Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction of Electric Field from Power Transmission Lines Using Phase Angle Adjustment Technique and Particle Swarm Optimization

This paper proposes a method to reduce the electric field intensity generated by the 69 / 230kV overhead power transmission lines of the Metropolitan Electricity Authority using the phase angle adjustment technique and Particle Swarm Optimization (PSO) method. In this paper, the three power transmission lines consisting one of 230 kV system and two of 69kV systems similar to the case of [8] are considered. To obtain even lower electric field, the phase arrangement technique presented in [8] is improved by the proposed method. From simulation results, it can be concluded that the generated electric field using the method presented in [8] can be reduced from 0.0378 kV/m to 0.00238 kV/m using the phase angle adjustment technique and PSO method.
E-Learning in Industrial Electronics during Covid-19

The educational methodologies employed in Industrial Electronics have been affected by Covid-19. In many cases, conventional learning methods relying on face-to-face lectures have been replaced by online methodologies. The whole process has required a fast adaptation and development of the e-learning tools to ensure a quality of theoretical, practical and laboratory lectures, as well as the development of new methods for the reliable assessment of the learning process. From this perspective, the present paper deals with the different strategies that have been implemented in institutions of several countries located in different geographical areas, including Portugal, Spain, Japan and Australia. It is shown that the use of methodologies, such as flip teaching, has provided a wide variety of possibilities to adapt to the new educational context. Moreover, for Industrial Electronics degrees, the use of virtual or remote laboratories, portable learning tools and advanced information and communication technologies have also risen as valuable resources. The paper also reports the problems arising during the development of the e-learning tools, their implementation constraints, and the evaluation of their results.
Imitation Learning With Additional Constraints on Motion Style Using Parametric Bias

Imitation learning is one of the methods for reproducing human demonstration adaptively in robots. So far, it has been found that generalization ability of the imitation learning enables the robots to perform tasks adaptably in untrained environments. However, motion styles such as motion trajectory and the amount of force applied depend largely on the dataset of human demonstration, and settle down to an average motion style. In this study, we propose a method that adds parametric bias to the conventional imitation learning network and can add constraints to the motion style. By experiments using PR2 and the musculoskeletal humanoid MusashiLarm, we show that it is possible to perform tasks by changing its motion style as intended with constraints on joint velocity, muscle length velocity, and muscle tension.
Apply VR and Simulation-Based Learning for Logistics and Warehouse Design Education

The current Logistics management courses here in Taiwan, are mainly divided into theoretical concepts, or system practice learning, but no matter what the existing model of the curriculum, most students’ ability is still out of the expectations for the employers of the logistics industries. Furthermore, the students’ learning effectiveness is not directly proportional to the course hours and their learning time, the major gap between the school and work area of Logistics management is still exist. Therefore, the theme of this study is to apply problem-based learning (PBL) and simulation-based learning (SBL) as the spirit of the design of Logistics management teaching plan and the compilation of teaching cases, and to evaluate the effectiveness of these courses design methods by the pre and post questionnaires, where the course were carried out with 45 students (into 10 groups) equipped with flexsim simulation software and VR headsets to design themselves-defined warehousing plan. The survey results show positive response from the students and provide insides for the teacher to keep on enhance the following course design.
An Automated Configuration Framework for TSN Networks

Designing and simulating large networks, based on the Time Sensitive Networking (TSN) standards, require complex and demanding configuration at the design and pre-simulation phases. Existing configuration and simulation frameworks support only manual configuration of TSN networks. This hampers the applicability of these frameworks to large-sized TSN networks, especially in complex industrial embedded system applications. This paper proposes a modular framework to automate offline scheduling in TSN networks to facilitate the design-time and pre-simulation automated network configurations as well as interpretation of the simulations. To demonstrate and evaluate the applicability of the proposed framework, a large TSN network is automatically configured and its performance is evaluated by measuring end-to-end delays of time-critical flows in a state-of-the-art simulation framework, namely NeSTiNg.
Extraction and Analysis of Micro-Doppler Signature in FMCW Radar

Micro-Doppler effect due to target micro-motions induce additional frequency components in the received radar signal. In addition to other target parameters, these micro-Doppler signatures can be used for target identification. For targets with high frequency micro-motion we observe that the conventional extraction of micro-Doppler signature in Frequency Modulated Continuous Wave (FMCW) radar fails due to insufficient sampling frequency along the slow-time. In this paper, we first develop a signal model for FMCW radar in the presence of a target with micro-motions. It can be inferred from the model that the micro-Doppler signature is preserved in the received signal. We then propose an approach to extract high frequency micro-Doppler signatures for FMCW radar, and verify it using simulation studies and experimental results.
Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction

During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.
Automatic Rubber Tree Classification

At present, the process of identifying the specie of a rubber tree is done manually and requires human experience and expertise. In Thailand, the Rubber Authority of Thailand (RAOT) has published a comprehensive guideline for this identification purpose so people can study from it. In this work, we aim to automate this identification task using image processing and machine learning technologies. We hope to alter the way RAOT conducts the survey to estimate the distribution of rubber tree species grown throughout the country. This undertaking has always been slow, labor-intensive, and in need of trained specialists. Our goal is to build a small footprint classifier so that non-experts can use to identify rubber tree species on the fields in realtime. The classifier is modeled after an Artificial Neural Network (ANN) and we have built a dataset for rubber leaf images to train it. Leaves are chosen to be the tree’s representative part as they are the most distinctive. Our approach to rubber leaf classification employs a mixture of tried-and-true techniques to maximize the accuracy while keeping the memory footprint small. As a result, with a memory budget of less than 100 Kbytes, our classifier identifies two species of rubber trees, RRIM600 and RRIT251, with an accuracy of about 83%. On average, it takes 3.42 seconds to identify each sample leaf.
Harmonic mean SINR maximization in a cognitive radar with communication spectrum sharing

We investigate the shared spectrum access between a communication system and a radar which inspects multiple range gates with non-homogeneous interference. The degrees of freedom for system design are the radar waveform, the radar receive filters at each range gate, and the communication codebook. Assuming some cognition of the environment, we propose to maximize the harmonic mean of the signal-to-interference-plus-noise ratio across the range gates inspected by the radar under semi-definite constraints on the communication mutual information, the transmitted power, and the similarity of the radar code to a desired reference signal. Numerical examples are provided to assess the merits of the proposed solution.
Using the PerFECt Framework to Invent Playful Learning Activities for Exploring the Binary System

This paper presents a learning community building framework and how it is being used to establish a learning community on the mathematical foundations of computer hardware, namely the binary system. The work builds on previous research efforts targeting gamification of mathematics and is seen under new light offered by the Theatre in Mathematics (TIM) Methodology. The result is a combination of a theatrical game and a board game to teach the binary system to children. The two games are closely related: The theatrical game uses the children's bodies to enact the mechanism of the board game by employing simple rules for interacting with each other. Following simple rules the participants embody the operation of a calculator that is able to transform integers into their binary representations and compute the result of the four arithmetic operations (addition, subtraction, multiplication and division). A simulator of the theatrical game is also offered so that learners can explore it for themselves and discover how the arithmetic operations are done, even when they are not in the same physical space with their peers. The board game is essentially a binary version of a counting table or abacus. It offers an alternative mechanism for understanding the validity of the rules of the theatrical game and makes these rules more concrete and accessible to the children. This way they can explore on an individual basis the way that the four arithmetic operations are performed. Both games offer new learning opportunities, when compared with previous work related to game-based teaching of the binary system. The games surveyed mainly focus on the binary representation of numbers without engaging students in active exploration of arithmetic operations in the binary system. Another important aspect of this work is its unplugged nature that enables students to engage their body in a playful inquiry of mathematical ideas. The knowledge developed can be transferred to other number systems and in particular to the decimal system, thus contributing to a deeper understanding of the mechanisms of number representations and arithmetic.
LQG digital state-feedback control of a Dual-Active-Bridge Series-Resonant Converter

Dual-active-bridge series-resonant converter (DABSRC) is popularly used in low- and medium-voltage applications that demand bidirectional power flow and galvanic isolation. From a discrete-time model of the converter, this paper explores the application of a linear-quadratic Gaussian (LQG) digital state-feedback controller to regulate the output voltage of the DABSRC. The proposed control includes a multivariable servo controller (regulator-integrator loops), a steady-state Kalman filter (SSKF) operating as a full state estimator to avoid increasing the number of sensors and an anti-windup algorithm to avoid the undesirable integrator windup. The SSKF also allows estimating the converter current and the anti-windup algorithm softens the converter start-up. The paper also proposes tuning criteria to facilitate the design of the controller. The results confirm the suitability of this kind of proposal for controlling the DABSRC.
An Industrial HMI Temporal Adaptation based on Operator-Machine Interaction Sequence Similarity

The incorporation of Artificial Intelligence (AI) into Industrial Environments has brought about a Smart Industry revolution, improving efficiency and simplifying complex industrial processes. However, these technological advances remain primarily focused on the process, and pay little attention to industrial Human-Machine Interfaces (HMI), the bridge between the operator and the industrial process.Current industrial HMIs have a static design, and are focused exclusively on the control and visualization of process information. They fail to take into account user behaviour and skills, information key to understanding how the operator interacts with the production process. Thus, the potential beneficial outcomes of considering operator-machine interaction in terms of efficiency and productivity, make a compelling case for industrial HMIs that can adapt to different operators based on their skills and process knowledge.This paper proposes a Machine Learning (ML) based method-ology capable of analysing operator-machine interaction and detecting the variability of interaction patterns for repetitive similar sequences in monitoring and control tasks. The method-ology generates a set of adaptation rules that improve Usability and User Experience, and hence operator working performance. To validate the proposed methodology, an experiment with real operators was conducted.
Statistical Inference for Periodic Self-Exciting Threshold Integer-Valued Autoregressive Processes

This paper considers the periodic self-exciting threshold integer-valued autoregressive processes under a weaker condition in which the second moment is finite instead of the innovation distribution being given. The basic statistical properties of the model are discussed, the quasi-likelihood inference of the parameters is investigated, and the asymptotic behaviors of the estimators are obtained. Threshold estimates based on quasi-likelihood and least squares methods are given. Simulation studies evidence that the quasi-likelihood methods perform well with realistic sample sizes and may be superior to least squares and maximum likelihood methods. The practical application of the processes is illustrated by a time series dataset concerning the monthly counts of claimants collecting short-term disability benefits from the Workers’ Compensation Board (WCB). In addition, the forecasting problem of this dataset is addressed.
A Capacity-Achieving Feedback Scheme of the Gaussian Multiple-Access Channel with Degraded Message Sets

The classical Schalkwijk–Kailath (SK) scheme for the point-to-point white Gaussian channel with noiseless feedback plays an important role in information theory due to the fact that it is capacity-achieving and the complexity of its encoding–decoding procedure is extremely low. In recent years, it has been shown that an extended SK feedback scheme also achieves the capacity region of the two-user Gaussian multiple-access channel with noiseless feedback (GMAC-NF), where two independent messages are, respectively, encoded by two intended transmitters. However, for the two-user GMAC-NF with degraded message sets (one common message for both users and one private message for an intended user), the capacity-achieving feedback scheme remains open. In this paper, we propose a novel two-step SK-type feedback scheme for the two-user GMAC-NF with degraded message sets and show that this scheme is capacity-achieving.
Benefits and Drawbacks of Adopting a Secure Programming Language: Rust as a Case Study

Programming languages such as Rust and Go were developed to combat common and potentially devastating memory-safety-related vulnerabilities. But adoption of new, more secure languages can be fraught and complex. To better understand the beneﬁts and challenges of adopting Rust in particular, we conducted semi-structured interviews with professional, primarily senior software developers who have worked with Rust on their teams or tried to introduce it (n = 16), and we deployed a survey to the Rust development community (n = 178). We asked participants about their personal experiences using Rust, as well as experiences using Rust at their companies. We ﬁnd a range of positive features, including good tooling and documentation, beneﬁts for the development lifecycle, and improvement of overall secure coding skills, as well as drawbacks including a steep learning curve, limited library support, and concerns about the ability to hire additional Rust developers in the future. Our results have implications for promoting the adoption of Rust speciﬁcally and secure programming languages and tools more generally.
Quality assessment models of information systems and projects: analysis and data based enhancing for GIS projects

Writing this paper supposed checking the availability of various definitions of quality of the information systems or projects on the basis of various factors included into the information project in this area that are divided into 3 categories: GIS operators, system performance specialists in the GIS field an GIS developers. Existed standards requirements to quality, software and information systems (IS) quality models are analyzed. Approaches and techniques to IS quality assessment are discussed considering specific application domains. In this study we examine whether the approach to the quality of systems and information projects in the field of GIS is the same as the quality approach of systems and information projects in other fields. As a result of a study, a model for quality assessment of the IS for geographic information systems is offered. This model and technique of the assessing is based on statistical processing of data collected in the course of search described above.
TEACHER EFFICACY AND ITS CORRELATES IN THE EFL CONTEXT OF IRAN: THE ROLE OF AGE, EXPERIENCE, AND GENDER

Teacher efficacy is a context and culture-specific construct. The current study explored the two types of teacher efficacy (individual and collective teacher efficacy) among Iranian English language instructors. Moreover, this study was an attempt to discover whether age, gender, and experience can significantly influence perceptions of collective and self-teacher self-efficacy. Data were collected from 130 English language instructors through Tschannen-Moran and Woolfolk Hoy's (2001) Teacher Sense of Efficacy Scale (TSES) and Goddard's (2002) Collective Teacher Efficacy Scale questionnaires. Multivariate Analysis of Variance (MANOVA) showed no significant difference between the English instructors' collective teacher efficacy and teacher self-efficacy and their respective subscales. Furthermore, the results indicated no meaningful differences in perception among teachers with different age, experience, and gender levels across all the subscales of collective teacher efficacy or teacher self-efficacy. We conclude that efficacy beliefs seem to be resistant to change once established and may not change significantly with age, experience and gender.
NDT_A_250648 1189..1197

Zheren Zhou* Xiaoyan Yin* Qiuwen Niu Simin Liang 2,4 Chunying Mu Yurong Zhang 2 1University Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Department of Neurology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 3Department of Neurology, Wuqi People’s Hospital, Yan’an, Shaanxi, People’s Republic of China; 4Department of Neurology, The First Affiliated Hospital of Xi’an Medical College, Xi’an, Shaanxi, People’s Republic of China
Transtheoretical model on the self-care behavior of hypertension patients: a systematic review

This review aims to describe the effectiveness of education with the transtheoretical model (TTM)-based on the self-care behavior of hypertension patients. ,A systematic literature search was carried out on four databases: PubMed, Science Direct, Cochrane and Grey literature to identify studies reported in English which were published in the last ten years. The literature search was conducted from November 13 to December 10, 2020. ,Based on the six studies that have been analyzed, TTM is effective in changing the stage of change and behavior of hypertension patients. These behavioral changes have an impact on the patient's controlled blood pressure. Various types of TTM-based educational interventions can be used, but the tailored behavior intervention is the most appropriate one with a minimum intervention duration of six months. Educational intervention is carried out through combine counseling and education using electronic media. ,This review presents the effectiveness of transtheoretical-based health education in changing the self-care behavior of hypertension patients accompanied by evidence-based on its implementation. There is a high and unclear risk of bias on several items influence this systematic outcome. Nevertheless, this review can still provide an overview of the effectiveness of education based on the TTM in hypertension patients about the quality results of the reviewed studies.
Towards Correctly Checking for Cycles in Overloaded Deﬁnitions

Safe extension of a logic with deﬁnitional axioms need acyclic deﬁ-nitions, because cycles in deﬁnitions possibly entail contradiction. In this report we give a mechanised exact characterisation of acyclic over-loading deﬁnitions. Our results support the soundness argument of the cyclicity checker of the Isabelle/HOL theorem prover, and serve as the theoretical foundation for future veriﬁcation of such a cyclicity checker
Effect of Processing Parameters on Aeration of Reduced Hatinh Ilmenite

Aeration step, one of the major stages in the Becher process was carried out on reduced Hatinh (Vietnam) ilmenite in NH4Cl solution and the effect of several processing parameters were thoroughly investigated including the temperature, time, liquid/solid weight ratio (L/S), air flow rate and concentration of NH4Cl solution. The obtained results showed that longer rinsing time, higher liquid/solid ratio and air flow rate facilitated metallic iron rusting. The rusted iron amount increased when the temperature increased and reached the highest value at 70°C. Variation of NH4Cl concentration showed similar impact as that of temperature. The highest amount of rusted iron achieved at 0.5% NH4Cl. Chemical analysis and XRD results indicated that 98% of metallic iron in reduced ilmenite has been transformed to iron oxides at an aeration condition (70°C, 8 h, L/S=7/1, air flow rate = 4 l/min and 0.5% NH4Cl). Consequently, TiO2 content from approximately 60% in the reduced ilmenite increased up to approximately 80% in the aerated rutile.