. Raw hyperspectral data is input to the last sub-model for supplying external information that SSSE and ULBP have lost. Experimental results demonstrate the effectiveness of the proposed model.
Energy and QoS-Aware Contention-Based Medium Access Control in Machine-Type Communications

Special characteristics of Machine-Type Communications (MTC) result in new challenges in 5G mobile networks including Medium Access Control (MAC) mechanisms. The traditional methods for contention-based MAC are unable to manage a massive number of simultaneous requests. On another side, recent methods for MTCs have not adequately attended to energy consumption of machines along with QoS. In this paper, a method for contention-based massive access protocol is presented based on p-persistent CSMA which considers both energy and delay requirement of machines, a multi-objective optimization problem is solved by BS at the beginning of each time frame in order to find the optimum value of transmission probability based on the number of machines and their distances from the BS. The optimization problem aims at achieving maximum energy efficiency and minimum access delay. To avoid the overhead of solving the optimization problem, the value of probability is temporarily updated by the machine at the end of each time slot, based on its delay requirement and the number of its recent access failures. The simulation results show a considerable increase in the number of successful transmissions, a decrease in access time and improvements in throughput and energy consumption.
Neural Network Based Phase Compensation Methods on Monaural Speech Separation

Monaural speech separation is a very challenging task, for the limitation of information from only a single channel. After Deep Learning is introduced into the monaural speech separation, researchers have made great improvement on the separation performance with the progress of different network structure. Among various of separation methods, especially methods with masking-based training targets, the influence of phase were always neglected during the separation process. In this paper, the impact of phase on monaural speech separation is discussed and proved using theoretical explanations and examples. New training targets in complement of existing magnitude training targets were trained through neural network methods to compensate for phase of target in order to achieve better separation performance. Comparisons and evaluations show that using phase compensation in separation boosts separation effect to a certain degree.
Direct Torque and Flux Control of Dual Stator Winding Induction Motor Drives based on Emotional Controller

Recently, Dual Stator Winding Induction Machines (DSWIMs) has attracted the attention of many researchers. These induction machines which have two sets of three phase windings with unequal pole pairs and standard squirrel cage rotors, can overcome the associated complications of conventional three phase induction machines in zero speed operation regions. Generally, having unequal pole pairs brings the ability of independent operation of winding sets and accurately flux estimation in various rotor speed ranges. This paper proposes a direct torque and flux control system for DSWIMs based on Brain Emotional Learning Based Intelligent Controllers (BELBICs). These controllers which are model and parameter independent, are auto learning and adaptive. Indeed, in the proposed DSWIM-drive system the reference values for d and q axis voltages of stator winding sets are determined through BELBICs to directly control flux and torque of each winding sets. The reference value of winding fluxes is determined to minimize the stator currents and the torque commands are assigned by a torque sharing algorithm which enables the DSWIM to operate in a wide speed region, ranging from nominal positive to nominal negative. The proposed control system simulation in MATLAB/SIMULINK verifies its excellent performance in various speed ranges including zero speed.