Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast Prediction Approach of Radiated Emissions From Closely-Spaced Bent Cables in Motor Driving System

Three-phase cables in a motor driving system are the major ways of electromagnetic radiation, which result in potential interference to the nearby electronic equipment on electric vehicle. Numerical simulation of radiated emissions from cables is time-consuming, which becomes insufficient to compute large numbers of cables for actual applications. Hence, this article proposes a fast prediction approach that can highly improve the computational efficiency. Since cables are often close to each other and arbitrarily bent in the real application due to confined working space, the influence of proximity effects caused by closely-spaced cables on radiated emissions is also considered in the calculation model by multi-transmission line and Hertzian dipole theories. Besides, an equivalent model is presented to characterize the measured motor impedance. On the basis of the proposed cables model, a complete motor driving system model is originally developed by integrating a radiated electromagnetic interference (EMI) source model of inverter and a load equivalent model of motor impedance. The system model is of vital value to identify the mechanism concerning radiated EMI issues on electric vehicles and efficiently evaluated in the end. Both numerical simulations and experiments are conducted for the validation of proposed approach, which show that the proposed approach can save over 90% computational time with desired evaluation accuracy.
Application of patent BR102013018500-0 in well type ionization chambers

The definition of the radioactive sample position in a well type ionization chamber is the largest source of uncertainty in the measurement of quantity activity. The determination of this parameter in two activimeters helped to improve their accuracies, from 2.62 and 2.59% to 3.87 and 1.74%, with and without the use of the positioning device, concluding, that with their use has reached an uncertainty of U =2276 and 0.2677% (k = 2) 95.45%.
Dadu-SV: Accelerate Stereo Vision Processing on NPU

Binocular vision and neural networks (CNNs) are widely seen in modern intelligent vision processing systems, such as robotics, autonomous vehicles, and AR gadgets. However, both the classic semiglobal matching (SGM) and deep CNNs entail substantial computing resource to reach the performance goal. Traditional embedded CPU/graphic processor unit (GPU) cannot simultaneously meet the processing speed and energy requirement, while the specialized circuits dedicated to SGM and CNN processing, respectively, will take considerable hardware and development costs. However, as the popularity of deep learning, neural processing units (NPUs) become prevalent in many embedded and edge devices, which possess high throughput computing power to deal with the matrix operations involved by neural networks. In this work, we attempt to take advantage of the neural processing architectures integrated in SoC chips to accelerate the SGM process, so that the hardware resources will be better utilized instead of investing more resources to create specialized SGM components. Thereby, this letter first deploys SGM on NPU by converting the incompatible operations into the neural-computing flow, and a configurable neural processing element is proposed to flexibly support various vector operation sequences. Then, a hybrid dataflow scheduler and the corresponding hardware modification are introduced to accelerate the cost processing, improving hardware utilization and on-chip memory footprint and access. Our solution runs at 45 fps for an image size of $640\times 480$ , with 128 disparity levels. The speed-energy efficiency is $52\times $ better than the GPU (Jetson TX1) solution with negligible additional hardware overhead and accuracy loss.
Learnable filter-banks for CNN-based audio applications

We investigate the design of a convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications or applications involving time-series. The kernels are defined as one-dimensional functions having a band-pass filter shape, with a limited number of trainable parameters. Building on the literature on this topic, we confirm that networks having such an input layer can achieve state-of-the-art accuracy on several audio classification tasks. We explore the effect of different parameters on the network accuracy and learning ability. This approach reduces the number of weights to be trained and enables larger kernel sizes, an advantage for audio applications. Furthermore, the learned filters bring additional interpretability and a better understanding of the audio properties exploited by the network.
Unsacred Children: The Portrayal of Unaccompanied Immigrant Minors as Racialized Threats

Since the early twentieth century, children have been regarded as a protected class, legally and symbolically, in the United States. Although legal protections for U.S. children have also extended to non-citizen children, this study investigates whether the symbolic aspect of children’s protected status is undermined in the case of immigrant children. Through an examination of media reports during the 2014 entrance of unaccompanied minors from Central America and Mexico, I analyze how anti-immigrant protestors portrayed unaccompanied minors in quotes published in news articles across several online historical databases including ProQuest Historical Newspaper and the Local Historical Newspaper Archives by NewsBank. Based on this analysis, I find that the symbolic protected status normally attributed to children did not extend to unaccompanied minors. Specifically, anti-immigrant protestors mobilized forms of racialization typically employed against immigrant adults, which took four distinct forms that framed immigrant children as: (1) threats to the economy, (2) carriers of disease, (3) criminal and terrorist threats, and (4) invaders. Despite belonging to a vulnerable and normally protected group, immigrant children were racialized in ways well-established by scholars as characteristic of adult migrants by anti-immigrant protestors, who portrayed them as unsacred children unworthy of a protected status. This study contributes to our understandings of child and immigrant racialization and further contextualizes policies of detention and deportation against immigrant children.
Advanced braking algorithm for robust longitudinal risk management

This paper describes an advanced braking algorithm for robust longitudinal risk management to prevent or mitigate rear-end collisions. Since the proposed safety system works in conjunction with a human driver, the longitudinal safety system must be acceptable to the driver. The key to achieving this is to ensure that control signals are given at the appropriate time. In order to construct an suitable control system, a new longitudinal safety index was developed by using the time to collision and the warning index, which are well-known safety parameters in longitudinal safety control. The clearance and the relative velocity constitute essential signals for safety monitoring in longitudinal safety control. These measured sensor signals include the uncertainty in the measurement noise. To design a robust safety monitoring and control mode decision, information consisting of fusion of the measurement signals was obtained. In addition, an expected error range of the proposed safety index from the measurement noise can be defined from the covariance matrix of the Kalman filter and the deviation of the function of the new longitudinal safety index. The control performance of the proposed algorithm was evaluated by analysis of the simulation results. From this analysis, it can be concluded that the proposed advanced braking algorithm can substantially enhance the longitudinal safety and can guarantee a robust performance with respect to the sensor uncertainty.
Model Pembelajaran Problem Based Learning untuk Meningkatkan Hasil Belajar IPS Siswa SD

Proses pembelajaran hanya sekadar mendengarkan, mengerjakan tugas, dan hanya terfokus pada buku saja, sehingga pembelajaran di kelas sangat pasif. Hal tersebut menyebabkan kurangnya interaksi antara guru dan siswa, atau siswa dan siswa lainnya, sehingga pembelajaran menjadi tidak efektif. Hal tersebut berdampak pada hasil belajar siswa yang rendah. Penelitian ini bertujuan untuk menganalisis penggunaan model pembelajaran Problem Based Learning (PBL) dalam pembelajaran dapat meningkatkan hasil belajar IPS siswa SD. Penelitian menggunakan metode meta analisis. Pengumpulan data dilakukan dengan cara menelusuri jurnal elektronik melalui google scholar dan studi dokumentasi di perpustakaan. Sumber data penelitian dari 16 jurnal dan 4 skripsi mahasiswa. Analisis data dilakukan secara deskripsi kualitatif. Berdasarkan hasil analisis, diperoleh bahwa model pembelajaran Problem Based Learning mampu meningkatkan hasil belajar siswa dari yang terendah 8,9% mengalami peningkatan menjadi 83,3 % diperoleh rata-rata peningkatan sebesar 30%. Hal tersebut menunjukkan bahwa model pembelajaran Problem based learning efektif dalam meningkatkan hasil belajar IPS siswa SD. Implikasi penelitian ini diharapkan dapat membantu guru dalam memilih penerapan model pembelajaran yang tepat untuk meningkatkan hasil belajar siswa.
Optimization of the routing of capsule network based on multiscale information and self-attention mechanism

Abstract. Capsule network (CapsNet) is well known as an evolution of classical convolution neural network, which is good at recognizing postures, orientations, and textures, thus achieving promising results in areas such as image classification. However, the high computational complexity hinders them from building larger models. Fortunately, reducing the complexity of the routing of CapsNet through the channel attention mechanism has achieved significantly good results, whereas there is a possibility of further optimization of the storage and precision. Therefore, we propose an improved version of combination of multiscale routing and self-attention mechanism on the basis of the traditional channel attention routing. First, we absorb multiscale information into the routing process, thus enriching the representation capability of the capsule. Second, the global information of the capsule is captured by the self-attention routing, which leads to less misclassification errors. Finally, the proposed self-attention routing is further improved by a soft-threshold strategy, which resists the interference of background noises on complex datasets. Experimental results on MNIST, affNIST, and Cifar10 show that the proposed methods can obtain better performances with fewer training parameters compared with traditional methods on average.
An Ensembling Architecture Incorporating Machine Learning Models and Genetic Algorithm Optimization for Forex Trading

Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be updated manually to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes several State of the Art (SOTA) machine learning strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies for maximizing profits. The system also includes a money management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and subsequently evaluated on unseen samples from Jan 2020 to Dec 2020. The system performance is promising under ideal conditions. The ensemble model achieved about 10% nett P&L with −0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs & execution slippage in real market conditions. It is concluded that with the increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger.
Improvement on short-circuit ability of SiC super-junction MOSFET with partially widened pillar structure

In this article, a novel 1200 V SiC super-junction (SJ) MOSFET with a partially widened pillar structure is proposed and investigated by using the two-dimensional numerical simulation tool. Based on the SiC SJ MOSFET structure, a partially widened P-region is added at the SJ pillar region to improve the short-circuit (SC) ability. After investigating the position and doping concentration of the widened P-region, an optimal structure is determined. From the simulation results, the SC withstand times (SCWT) of the conventional trench MOSFET (CT-MOSFET), the SJ MOSFET, and the proposed structure at 800 V DC bus voltage are 15 μs, 17 μs, and 24 μs, respectively. The SCWT of the proposed structure increased by 60 % and 41.2 % in comparison with that of the other two structures. The main reason for the proposed structure with an enhanced SC capability is related to the effective suppression of saturation current at the high DC bias conditions by using a modulated P-pillar region. Meanwhile, a good Baliga’s FOM (BV 2/R on) also can be achieved in the proposed structure due to the advantage of the SJ structure. In addition, the fabrication technology of the proposed structure is compatible with the standard epitaxy growth method used in the SJ MOSFET. As a result, the SJ structure with this feasible optimization skill presents an effect on improving the SC reliability of the SiC SJ MOSFET without the degeneration of the Baliga’s FOM.
Transient Controller Design Based on Reinforcement Learning for a Turbofan Engine with Actuator Dynamics

To solve the problem of transient control design with uncertainties and degradation in the life cycle, a design method for a turbofan engine’s transient controller based on reinforcement learning is proposed. The method adopts an actor–critic framework and deep deterministic policy gradient (DDPG) algorithm with the ability to train an agent with continuous action policy for the continuous and violent turbofan engine state change. Combined with a symmetrical acceleration and deceleration transient control plan, a reward function with the aim of servo tracking is proposed. Simulations under different conditions were carried out with a controller designed via the proposed method. The simulation results show that during the acceleration process of the engine from idle to an intermediate state, the controlled variables have no overshoot, and the settling time does not exceed 3.8 s. During the deceleration process of the engine from an intermediate state to idle, the corrected speed of high-pressure rotor has no overshoot, the corrected-speed overshoot of the low-pressure rotor does not exceed 1.5%, and the settling time does not exceed 3.3 s. A system with the designed transient controller can maintain the performance when uncertainties and degradation are considered.
A supervised learning‐based approach for focused web crawling for IoMT using global co‐occurrence matrix

Irrelevant search results for a given topic end up wasting search engine users' time. A learning focused web crawler downloads relevant URLs for a given topic using machine‐learning algorithms. The dynamic nature of the web is a challenge in related computation for focused web crawlers. Studies have shown that the learning focused crawler utilizes term frequency‐inverse document frequency (TF‐IDF) to compute the relevance between a web page and a given topic. The TF‐IDF detects similarity of the given topic to its co‐occurrence on the web page. The necessity of efficient mechanism to compute the relevance of URLs syntactically and semantically has led to the proposal of this paper with a word embedding approach to compute the relevance of the web page. The global vector representation cosine similarity is calculated between a topic and the web page contents. The calculated cosine similarity is provided as input to the trained random forest classifier to predict the relevancy of the web page. The evaluation results proved that the proposed crawler produced an average hrate of 0.41 and prate of 0.59, which outperformed other learning‐focused crawlers on support vector machines, Naive Bayes and artificial neural networks.
Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision

To enhance the control technology of coal gangue dry separation method which is replaced by the machine in coal washing plant and to explore the control effects of traditional PID and dynamic domain fuzzy self-tuning PID, which will aid in determining the ideal position and orientation for grasping an object as well as understanding physical and logistic data patterns, an optimal design of PID controller for sorting robot based on deep learning is initiated. The mathematical model of ball screw system driven by a single joint motor of the robot is introduced, the control effects of classical PID and variable domain fuzzy self-tuning PID are studied and imitated, respectively. The simulation outcome appears that the selection time is 0.001 s and simulation time is 8 s. The tracking error of variable domain fuzzy PID is minor than that of PID tracking at the starting point, and the convergence rate of error is quick than that of PID manage, the steady-state error is minor than PID, the control accuracy is higher, and the tracking performance is better. The advantages of variable domain fuzzy PID control method in position tracking control are verified, the variable domain fuzzy PID can modify the control framework online as per the different position mistake and mistake change rate, the design of the variable domain of input and output makes the fuzzy inference rules locally finer, the speed of adjustment is faster and the tracking accuracy is further improved, so it has finer tracking presentation than the traditional PID tracking management.
The Role of Information Technology in the Effect of Innovation Capability on Logistics Service Quality

Purpose –In the current intense competitive environment, companies continuously need to increase their performance and adapt their activities to the developing and changing environment in order to survive. The concepts of quality, technology and innovation are among the most prominent issues in this competitive environment. At this point, information technologies integrated into the business, innovation activities and logistics services that develop along with these activities are important issues that have been recently emphasized. Accordingly, this study aims to reveal the role of information technology use in supply chain innovation and logistics service quality.Design/Methodology/Approach-More than 326 questionnaires were distributed to Turkey's leading logistics companies and companies with logistics departments. The companies operating in logistics sector and the employees working in the logistics departments of companies operating in the production and service sector constitute the sample of the study. 144 valid questionnaires were reached. 80% of the respondents are mid-level and 20% are senior managers. The data obtained by the survey method were analyzed using the structural equation model.Findings –According to the results, it has been determined that information technologies have a mediator effect between innovation capability in the supply chain and logistics service quality.Discussion–Findings obtained at the end of the research are consistent with the other studies in the literature. This research has important managerial implications. The results of this research have revealed the role of information technologies in the efficient use of innovation capacity for companies operating in logistics sector. Information technologies play a key role in the relationship between innovation and logistics service quality.
The Reality of English Presentation Skills of English-majored Students in Vietnam

Presentation skills play an indispensable role in undergraduates’ education and developing their future careers because this skill assists college students in accomplishing a superior knowledge base and enhancing their public speaking. The study aims to measure English-majored students' reality at the Faculty of Foreign Languages of Van Lang University in Vietnam about speaking skills, especially presentation skills. The quantitative study has been carried out among 600 second-year, third-year, and four-year students at the Faculty of Foreign Languages of Van Lang University in Vietnam. The results of the research paper demonstrated that most students were not confident about their presentation skills because of making mistakes about vocabulary usage, grammar, fluency, and so on when giving presentations. EFL lecturers can consider the results to improve their teaching methods regarding speaking skills and find out more strategies to solve students' speaking challenges.
Preventing Xenophobia in Africa: What Must the African Union Do?

The issue of xenophobia has, for many years, been one of the pressing challenges in Africa. While South Africa currently appears as a poster-child of this problem in Africa, the issue of xenophobia is not solely a South African problem. Traces of these attacks have long existed since the 1960s with countries such as Ghana and Nigeria raising agitations against each other and occasioning displacements of millions of people. Similar to the South African narrative, the agitations were borne out of a need to create an economic haven, the realisation of which was impeded by the influx of foreign populations. In recent years, the issue of xenophobia has equally resonated in the treatment of Somalis in Kenya following the attacks by the Somali-dominated insurgency group and in the treatment of Rwandan refugees in western districts of Uganda.
PROXIMITY COUPLED RECTANGULAR MICROSTRIP PATCH ANTENNA FOR S-BAND APPLICATIONS

Microstrip antennas have been one of the most featuring topic in antenna theory and design in recent years, and are progressively more finding application in a wide range of modern communication systems. In this study a microstrip antenna has been designed using proximity coupled feeding technique. The proposed antenna uses two layered substrate, such as the patch antenna on upper layer and the microstrip feedline on the lower layer. The study is made using the thickness of the substrate material. The experimental result shows that the Proximity coupled Rectangular Microstrip Antenna (PRMSA) gives an impedance bandwidth of 3.97% (110MHz) and 17.17% of gain. Also the other antenna parameters such as radiation pattern, reflection coefficient, VSWR and HPBW are calculated and discussed.
Correction to: A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes

1. Figures. The figure for the marginal posterior probabilities of inclusion for dietary covariates using the fecal microbial data in Koslovsky et al., 2020 (Figure 3) has been corrected and is labeled similarly for reference. The original figure displayed results for the oral microbiome analysis (incorrectly replicating Figure 2). Main results and conclusions in the manuscript remain unchanged.