Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison Between Random and Hairpin Winding in E-mobility Applications

This paper presents a comparative study on traction motors built using random and hairpin winding. Advantages and disadvantages of each winding configuration are reported and challenges of using hairpin winding are detailed. The paper focus is placed on winding copper loss, which is the main root of performance differences in motor equipped by random and hairpin windings. A motor is designed using both winding types. Differences in losses and their effect on motor efficiency is studied. Considering the higher slot fill factor of hairpin winding, DC component of copper losses in hairpin winding has less contribution on the motor efficiency, however, higher AC copper loss is seen in hairpin windings. Thus, motors designed and manufactured using hairpin winding have higher efficiency in comparison to the equivalent random wound motors, especially at high load and low speed.
Emotion Classification of Facial Thermal Images using Sparse Coded Filters

In affective computing, identifying the true emotion of an individual is still a significant concern. Under real-life conditions, muscle movements are found to be unreliable to identify the behavior of a person. Researchers have used thermal modality to recognize real emotions; however, features used were originally handcrafted for visible modality and were directly adopted for thermal modality. Since visible and thermal images are built with different principles and have distinct characteristics, adopted features do not perform well. This paper presents an algorithm to classify six basic emotions from thermal facial images. The primary aim is to find the thermal modality-specific filters using a subset of the NVIE dataset. For this, an optimal set of local region-specific filters are generated using convolutional sparse coding. The optimal set of filters is used for feature extraction in which the idea of a histogram-based feature descriptor known as binarized statistical image features (BSIF) is used. Further, a supervised dimensionality reduction algorithm acknowledging the correlation between classes is employed based on the idea of discriminant correlation analysis (DCA). Finally, six emotions are classified using a linear support vector machine (SVM). The improved accuracy validates the performance of the proposed method with respect to previous works.
MPC Based Full-speed Domain Control Strategy of Interior Permanent Magnet Synchronous Motor

This paper presents model predictive control (MPC) of interior permanent magnet synchronous motor (IPMSM) for electric vehicle. In the process of full-speed domain control, the disadvantages of complex parameter setting and slow dynamic response are inevitable for PI control. In this paper, the current control principle of full-speed domain is calculated, then the cascade MPC system is designed to replace PI control system. However, cascade MPC system does not fully utilize the characteristics of multi-objective simultaneous optimization of MPC, so non-cascading MPC system with less time delay and faster response speed are designed. The performance of MPC system and PI control system under full-speed domain control are compared through simulation and experiment. The result shows that MPC based full-speed domain control strategy has better dynamic performance and anti-interference ability.
Blok Zinciri ile Yeni Nesil Dağıtık Defter Teknolojilerinin Karşılaştırılması

Özet— Tarihin ilk yıllarından itibaren alım-satım gibi güvene dayalı ekonomik işlemler merkezi otoriteler tarafından kayıt altına alınmaktadır. Kağıdın icadı ile kayıt işlemleri daha düzenli ve detaylı bir şekilde yapılmaya başlanmıştır. Son yıllarda da bilgisayarların yaygınlaşması ile birlikte kayıt işlemleri dijital olarak yapılarak kayıtların yönetimi kolaylaştırılmıştır. Yaşanan her teknolojik gelişme veri kayıt işlemlerinde kolaylık ve hız sağlamıştır. Gelişen teknoloji ile birlikte işlemler hızlanmış ancak kayıtların yönetim merkezi otoritelerin kontrolünde kalmıştır. Dağıtık defter teknolojisi kayıtların merkezi otoritelerin kontrolünden çıktığı yeni bir yaklaşım sunmaktadır. Dağıtık defter teknolojisi merkezi bir veritabanı veya merkezi yönetimi olmayan verilerin katılımcılarda dağıtık olarak tutuldğu eşler arası ağdır. İlk dağıtık defter uygulaması 2008 yılındaki ekonomik krizin ardından ortaya çıkan Bitcoin ödeme sistemi ve kripto parasıdır. Bitcoin’in ortaya çıkmasında merkezi otoritelerin verdiği kötü ekonomik kararlar etkili olmuştur. Bitcoin ile amaç mevcut finansal sistemleri merkezi otoritelerin kontrolünden çıkarmaktır. Bitcoin’in popüler hale gelmesiyle birlikte arkasındaki blok zinciri teknolojisi dikkat çekmeye başlamıştır. Başlangıçta Bitcoin nedeniyle blok zinciri teknolojisi finans sektörü ile özdeşleştirilse de tedarik zinciri, sağlık, tarım, enerji gibi farklı sektörde kullanımına yönelik çalışmalar yapılmaya başlanmıştır. Blok zinciri teknolojisinin farklı sektörlerde kullanılabilmesi için Hyperledger, Ethereum ve Corda gibi blok zinciri altyapıları geliştirilmiştir. Ancak yapılan geliştirme çalışmalarında blok zinciri teknolojisinin hız ve ölçeklenebilirlik açısından eksiklikleri olduğu görülmüştür. Bu nedenle hız ve ölçeklenebilirlik açısından gelişmiş yeni dağıtık defter teknolojileri geliştirilmiştir. Yeni geliştirilen popüler dağıtık defter teknolojisi türleri Directed Acyclic Graph, Hashgraph, Tempo ve Holochain’dir. Yeni nesil dağıtık defter teknolojilerinin öne çıkan farklı özellikleri bulunmaktadır. Yapılan çalışmada blok zinciri ile yeni nesil dağıtık defter teknolojileri karşılaştırılmıştır.
Characterization of a Flexible Piezopolymer-based Interdigital Transducer for Selective Excitation of Ultrasonic Guided Waves

Structural health monitoring (SUM) is a term that groups together techniques adopted to evaluate in a continued fashion the structural integrity and degradation of technical appliances. SUM is particularly attractive for components that are difficult to access or expensive to take off-line. Among many other techniques, SUM can be performed using ultrasonic guided waves (UGW) which have an advantage of traveling over long distances. Various guided wave modes exist along with many methods for their generation and sensing, e.g by means of interdigital transducers (IDT). This contribution is dedicated to the design and characterization of a flexible piezopolymer-based IDT which allows for the selective excitation of UGW, resulting in more straightforward data analysis. The designed IDT was characterized using a 3D Laser Doppler Vibrometer (3D LDV) in the air to identify and analyze the IDT's vibration modes. Then the transducer was mounted on an aluminum plate, and the generated wavefield was measured with the 3D LDV. According to this investigation, we demonstrate that it is possible to selectively excite desired guided wave mode, namely the A0 mode, suppressing the excitation of the S0 mode. Moreover, the measured wavefield allows for analysis of the directivity of the designed IDT. All in all the results show good correlation between theoretical predictions and measured values, thus allowing to use the current design in terms of selective excitation as it is.
Fingerprint Recognition By Using Convoloutional Neurla Network And Support Vector Machine Classification

Biometrics seeks to solve the problems of traditional verification methods by using certain physiological properties associated with an individual. Among all the biometric indicators, fingerprints have been shown to have good levels of reliability. The most widely used local representation is based on the details (minutiae) of the fingerprints. The pattern of the minutiae on a fingerprint forms a valid representation of the fingerprint. The minutiae that are most used for automatic recognition are branches and endings. However, given fingerprint acquisition techniques, it is common for endings and bifurcations to undergo deformations, which is why they are commonly referred to as minutiae. That is why in this document we will simply refer to these characteristics as minutiae. In this work we describe the results obtained using a methodology proposed for the recognition of minutiae using convolutional neural networks CNN, trained with different databases that contain fingerprints then we use the support vector machine classification to classify newly input images of fingerprints based on the features extracted by the CNN and matched with the dataset, our method proves to have better accuracy and lower MSE than the previous linear methods use for fingerprint recognition.
Eyes Status Detector Based on Light-weight Convolutional Neural Networks supporting for Drowsiness Detection System

The drowsiness is the leading cause of many accidents on the road. These causes can be reduced by using the drowsiness alarm or drowsiness detection system. These systems monitor drivers while driving and alarm when they don’t focus or have some abnormal signs in the driver’s body. Currently, most methodologies use the analysis of human behaviors, vehicle behaviors, and human physiological conditions. This paper regards eyes status analysis based on deep learning method using proposed Convolutional Neural Networks (CNN) with two stages are face detection and eyes classification. The face detector employs a single detector module and shallow layer, then the eyes classifier using simple CNN without ignoring the accuracy. As a result, the average speed was tested in real-time by 50.03 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.
High frame rate color Doppler to measure intraventricular pressure gradients

In echocardiography, Color Doppler imaging (CDI) is mainly used for a qualitative analysis of abnormal blood flows. Indeed, any quantitative assessment is hampered by the compromise between frame rate, field of view, and measurement quality. Recently, we showed that high frame rate (HFR) imaging techniques, applied to CDI, soften those limitations at the expense of reduced image quality and penetration depth. Therefore, the aim of this study was to assess whether HFR-CDI could provide non-invasive, quantitative estimates of the intraventricular pressure gradient (IVPG), which may be an important marker for the diagnosis of diastolic function. Acquisitions, conducted on 29 healthy volunteers, showed that both diverging wave and multiline transmission allow achieving a high enough frame rate for a quantitative use of CDI. Indeed, diastolic IVPG peak values obtained with HFR-CDI where not statistically different from those obtained with standard color M-Mode, i.e. the gold standard reference.
Just Another Attention Network for Remaining Useful Life Prediction of Rolling Element Bearings

Rotating equipment often fails due to faults in the rolling element bearings. The remaining useful life (RUL) prediction of the bearings plays a critical role in prognostics and decision-makers. In this study, attention mechanism is integrated into the internal of just another network (JANET) unit, and a new improved version of JANET unit, namely, just another attention network (JAAN), is firstly presented. Firstly, root mean squares (RMS) of the test-to-failure datasets are calculated to characterize the degradation behavior of the bearings. Then, the prediction model is constructed by stacking multiple JAAN units to estimate the RUL values of rolling element bearings by existing RMS values. Extensive experiments on PRONOSTIA dataset are carried out to validate the superiority of the presented approach. The experiment results show that the proposed JAAN achieves good prediction results than other advanced technologies.
Improving the Feature Set in IoT Intrusion Detection Problem Based on FP-Growth Algorithm

This paper proposes and develops a new method to improve the feature set in IoT intrusion detection problem based on FP-Growth algorithm. Extracting a good feature set makes an important contribution to improving the accuracy of the IoT intrusion detection problem. Current studies on the detection of new IoT attacks focus on extracting independent features without considering the relationship between specific features and groups of features. Therefore, in the paper, we analyze and evaluate the original feature set to find the relationship between feature groups and add new features generated from relationships with a certain threshold to the original feature set to create the improved feature set. The detection of relationships reaching threshold is based on combination law mining technique according to FP-Growth algorithm. The improved feature set was tested with detection method based on convolution neural network with accuracy up to 98.2% and 1.45% improvement compared to the original feature set.
A 3D Motion Compensation Method for High Frame Rate Volumetric Ultrasound Imaging based on Velocity Vector Estimation: A Simulation Study

Three-dimensional (3D) diverging wave compounding (DWC) based on a matrix array is a modern approach for high frame rate volumetric ultrasound imaging. However, the image quality of 3D DWC is easy to be deteriorated due to its high sensitivity to tissue motion. Current Doppler-based motion compensation (MoCo) methods just correct one-dimensional (1D) motion because only the velocity component in the beamforming direction can be estimated. In this paper, we propose a 3D MoCo method based on 3D velocity vector estimation to eliminate motion artifacts in three dimensions. The virtual sources are activated in a “Round-trip” sequence to record the backscattered echoes of the target. A set of velocity components are estimated with a modified Doppler method and beamforming with respect to the receive sub-apertures. The velocity vector is then calculated by modeling the corresponding components into a least-square optimizer. Finally, the artifacts in the three dimensions are compensated sequentially based on the vector maps. Computer simulations on static and moving point targets were performed to validate the feasibility of the 3D MoCo method. The results demonstrated that the proposed 3D MoCo method can restore the profiles of the point targets with a higher structural similarity (SSIM) when compared with the 1D MoCo method.
Online Searching MTPA Control of Wound Rotor Start/Generator Drives Considering Cross-Coupling Effect

The cross-coupling effect is the most common phenomenon in the electric machinery operation, it has the influence on the machine efficiency and control scheme. However, the cross-coupling effect is ignored in the existing virtual signal injection (VSI) maximum torque per ampere (MTPA) control. In this paper, the online searching MTPA control based on VSI scheme for Integrated Starter/Generator (ISG) system considering the cross-coupling effects is proposed. For obtaining the accurate torque gradient with respect to the torque angle online, the torque equation in the existing VSI scheme is extended by the cross-coupling effect is proposed. A prototype is set up and experiments are carried out to verify effectiveness the proposed control scheme.
Controlling a Bank Model Economy by Using an Adaptive Model Predictive Control with Help of an Extended Kalman Filter

This paper describes a bank-customer model and proposes a control scheme algorithm which guarantees to obtain a desired capital to asset ratio. The used control system is an Adaptive Model Predictive Control (MPC). The MPC offers the possibility to control the error between capital/asset ratio and the ratio required by the bank under the Basel regulatory framework, which should converge to zero. The uncertainties of the model are estimated by an Extended Kalman Filter and the model is online adapted to realize the prediction.
Case studies for load compensation using RSDCHBMLI based DSTATCOM using predictive current control

In this paper, the load compensation performance of a distribution static compensator connected to a non-stiff distribution network is presented under different conditions of loading and source voltages. The DSTATCOM is implemented using a reduced switch single DC-source based cascaded H-bridge multilevel inverter (RSDCHBMLI). RSDCHBMLI overcomes the problem of high power rating of the switches required for a two level inverter along with providing inherent isolation and a lower component count as compared to other popular multilevel inverter (MLI) topologies. The usage of single DC-bus capacitor in RSDCHBMLI eliminates the need of additional circuits or controls for voltage balancing among them, which is a major drawback of other MLI topologies. In this paper, the voltage control of the DC-bus capacitor is achieved using a current based controller which permits direct calculation of the PI controller gains. The DSTATCOM is operated using finite control set model predictive control (FCS-MPC) algorithm which ensures balanced and sinusoidal source currents along with maintaining a distortion free PCC voltages. The performance of the DSTATCOM is investigated under different conditions of linear and nonlinear loads, with balanced and unbalanced sources.
The Bifurcated Detection using the Sensing Coil for Parallel-Parallel Compensation in IPT System

The bifurcation phenomenon is the usually found in inductive power transfer system (IPT). The coupling and load on secondary side that induce the bifurcation cannot could not be directly measured. This paper presents the bifurcated detection by using the sensing coil. The sensing coil is placed on the secondary side. The phase of the sensing coil voltage is compared with the phase of secondary voltage to indicate the bifurcation. The frequency responses show that the phase difference between sensing coil voltage and secondary voltage is decreased when increased the secondary quality factor. The experimental results revealed that the bifurcation was detected from the secondary side in both perfect alignment and misalignment condition. By using the proposed circuit, the bifurcation detection from the secondary side is possible.
Covid-19 Salgınıyla Beklenen Değişimlere Blok Zinciri Teknolojisinin Etkileri

Covid-19 salgınıyaşadı ğımız yüzyılın en önemli küresel sağlık felaketlerinden ve insanlığın karşılaştığıen büyük zorluklardan biri olarak kabul edilmektedir. Covid-19 salgı nıkısa sürede dünyanın tamamına yayıldıve tüm ülkeler için sağlık, ekonomik, eğitim, ulaşım, çevresel ve sosyal zorluklar oluşturdu. Tüm ülkeler virüsün yayılmasınıengellemek amacıyla önlemler aldı. Virüs damlacık yoluyla bulaştığıiçin teması mümkün olduğunca azaltmak önemli hale geldi. Bu nedenle insanlar işlemlerini evden çıkmadan yürütmeye çalı şmışlardır. Teknolojik altyapıevden çıkmadan yürütülebilecek işlemlerin sınırlarını belirlemiştir. İnsanlar teknolojik imkanlar doğrultusunda çalışmalarını, alışverişlerini, görüşmelerini dijital olarak sağlayabilmişlerdir. Teknolojik altyapıbu süreçte ülkelerin salgını yönetmesinde ve insanların ihtiyaçlarınıkarşı layabilmesinde önemli rol oynamıştır. Ülkeler teknolojik kabiliyetleri sayesinde vatandaşlarının salgın döneminde dijital olarak işlem yapabilmesine olanak vermiştir. Covid-19 salgı nıdünyada birçok sektör ve iş modelini etkileyecektir. Yeni teknolojilerin hayatımıza girme sürecini hızlandırması beklenmektedir. Blok zinciri potansiyel olarak bu yeni teknolojilerden biridir. Blok zinciri teknolojisi Covid-19 salgınının etkisi ile yeniden tasarlanacak dünyada birçok sektörü dönüştürme potansiyeline sahiptir. Yapılan çalı şmada Covid-19 salgınıile dünyada yaşanacak değişimin etkisi ile geliştirilebilecek blok zinciri uygulamalarıve bu uygulamaların getireceği yeni iş modelleri incelenmiştir. Kripto para, dijital kimlik, tedarik zinciri yönetimi, sağlık kayıtlarının yönetimi, enerji dağı tımıve ticareti konularısalgının etkisi ile birlikte önemli hale gelecek muhtemel blok zinciri uygulamalarıdır. Bu uygulamaların geliştirilmesi ülkelerin dijital dönüşüm çalı şmalarınıtamamlamasına katkısağlayarak verimliliği artı racaktır. Bunun yanında teknolojik altyapının geliştirilmesi ile afet, salgın gibi ulusal güvenliği tehdit eden olağan dı şıdurumlarda ülkelerin süreci daha iyi yönetmesi sağlanabilecektir.
State of Charge Balancing of Distributed Batteries with Different Capacities in DC Microgrid

This paper proposes an adaptive droop control method to balance the state of charge (SoC) of distributed battery energy units (BEUs) with different capacities in DC microgrid. In this proposed method, the droop coefficient of BEU is adjusted by its SoC value as well as its capacity in order to regulate the BEU’s output power. From the modified droop coefficient, the virtual power rating is determined to implement the accurate power sharing among BEUs with balanced SoC despite of the different line resistance. In addition, a voltage restoration method is introduced to keep the DC voltage level within an allowable region. The proposed control method is implemented with the aid of communication network, and the performance is verified by simulation results.
Forecasting of Electric Peak Load Using ANN-Cascaded, ANN-NARX and GPR Techniques

Forecasting of Electric Peak load is very essential routine work for safe operation of power systems. In this paper, three different forecasting approaches are demonstrated. These include two artificial neural network based techniques of cascade and Nonlinear-Autoregressive-Exogenous (NARX). The third considered approach is Gaussian Process Regression (GPR). The three forecasting methods are applied on electric load, temperature and humidity date for Sharjah city over 3 years, i.e, 2014–2016. The date of the first two years were used to train the model; while the third year was used to evaluate its forecasting capability. The results showed that all the models provide almost similar forecasting performance. However, ANN-NARX has a degree of advantage over other methods owing to its account for the time dependency.
Deep Learning approach for the Multilink Selection Problem in Avionic Networks

In the Aeronautical Telecommunication Network, the aircraft has equipped with different physical radio stations and has the capabilities to use different Air/Ground data link for communications between the airborne segment and the ground segment. The COMET system considers a heterogenous set of radio access technologies. Each datalink has its transmission characteristics regarding the differences in data rate capacity, transmission delay, packet loss rate and the money cost. Besides, different Aeronautical Communication services applications have different QoS requirements, security requirements, for example, critical and non-critical safety communication services. In different phases of flight, various A/G datalinks could be available at the same time. In this paper, a multi-attribute decision-making model has been proposed to solve the multilink selection problem in ATN environment. The Deep Learning approach for Multilink Selection (DL-MS) has been applied to reduce the running time, and at the same time, still guarantees the accuracy of the optimal link selection solution.
Thermal and dielectric properties of two side chain liquid crystalline homopolymers and their corresponding equimolar copolymer and blend

The thermal and dielectric relaxation properties of two liquid crystalline homopolymers and their equimolar copolymer and physical blend are examined. The thermal properties of the copolymer are found to be a weighted mean of the homopolymers, whilst the blend was predominantly two-phase but showed some indications of miscibility. Dielectric relaxation measurements indicated that, in the liquid crystal state, the relaxation times of all homopolymers and copolymers were similar at any given temperature, indicating that it is the polymer backbone relaxation which dominates the motion of the coupled, polar mesogens. The blend relaxation appears to be a weighted average of the two components, indicating that dielectric relaxation can be used as a sensitive indicator of liquid crystal polymer blend miscibility.