Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation of Hairpin Winding in Electric Traction Motor Applications

This paper investigates the influence of number of layers and conductor size of hairpin windings of electric motors for electric vehicle applications. Firstly, the hairpin winding technology for electric motors is reviewed and key design rules are introduced. Based on these design rules, a traction motor with hairpin winding technology is modelled in a finite element analysis (FEA) software. The influence of the hairpin winding layers on the DC and the AC copper losses is investigated, based on the results obtained considering different operating points and drive cycles. The results indicate that using a higher number of winding layers does not always lead to higher efficiency. The trade-off between DC and AC losses under different operating points and drive cycles must be considered when designing for the optimal number of winding layers. Furthermore, using a larger conductor and higher fill factor for hairpin winding not necessarily lead to higher efficiency. In this study, the optimal conductor size for electric motors with hairpin windings is identified by carrying out optimizations considering drive cycle efficiency.
The Scientific and Technological Innovation Performance of Chinese World-Class Universities and its Influencing Factors

This paper explores the scientific & technological innovation performance of world-class universities in China from 2014 to 2019, based on the super-efficiency DEA model and Malmquist index. Then we provide a novel five-dimensional analysis framework to research influencing factors of their S&T innovation efficiency with the Tobit-DEA model. The results show that 36.6% of the sample universities are DEA efficient, and their efficiency varies according to their type and location. Besides, the technical efficiency index of universities is relatively high and the total factor productivity is volatile, which are reflected in the ability of transforming scientific research achievements into practical productivity. From the perspective of dynamic analysis, the sample universities expand slowly in the frontier of S&T innovation efficiency. In addition, the S&T innovation performance is highly related to the input quality and matching structure of scientific research elements, government relevance, and industry-academia-research collaboration level. Among these main factors, the high proportion of full-time teachers with senior titles, reasonable resource allocation structure and government support have significant positive impact, whereas the number of participants in international academic has negative impact.
A Hybrid Multi-Population Approach to the Project Portfolio Selection and Scheduling Problem for Future Force Design

Future Force Design (FFD) is a strategic planning activity that decides the programming of defence capability options. This is a complex problem faced by the Australian Department of Defence (DoD) and requires the simultaneous selection and scheduling of projects. Specifically, this is a NP-hard problem known as the Project Portfolio Selection and Scheduling Problem (PPSSP). While the PPSSP is a complex problem itself, its complexity is further increased when coupled with the additional characteristics that arise in the context of defence-oriented planning, such as long planning periods and complex operational constraints. As a result, many previous studies examined only a small number of projects over a short planning period and are largely unsuitable for the scale required in the defence sector. To address this issue, two primary contributions are made in this paper. Firstly, this study describes a complex practical PPSSP, inspired by the FFD process, and develops a corresponding mathematical model. Problem instances are derived from real-world, publicly-available defence data. Secondly, to address instances of the problem, two existing meta-heuristics are considered and a hybrid, multi-population approach is proposed. Results are compared against those attained by a commercial exact solver and indicate that there is no statistically significant difference in performance between the proposed multi-population approach and the exact solver. A key benefit of the proposed meta-heuristic approach is that its run time is not significantly influenced by the complexity of the problem instance. Additionally, many interesting practical insights regarding the solution of selection and scheduling problems are uncovered.
Comprehensive Study on Electrical Characteristics in 1.2 kV SiC SBD-integrated Trench and Planar MOSFETs

This study investigated the static and dynamic characteristics of a 1.2 kV SiC SBD-integrated trench MOSFET (SWITCH-MOS) and an SBD-integrated planar MOSFET. The SWITCH-MOS exhibited superior switching characteristics to the planar type MOSFET, while showing low specific on-resistance (Ron,sp). This is because the SWITCH-MOS has an SBD-wall trench structure and larger SBD area per unit cell than the planar type MOSFET, and the unipolar operation of the embedded SBD in the SWITCH-MOS can be maintained even at high current under high temperature. In addition, the SWITCH-MOS has smaller Crss owing to the p+ regions at trench gate bottom, resulting in higher dVds/dt during both turn-on and turn-off operations. Furthermore, the SWITCH-MOS showed superior Ron,sp - SCSOA trade-off characteristics.
Reconstruction, Identification and Classification of Brain Tumor Using Gan and Faster Regional-CNN

Brain tumor defines rapid development of abnormal cells in the brain. These tumors are capable of cause problems in the working of the brain as the space inside the skull is small and extra growth in a tiny area can lead to severe problems. There are two types of brain tumors: cancerous (malignant) and noncancerous (non-cancerous) (benign). Since malignant tumours may spread to other areas of the body, they are more harmful than benign tumours. These malignant tumors can be controlled from spreading to other parts of the body and possibly cured if its detection is done at an early stage. In the system we propose, we first identify, then find its exact location and finally classify the type of tumor inexpensively and quickly. Our proposed system uses DCGAN as a preprocessing technique in which generator creates fake images to fool the discriminator as it a real image to give a large data set less expensively. Hence the accuracy of preprocessed data in more compare to other preprocessing technique. These preprocessed data is trained using Faster R-CNN and classified into three types namely meningioma, glioma, pituitary and a plain type. In Faster R-CNN the region proposal time is 10ms which is very much lesser compared to other R-CNN technique.
An Extended Multilayer Thermal Model for Multichip IGBT Modules Considering Thermal Aging

An accurate and real-time knowledge of temperatures in insulated-gate bipolar transistor modules is crucial for reliability analysis and thermal management of power electronic converters. For this purpose, this paper establishes an integrated thermal equivalent circuit model comprising self-heating thermal impedances and cross-heating thermal impedances to provide a temperature profile of the junction and solder joints during various operations and in the case of thermal aging. The thermal resistance and capacitance parameters of the thermal impedances are characterized in terms of different electro-thermal operating conditions and solder joints aging conditions with the help of three-dimensional finite element simulations. Also, the effect of the heatsink, which brings an uneven heat transfer coefficient distribution at the module baseplate, is investigated and modeled into the thermal impedances. The introduced thermal model can work even if the conditions change simultaneously. The accuracy of the model is verified by experiments and finite element simulations, all of which agree with negligible error unlike thermal models given in the datasheet and fixed-parameter thermal models.
Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

This work proposes CAlECs, a clustered scheduling system for MPSoCs subject to thermal and energy constraints. It calculates off-line a cyclic executive honoring temporal and thermal constraints, for a hard real-time (HRT) task set at minimum frequency to reduce consumed energy, minimizing context switches and migrations. It also provides an on-line controller able to manage system and task parametric variations and soft real-time (SRT) tasks, always meeting the HRT task set constraints and the system thermal bound. CAlECS maximizes CPU utilization to help avoid overprovisioning contributing to a low SWaP factor. Its modular design allows the utilization of different modeling and scheduling approaches, and makes the off-line and on-line components independent from each other to better suit the requirements of a specific system. We experimentally show that the cyclic executive provided by CAlECS for HRT task sets outperforms RUN, a reference off-line algorithm in terms of optimal number of context switches.
Voltage Control in Distribution Networks with Soft Open Point and On-load Tap Changer

This paper investigates a novel coordinated voltage control strategy. This strategy considers a coordination between two voltage control devices: the soft open point (SOP) and the on-load tap changer (OLTC). A control frame based on distributed control manner was proposed, where each of the voltage control device acts as a control agent and operates based on both localized information and the information exchanged via a data acquisition platform. Under the proposed control frame, a priority-based coordination strategy was developed to achieve an optimal voltage management: no voltage compliance, reduced network energy losses and number of OLTC tap changes. A case study was conducted in an 11kV example network with different system operations and DG penetrations to testify the proposed control strategy.
Immersive Learning Aid for Children with Autism(ASD) using Object Recognition

Autism Spectrum Disorder is a chronic developmental disorder afflicting patients in their early formative years. Without a definitive cure, children can experience a spectrum of symptoms from difficulties with communication, social interaction, and obsessive/repetitive behaviour. These symptoms affect the children’s opportunities to experience and learn ideas and concepts, unlike their peers. With assisted learning and therapy, children with Autism can become independent members of society. The proposed system contributes to the child’s learning curriculum by offering education attractively and entertainingly. The premise of a smartphone or a tablet as the only hardware requirement breeds familiarity with the user and promotes comfort when utilizing the application. The system consists of an application that renders augmented reality objects onto the device screen when the camera module recognizes target objects in the frame. The application processes target information using the Vuforia Engine by scanning the images of the video stream for targets. The target is either an image or three-dimensional object stored locally in a database. The application can identify and process multiple targets to render a combination of text, image, audio, video, and three-dimensional objects. The application’s ease of usage combined with immersion provides a conducive environment for learning concepts and subjects of various fields.
Dermoscopy Cancer Detection and Classification using Geometric Feature based on Resource Constraints Device (Jetson Nano)

Skin cancer is actually considered one of the most harmful human types of cancer. It exists in many types, but melanoma is the most severe. Early detection of melanoma cancer is valuable for the treatment of patients. For this function, computer vision plays a major role in medical imaging for the diagnosis of cancer. Using Jetson Nano, we established an image processing method for skin cancer detection at the initial stage in the proposed work. The proposed research framework consists of five phases: a collection of dermoscopic images, grayscale conversion of an image, area of interest segmentation, and noise removal. Finally, the lesion features are extracted from the lesion and categorized into three groups with the aid of ABCD rules: benign, suspicious and malignant. On PH2 and ISIC data sets of skin lesion images, experiments are performed. Compared to those published in state of the art for skin cancer identification, the proposed method has shown better results.
Comparison of Short Circuit Robustness and Failure Mechanisms of GaN/SiC Cascode Devices and SiC Power MOSFETs

Comparative short circuit (SC) study is conducted on 650-V GaN/SiC cascode devices and mainstream SiC MOSFETs. The cascode devices deliver competitive SC capability and exhibit drain-to-source failure in the local areas without the source pad in the JFET, where the maximum temperature is located according to 3D thermal simulation results. The improvement of SC capability can be realized by adjusting the source pad layout. Gate-to-source breakdown occurs in the SiC MOSEFTs but is prevented in the JFET mainly by the slow heat conduction from SiC to the upper source pad.
Local Learning in Point Clouds based on Spectral Pooling

As one of the most fundamental geometric data types for the representation of space and object shapes, a point cloud usually maintains much structural information about the spatial relationship between objects and their features. However, the relative sparseness of point clouds sampled in most practical applications make extracting information-rich features a major challenge. Traditionally, feature extraction algorithms resorted to structured feature engineering and used handcrafted representations for some specific problems. Motivated by the development of deep neural networks, many researchers started to handle the unstructured point clouds from the raw data samples of 3D scanning devices. Some important advantages that deep learning frameworks have over traditional feature engineering is generalizing complex features and associated semantic concepts in a hierarchical manner. Deep learning models have achieved significant landmarks in cognitive processing of speech, image, and video signals. However, unlike in 2D image processing, a 3D point cloud is irregular and sparse. Hence, traditional network frameworks are difficult to apply on 3D geometric data directly. In this paper, we propose to integrate a local point convolution network with spectral pooling to aggregate and learn features in 3D point clouds. The benefits of our framework are fast convergence and competitive performance on point cloud classification.
Intrusion Detection Mechanism for Large Scale Networks using CNN-LSTM

In today’s world, Network and System Security are of paramount importance in the digital communication environment. To avoid breaches, it is badly needed for a security administrator to detect the intruder and prevent him from entering into the network. Machine Learning techniques are used to solve these types of problems, but they are not highly able to generalize as they fail to obtain relation among the features. Several works have also been done in Deep Learning using Artificial Neural Networks, Deep Neural Networks, RNN, etc. are not computationally efficient. This paper suggests a new machine learning model for intrusion detection that uses LSTMs and CNNs. This work uses CNN to choose feature characteristics from the input data, and send these features to LSTM for sequence analysis, and to address the imbalanced data set problem, Based on the total number of training examples in each class, each example will have its weight calculated based on cost function method. The raw input data format is transformed into a matrix format(image) to further decrease the computation cost. To test the efficiency of the CNN-LSTM model this work uses a conventional NSL-KDD dataset. The computation time has been reduced to ${\frac{1}{{10}}^{th}}$ the time that a fully connected layer took to train. The experimental results show that the model achieves an accuracy of 99.6% and a Detection rate of 96.75% while training.
1-Bit Precoding for Massive MIMO Downlink with Linear Programming and a Greedy Algorithm Extension

Employing 1-bit quantization in massive multiple-input-multiple-output (MIMO) systems is a promising approach to improve the power efficiency. Various linear and nonlinear precoding techniques including the maximum safety margin (MSM) precoder have been presented to cope with severe 1-bit quantization distortions. In this paper, we show the reason for MSM precoder’s success in reducing the quantization distortions and propose a low complexity extension with a greedy algorithm by exploiting this analytic result. Numerical results illustrate that the extended MSM is almost optimal in terms of maximizing the constructive interference (CI) between the received signals.
Energy Detection for M-QAM Signals

In this paper, we address energy detection for M-ary quadrature amplitude modulation (QAM) signals. In the literature deterministic signal model is widely used and detection probability is a function of signal energy. Unlike constant amplitude signals, the QAM signal is not deterministic since the energy in each QAM symbol can randomly vary. For random signals, model where both signal and noise are Gaussian has been widely used. However, this approximation may not provide accurate detection probability for QAM signals. Instead the detection probability should be averaged over the distribution of the energy. Previous work has considered calculating exact detection probability for given M analytically. However, the method presented previously has complexity that increases as a function of M and the number of samples.In this paper, we show that the distribution of observed energy for any M can be accurately approximated by one distribution which is derived analytically. Multiple numerical results showing probability density function, Kolmogorov-Smirnov distance, and detection probability are shown. Based on these results, a range where the proposed approximation is applicable is obtained.
Silica in Soils: Quartz and Disordered Silica Polymorphs

In nature, silica (Si02) occurs as seven distinct polymorphs: quartz, cristobalite, tridymite, coesite, stishovite, lechatelierite (silica glass), and opal; the latter two are amorphous. Of these minerals, quartz is most abundant in soil environments, while disordered cristobalite commonly occurs in soils. Tridymite is rare in soils and is usually associated with siliceous volcanic rocks. Coesite, stishovite, and lechatelierite are rare polymorphic forms produced at high pressures, but found under atmospheric temperature and pressure at sites of meteor impact. Opal is a hydrated "amorphous" silica that comprises the bulk of diatomaceous rocks of biogenic origin and silica of inorganic origin (Frondel, 1962; Deer et aI., 1978). This chapter emphasizes quartz and the disordered polymorphs of silica. The disordered polymorphs have been referred to by a number of conflicting and sometimes ambiguous names, often resulting in confusion. The definitions of the disordered silica polymorphs as proposed by J.B. Jones and Segnit (1971) appear to be more widely adopted and will be utilized, where possible, throughout this chapter. Each of these distinct and unique polymorphs of silica expresses a definite degree of structural order. The structural order defines the individual polymorph and results in unique physical and chemical properties which are briefly summarized below.
Permanent Magnet Vernier Generator Design for a Small-scale Passive Wind Generator System

In this paper it is proposed that permanent magnet vernier generators be used in a small-scale passive wind generator system. Various aspects such as the machine selection, design optimisation, torque quality and the overall feasibility of the proposal are investigated. The performance and power matching results that are obtained for the permanent magnet vernier generator with a magnetic gearing ratio of eight are very attractive for this application. Only the vernier generator with a magnetic gearing ratio of five is not suited for this application due to poor torque quality. Designs are confirmed by comparing 2D and 3D finite element results.
Non-Isothermal Crystallization Behaviors of Ethylene Vinyl Acetate Copolymer and Ethylene Vinyl Acetate Copolymer-Graft-Maleic Anhydride

Maleic anhydride (MAH) was melt-grafted on ethylene-vinyl acetate copolymer (EVA) with a grafting degree of 6.3%. Through studying the crystal structure of the resulting EVA-g-MAH, the MAH groups had little influence on crystal structure. According to the Owaza model analysis of the non-isothermal crystallization kinetics of EVA-g-MAH and EVA, the MAH groups had a large blocking effect on crystallization. However, the Mo model showed that the crystallization rate of EVA-g-MAH was slightly faster than EVA. The crystallization activation energy was calculated by the Takhor and Kissinger models, and both results indicated that EVA had a larger effective energy barrier than EVA-g-MAH.
DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning

Traditional Recommender Systems (RS) use central servers to collect user data, compute user profiles and train global recommendation models. Central computation of RS models has great results in performance because the models are trained using all the available information and the full user profiles. However, centralised RS require users to share their whole interaction history with the central server and in general are not scalable as the number of users and interactions increases. Central RSs also have a central point of attack with respect to user privacy, because all user profiles and interactions are stored centrally. In this work we propose DARES, an distributed recommender system algorithm that uses reinforcement learning and is based on the asynchronous advantage actor-critic model (A3C). DARES is developed combining the approaches of A3C and federated learning (FL) and allows users to keep their data locally on their own devices. The system architecture consists of (i) a local recommendation model trained locally on the user devices using their interaction and (ii) a global recommendation model that is trained on a central server using the model updates that are computed on the user devices. We evaluate the proposed algorithm using well-known datasets and we compare its performance against well-known state of the art algorithms. We show that although being distributed and asynchronous, it can achieve comparable and in many cases better performance than current state-of-the-art algorithms.
Surface Reinforcement Technology for Suppressing Hot-Carrier-Induced Degradations in p-GaN Gate Power HEMTs

Highly energetic "hot" carriers generated during both ON and semi-ON states have been identified as a predominant root cause of reliability issues in p-GaN gate power HEMTs. In this work, with plasma oxidation and high-temperature annealing, a surface reinforcement layer (SRL) is formed at the surface of both the p-GaN gate and the AlGaN barrier in the access region. At ON-state, the reinforced p-GaN surface exhibits enhanced immunity against hot-electron bombardment at the gate metal/p-GaN junction, and thus effectively prolongs the time-dependent-gate-breakdown (TDGB) lifetime. During the transient state of hard switching (i.e., the semi-ON state), the SRL formed at the AlGaN surface exhibits improved robustness under the stress of hot electrons generated from the channel. As a significant benefit, the dynamic ON-resistance degradation after long-term operation could be substantially suppressed.