Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical magnetic field distribution of slotless brushless permanent magnet motors – Part 1. Armature reaction field, inductance and rotor eddy current loss calculations

Two-dimensional analytical armature field calculations are presented for slotless permanent magnet brushless motors. The proposed method is applicable to both brushless AC and DC motors with any number of phases. The problem is formulated for eight annular regions: shaft, rotor back-iron, magnets, retaining sleeve, airspace, winding, stator back-iron and exterior. A general armature current density distribution is expressed as a function of spatial angle and time for any number of phases, any current waveform and any winding distribution. The governing partial differential equations of all the regions subject to a set of boundary conditions are represented and solved analytically for both internal and external rotor motors. Self- and mutual-inductance calculations as well as expressions for the calculation of rotor eddy current losses in the magnets, retaining sleeve and rotor back-iron are also represented. In the second part of these two papers, the open-circuit magnetic field is presented for different magnetisation patterns and subsequently the electromagnetic torque is calculated. The effectiveness of the analytical model is validated by comparing the results with those obtained from finite-element analyses as well as an experimental set-up which presented in the second part of the series.
Parametric Comparative Analysis between Virtual Synchronous Generator and Droop-based Inertia for Inverter-Based Microgrids

This paper presents a parametric comparative analysis between the virtual synchronous generator (VSG) method and the droop control method to emulate inertia in the voltage-source inverter (VSI). Droop controllers are commonly used to regulate sharing power in microgrids and distribute power generation proportionally among VSI’s depending on their rated power. Additionally, VSG has been used to regulate the Rate-of-Change-of-Frequency (RoCoF) of the microgrid using virtual inertia. Although both methods can be used to regulate the frequency variation, the influence of each method on the closed-loop eigenvalues is not the same. In this work, the transient response of the frequency is analyzed for each method to determine their advantages and disadvantages regarding frequency regulation in microgrid applications. The results were verified by conducting experimental trials using VSI’s. These experiments demonstrated that VSG is more suitable for regulating RoCoF and frequency nadir than droop controllers since it provides inertial support and improves frequency response.
Study of brain activity during total sleep deprivation.*

Aim: To study the effect of total sleep deprivation on brain activity using EEG. Methodology: In this paper, the EEG of sixteen subjects (Mean = 21.5; SD = ±2.5) is recorded just after they woke up from sleep. Then, they are sleep deprived for the next twenty-four hours, and the EEG is recorded after sleep deprivation. The raw EEG data is pre-processed. The source localization is performed by a recently developed method- CARSS and sLORETA.Results: (1) The prominence of the right lobe increased from pre to post. (2) The increase in frontal source activation powers and a decrease in parietal is observed from the pre to the post (3) The δ, θ, and α powers increased from pre to post in pre-frontal leads. The left frontal lead showed an increase in α, but a decrease in the right frontal lead is observed. Conclusions: The changes in the source activations and the powers show that there is a change in the way in resting-state neural networks post sleep deprivation.
Optimization of Hydro Turbine Governor in a Stand-Alone Hydro Plant using PID Control

The expensive and limited fossil energy is the reason to develop renewable energy, which is the right solution to fix the environmental problems and support the national development of Indonesia. The Department of Electrical Engineering of USU supports the government and sees the potential sources of power around the campus, which is performed by developing a mini-hydro generator by using water as the source of power that comes from a tower on campus. The design of the mini-hydro generator that will be implemented in the Electrical Engineering department was made at the di Simulink Matlab R2018a. There is a speed control using PID control in HTG in the system during disturbances, namely the loading changes (loading variations). The PID control value used is the result of optimization using GA, and by controlling the speed in HTG, an IEEE standard frequency is obtained, which is ±1% (50 Hz).
Parameter Estimation of Batteries in MMCs with Parallel Connectivity using PSO

Modular multilevel converters (MMCs) are a well-known solution in high-voltage applications. Recently, new topologies such as MMCs with series/parallel (MMSPC) connectivity are attracting large amount focus, due to their improved efficiency as well as self-balancing capability. Although MMSPCs can operate without direct measurement of modules’ voltages in battery-integrated modules, monitoring of the voltage and resistance of each module can still provide useful information to assess the state of each battery. However, including a voltage and current sensor in each module is not a cost-effective approach. This paper proposes a method to estimate the voltage and resistance of each battery-module using only the voltage and current measurement at load terminal. The proposed technique can reduce the number of sensors from 2N + 2 to only 2. Additionally, due to the self-balancing capability, the estimation technique does not require fast convergence and can operate in the background during the idling intervals of the processor. MATLAB simulations confirm the applicability of the proposed approach. The result show a 99 % and also 96 % accuracy for balanced and imbalanced systems.
PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration

Graph Convolutional Network (GCN) is a promising but computing- and memory-intensive learning model. Processing-in-memory (PIM) architecture based on the ReRAM crossbar is a natural fit for GCN inference. It can reduce the data movements and compute the vector-matrix multiplication (VMM) in analog. However, it requires an unbearable crossbar cost to leverage the massive parallelism exhibited in GCNs. This paper explores the design space for GCN acceleration on ReRAM crossbars and presents the first PIM-based GCN accelerator named PIMGCN. PIMGCN employs dense data mapping and a search-execute architecture to take full advantage of the intra-vertex parallelisms with acceptable crossbars cost. We further propose two scheduling strategies for PIMGCN to maximize the inter-vertex parallelisms and optimize the pipeline. The optimal scheduling is reduced to a maximum independent set problem, which is solved by a novel node-grouping algorithm. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA RTX8000 GPU, PIMGCN achieves on average 11044× and 74.3× speedup, 6.13E+06× and 5.09E+03× energy reduction, respectively. Compared with ASIC accelerator HyGCN [1], PIMGCN achieves 219× speedup and 95.3× energy reduction.
Competency-based education in an electronic-supported environment: an example from a distance teaching university

Competency-based instruction can be applied to various settings, including an academic programme with a focus on producing performance-based learning outcomes. This paper provides theoretical and practical information about underlying characteristics of competencies and introduces a Master's programme, which has recently been started at a distance teaching university. It is aimed at fostering competencies needed in a knowledge-based society and prepares learners to respond to increasing demands of the working life. Its focus is on technology-enhanced settings such as e-learning, e-teaching or blended learning. A detailed description of the introductory module will be given to illustrate how theoretical and didactical approaches can be combined in a practical way.
A Utility Operation Model Design and Analysis of a Massage Robot

A utility operation massage robot structure is proposed in this paper, which has the characteristics of flexibility and versatility compared with other existing massage robots. Three humanoid massage types are achieved by the proposed robot structure, which include physical beating mode, physical pressing mode and physical rubbing mode. Its performance is analyzed by discussing its working space and movement status. The validation of the proposed massage robot has been performed by the results of the simulation experiment.
Trapezoidal operation of modular multilevel DC-DC converter for HVDC interconnections

High Voltage Direct Current (HVDC) grids are becoming a preferred option for the expansion of the traditional electrical networks as a result of their potential economic, technical and environmental benefits. Developing HVDC grids includes the linking of existing HVDC buses which may be implemented with different voltage levels, different HVDC technology and various grounding schemes. Interconnecting these HVDC systems will require DC-DC converters to provide voltage matching, isolation and a means for splitting the HVDC grid into sub systems. This paper presents a modified trapezoidal operation of MMC-DC-DC converter, MTrMMC-DC-DC, with high semiconductor device utilization and lower device current rating in addition to an increase of converter’s energy density. Better utilization of the semiconductor devices results from conducting the current through the arms of the TrMMC-DC-DC converter continuously. The reduction of the energy storage requirements is achieved through careful design of the submodule (SM) capacitors and arm inductors to achieve resonance between the effective equivalent capacitor of each arm and the arm inductor at a frequency below twice the fundamental operating frequency. The circuit parameter design is validated for an 800MW, 400kV HVDC-based system through PLECS simulations.
Identification of Rice Leaf Disease Using Convolutional Neural Network Based on Android Mobile Platform

Rice is a rice-producing plant that is susceptible to disease so it can make it difficult for farmers to identify the types of diseases that exist in rice leaves. On the other hand, farmers need convenience in identifying diseases that exist in rice leaves more effectively and efficiently. Seeing the development trend of deep learning and mobile android, we need an application that can help farmers to analyze diseases in leaves effectively and efficiently. This research was conducted in several stages including literature study, application design and manufacture, application testing and analysis as well as conclusion drawing and report writing. With deep learning technology, a Convolutional Neural Network (CNN) model was developed on Tensorflow lite and stored in the ML Kit service. Furthermore, the model can be embedded in a detection application built on the android mobile platform. This is to assist farmers in identifying healthy and unhealthy rice leaves. The results of the development of the algorithm and its application to an Android-based mobile application can run well where the level of accuracy generated from the model formed in classifying disease images on rice leaves in this study is 80%.
Automatic Flux Linkage and Inductance measurement for a Variable Flux Machine at Different Magnetization States

This paper presents the current controller-based parameter measurement of a variable flux permanent magnet synchronous machine (VFM) for different magnetization states (MS). A series of pulse currents are supplied to the VFM in a closed-loop system. The machine voltage and current response are measured and are processed in real-time to calculate the flux linkage, inductance and resistance. Thus, automated machine parameters are generated in real-time by the real-time processor. Since the same closed-loop system is used to change the machine MS to the desired level, the machine parameters for different MS can be automatically calculated. Experimental validation is performed on a 5 hp VFM. The experimental results are compared with the finite-element analysis results and show a good correlation.
A Survey of Real-Time Industrial Wireless LAN Research

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.
Selective Sampling Based Angular Domain Cyclostationary Feature Detection of ODFM Signals

This paper proposes a novel approach to simplify and improve the Angular Domain Cyclostationary Feature Detection (ADCFD) of OFDM signals for cognitive radio wireless network. The originality is to perform the ADCFD by a selection of CP (Cyclic Prefix) samples to check the non-uniformity of the signal phase when the OFDM signal is present in the spectrum band. The simulation results show that the proposed method improves the performance by increasing the sensitivity of the detector with a gain of about 5 dB in an Additive White Gaussian Noise (AWGN) channel compared to the other methods in the literature. The proposed method reaches the maximum detection probability (100%) from a Signal to Noise Ratio (SNR) equal to -16 dB.
Design and Hardware Implementation of a H-Bridge Sub-module for Single-Phase 5-Level Cascaded Voltage Source Inverters

This paper presents the design and the hardware implementation of an IGBT-based H-bridge sub-module for cascaded voltage source inverters (VSIs). The design methodology and the utilized techniques for reducing stray inductances on the printed circuit board are explained. In addition, the way to calculate and to select the electronic devices are exposed, with special emphasis on the IGBT and its driver. For the design of the circuit board, local regulations for grid interconnection and international standards have been considered to obtain a safe and reliable electronic power cell. The developed hardware has been subjected to different tests by using a single-phase AC electric motor as loads. Two VSIs topologies are evaluated: single-phase 2-level H-bridge inverter and the single phase 5-level cascaded inverter. Experimental tests validate the design and the manufactured hardware and demonstrate an excellent performance, reliability, and high efficiency.
Pack-Level Electrochemical Impedance Spectroscopy in EV Batteries Enabled by a DC Fast Charger

In order to reliably operate Lithium-ion battery systems at their maximum power capability, it is advantageous to track the battery electrical impedance. Direct-Current Fast Charging (DCFC) stations provide a low-cost opportunity to introduce in-situ impedance measurements through the shared use of new hardware across many users. The feasibility of DCFC-based EV battery pack impedance measurement is explored in this work. To the authors’ knowledge, this is the first published measurement and analysis of impedance data from a commercial EV dc charging plug. A custom 50 kW CHAdeMO-interface DCFC is implemented, featuring electrochemical impedance spectroscopy with 5 A perturbation current at 400V, up to 3.1 kHz. The high-voltage system impedance is measured for a prototype EV featuring a custom 27kWh battery pack, and a 2015 Nissan Leaf EV through the CHAdeMO port. It is found that while the vehicle-level impedance is significantly affected by the DCFC charging cable and internal vehicle components, battery impedance variation due to temperature can still be detected. This work motivates the future development of charger-side battery diagnostics, including 1) large-scale vehicle fleet battery health monitoring, 2) data collection for improved battery degradation modeling, and 3) direct information exchange with EVs for battery performance optimisation.
A tunable integration method for the simulation of power system dynamics

Accurate simulation of power-systems dynamics over an extended time period results in a model that is relatively stiff; the solution of such models requires a numerical integration method that is not only fast and accurate, but also capable of using long time steps. An integration method which can be numerically tuned to suit the system of equations is developed and is shown to be superior to the trapezoidal rule when very long time steps are used. While the fixed-tuning methods are easy to implement, they are not recommended since they resulted in no significant improvements. Adaptively tuned techniques performed better and some worthwhile gains were obtained with long time steps. >
Performance Analysis of Smart Intravenous Infusion Systems using Machine Learning

The Artificial Intelligence (AI) is one of the emerging eras of at present. It supports big data analysis and the classification of them in fruitful way. Use of AI in real time data analytics provides optimized performance. Especially in the medical field, the AI plays important role. This research article focuses the kind of medical problem. The Intravenous Infusion System (IVF) has impressive role in transferring the medicine, saline and other liquids to the patients during hospitalization. The frequency of changing the IVF is taken care at most. So our proposed work discuss about designing the automated IVF system which has the three sections such as control, communicate and observe unit. This works on automated mode so whenever the saline low is below the predicted level the dripper unit triggers and adjusts the flow of saline with the patient's body temperature and other parameters variations. At the observer unit the clouds are connected. The monitoring data's of IVF changes are considered as base data set to train the machine learning models. This research also compares the performance matrices of the different machine learning methods such as accuracy, recall,F1 and precision. Here the SVM, NN, kNN & RF methods are compared where the RF has the better accuracy of 90.2%, SVM has 69.6 %, k NN has 69.5 % and the NN of general model has 92.5% for the obtained data set from the IVF device.
Meta-WF: Meta-Learning-Based Few-Shot Wireless Impersonation Detection for Wi-Fi Networks

As the common wireless open medium of the Internet of Things (IoT), Wi-Fi networks are often suffering from impersonation attacks. For the purpose of Wi-Fi impersonation attack detection (WIAD), recently, deep learning-based techniques provide superior detection performance. However, considering the dynamics of the WIAD task, these methods require retraining deep learning network (DNN) through large-scale samples and long training time to adapt to new WIAD tasks. In this letter, we propose a meta-learning-based Wi-Fi impersonation detection (Meta-WF) algorithm, which learns knowledge from historical multiple WIAD scenarios and quickly adapts to new WIAD scenarios with few-shot samples. Numerical results show that the proposed algorithm is capable of achieving 98% accuracy for new WIAD tasks by 1-step fine-tune using less than 0.1% (50-shot) of the training set samples.
Real-Time Facial Emotion Recognition

An important aspect of human beings is their ability to display emotions. Emotions form the basis of intimate means of communications between one another. Recognition of emotions displayed via facial expression with the help of a computer could prove to be a very powerful too. In recent years, an increased number of intelligent systems are using facial emotion recognition to improve human interaction. These systems cause constant changes in their operation based on the emotion of humans. In this paper, we propose an architecture based on the convolutional neural network (CNN) for the facial recognition of emotions. In the implementation, we use the Facial Expression Recognition 2013 dataset (FER2013). Through behavioral analysis, we show how different emotions seem to be sensitive to different parts of the face.
An empirical study of process discipline and software quality

There is a widespread, but not universal, belief in the software community that software organizations and projects can systematically improve their ability to meet commitments and build high-quality products using principles of software quality management. Quality affects cost and schedule, therefore the engineering practices that affect quality are also a management concern. Understanding the factors that influence software quality is crucial to the continuing maturation of the software industry; an improved understanding of software quality drivers will help software engineers and managers make more informed decisions in controlling and improving the software process. My research is motivated by a desire to understand the effect of disciplined processes and effective teams on improving performance and lessening variability with respect to software quality. Classroom data provides insight into interpersonal differences between competent professionals as increasingly disciplined processes are adopted. Project data using similar processes enables an exploration of the impact of effective teams on software quality. My results show that: (1) Program size, programmer ability, and disciplined processes significantly affect software quality. (2) Factors frequently used as surrogates for programmer ability, e.g., years of experience, and technology, e.g., programming language , do not significantly impact software quality. (3) Recommended practices are not necessarily followed even when processes are consistently performed, e.g., peer reviews may be consistently performed, but the review rates may exceed recommended practice for effective reviews. (4) When moving from ad hoc processes to disciplined processes, top-quartile performers improve more than 2X; bottom-quartile performers improve more than 4X. (5) Rigorous statistical techniques that allow for individual differences confirm the importance of process discipline and following recommended practice for improving software quality.
DC-Side Impedance Interaction Analysis in an MMC-Based Back-to-Back VSC-HVDC System

In this paper, harmonic stability of a back-to-back modular multilevel converter based voltage source converter high voltage direct current system is analyzed. These systems are prone to ac- and dc-side resonances and interactions between the converter and its interconnected systems. The stability of the system can be predicted by the impedance based stability criterion, which requires the modeled or measured converter impedance. The analysis in this paper focuses on interoperability when the converters employ different control schemes in order to utilize the inherent damping properties of the sum capacitor voltage balancing control loops.