Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Free Predictive Current Control of a Voltage Source Inverter

Conventional model predictive control (MPC) of power converter has been widely applied to power inverters achieving high performance, fast dynamic response, and accurate transient control of power converter. However, the MPC strategy is highly reliant on the accuracy of the inverter model used for the controlled system. Consequently, a parameter or model mismatch between the plant and the controller leads to a sub-optimal performance of MPC. In this paper, a new strategy called model-free predictive control (MF-PC) is proposed to improve such problems. The presented approach is based on a recursive least squares algorithm to identify the parameters of an auto-regressive with exogenous input (ARX) model. The proposed method provides an accurate prediction of the controlled variables without requiring detailed knowledge of the physical system. This new approach and is realized by employing a novel state space identification algorithm into the predictive control structure. The performance of the proposed model-free predictive control method is compared with conventional MPC. The simulation and experimental results show that the proposed method is totally robust against parameters and model changes compared with the conventional model based solutions.
System Design for Automation in Multi-Agent-Based Manufacturing Systems

This paper proposes a system design for automation in multi-agent-based manufacturing systems to conduct a given complex task automatically by controlling multiple robotic manipulators in a systematic manner. To this end, the proposed system is designed with three-module configurations: environmental perception, task planning, and motion planning. The environmental perception module utilizes a vision sensor to recognize all objects placed on the workspace and extract their unique ID, size, and pose. The task planning module divides a given task into primitive skill levels and distributes each primitive skill to the associated robotic manipulator with the relevant object information in a systematic manner for robotic manipulators not to collide with each other. The motion planning module determines the motion of a robotic arm and a robotic hand by solving inverse kinematics for the robotic arm and by opening or closing two fingers. The proposed system has been tested and verified in real robot environments through a complex task "peg in hole" that requires at least two robotic manipulators.
Intelligent Analysis of Technical Support Requests in Service Desk Ticketing Systems

Natural language processing methods are actively being used in systems that interact with people. These systems include voice assistants, spam filters, Service Desk ticket processing systems and many others. This article studies the problem of intelligent automatization of request processing. Requests are coming from users and describe a problem in wireless network equipment. The subsystem described in the article is integrated via specifically developed API to the Atlassian Jira technical support system to access user requests. Requests in technical support system contain information about a problem in the wireless network that the user encountered. The main goal of the subsystem is to analyze the input message, which is written in natural language. Methods of intelligent natural language text processing extract information about a network problem that can help locate a specific device and determine the cause of the problem, which can then be fixed by standard actions. A word embedding approach called word2vec is used for text analysis. The article describes the work process of convolutional neural networks and their integration with natural language processing methods for obtaining needed results, key modules that make up the subsystem, in particular, the message processing subsystem and the actualizer system and the relationship between them. Examples of input message text analysis and results obtained by the subsystem are shown.
Single-Phase LED Driver With Reduced Power Processing and Power Decoupling

Reduced power processing is a new trend to improve the conversion efficiency of light-emitting diode (LED) driver systems. Based on the concept, a new LED driver architecture is proposed in this article, which requires fewer power semiconductors compared with prior works, leading to the potential of lower cost and higher efficiency. A new ac/dc LED driver topology is derived accordingly based on the architecture to achieve the constant output current and unity power factor with significant energy storage reduction of the 100-/120-Hz energy buffer. Through a split and shared inductor, the required magnetic energy storage in the derived topology is the same as the conventional single-stage power factor correction converter, indicating high power density. Experimental evaluation verifies the performance in terms of improved power quality and conversion efficiency.
Mixture of Decimal and Binary Arithmetic Units For FPGA Based Architecture

Minimization of rounding error can be done by simply using decimal arithmetic over binary arithmetic systems. This method eliminates the conversion technique which leads to good result. The decimal arithmetic hardware uses a typical binary arithmetic circuit along with a conversion unit. In this proposed architecture BCD input values are divided into many groups of bits to find out the contribution of individual group. The outcome of individual group is to be added to obtain the final output. The paper studies the design of 8 bit grouping mechanism method. The proposed method is simulated using ISim simulator and synthesized using 4vsx35ff668-12 FPGA device. The Delay and device utilization are calculated from the synthesis result and are compared to the earlier conversion technique.
A Defect Detection Model for Imbalanced Wafer Image Data Using CAE and Xception

The development of technology in modern society causes consumers to create new demands. And consumers' demands lead to improved product quality. In particular, as the mobile era enters, the development of semiconductor technology is essential for electronic products. In electronics, semiconductors are used as various precision parts and control the performance of products. Therefore, improving the yield of semiconductors is the most laborious task for semiconductor companies. In the semiconductor manufacturing industry, semiconductor wafer defects are a major problem causing large losses. In semiconductor manufacturing, which includes many processes, wafer defects cause various variations, resulting in great losses. Accurately identifying and classifying defects would bring great benefits to the semiconductor manufacturing industry. Wafer defect inspection is being conducted passively by experts. Wasting such passive and human resources can be prevented through machine learning. In this paper, a deep learning-based model using Xception is proposed to proceed wafer defect detection and classification. Xception has a total of 36 convolution layers and consists largely of three flows. In addition, to solve the imbalance problem of the dataset, data augmentation was performed using the convolutional autoencoder. Through the proposed method, it was possible to improve the detection and classification of wafer defects while solving the problem of data imbalance.
SoC compatible 1 T1 C FeRAM memory array based on ferroelectric Hf0.5Zr0.5O2

This paper experimentally demonstrates fundamental memory array operation of a ferroelectric HfO2-based 1 T1 C FeRAM. Metal/ferroelectric/metal (MFM) capacitors consisting of a TiN/ $\mathrm{Hf}_{0.5}\mathrm{Zr}_{0.5}\mathrm{O}_{2}(\mathrm{HZO}$)/TiN stack were optimized for a sub 500°C process. Structures revealed excellent performance such as remanent polarization $2\mathrm{P}_{\mathrm{r}} > 4\vert \mu\mathrm{C}/\mathrm{cm}^{2}$, endurance> 1011 cycles, and 10 years data retention at 85°C. Furthermore, the MFM capacitors were successfully integrated into a 64 kbit 1T1C FeRAM array including our dedicated circuit for array operation. Back-end-of-line (BEOL) wiring showed no degradation of the underlying CMOS logic. Program and read operation were properly controlled resulting in 100 % bit functionality at an operation voltage of2.5 Vand operating speed at 14 ns. This technology matches requirements of last level cash (LLC) and embedded non-volatile-memory (NVM) in low power System-on-a-Chip (SoC) for IoT applications.
A Review of Gamification Design Frameworks in Education

This paper discusses the benefits of using games on students’ engagement and learning acquisition. Then, the paper describes the teacher’s role as key stakeholder in gamification design. The paper uses an extensive literature review and discusses 23 existing gamification design frameworks and approaches, which have been investigated and grouped into four categories namely Pedagogical context; Scenario based; High-level approach and Gamification elements guidance. Although, some of the frameworks could fit in more than one category, they have been placed in the most representative one. The paper then suggests and discusses five frameworks and provides a comparative review. Finally, the paper closes with a critical review of existing gamification design frameworks used in the educational context to explore current practices and find the barriers and challenges that hinder teachers’ integration in the design process.
Density-Attentive Head Detector for Crowd Counting

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detectionbased approaches, and a comparable performance to regression-based approaches in term of counting.
Building and Evaluating Federated Models for Edge Computing

Today’s state-of-the-art machine learning (ML) techniques, such as deep learning (DL) networks are typically trained using cloud platforms, leveraging elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all applications due to concerns about latency, connectivity, and privacy. For example, sharing life logging photos and videos from cellphones and wearable devices can cause privacy concerns for users, and transferring the unstructured data can burden the communication network. With the increase of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones and wearable devices. In FL, clients collaboratively train a model on their device without sharing their data. Each client trains a local model with their data and shares the model parameters with a FL server to aggregate and build a global model. Shifting from traditional ML techniques to federated solutions requires comparing these two approaches. Moreover, users need to study the performance of FL models to decide if federation is feasible for their learning task. In this paper, we propose an automated solution to compare centrally trained DL models with federated solutions. The tool allows users to easily analyze the accuracy of federated models for their learning task and study the effect of the federated parameters. We show the features of our tool building central and federated DL models from an input model structure for recognizing images in the MNIST benchmark dataset.
Kinematic Modeling of Spherical Rolling Robots with a Three-Omnidirectional-Wheel Drive Mechanism

The spherical rolling robot (SRR) is an underactuated system with nonholonomic constraints. The proposed drive mechanism for the mobile robot is comprised of three omnidirectional wheels to make the omnidirectional movement of the robot on the floor. The kinematic model of the SRR is derived via nonholonomic constraints. Numerical simulation is analyzed. Results indicate that the robot can track the desired trajectory and maintain its balance stably at the same time.
Smart structural bonding process applied to high speed actuators: an experimental comparative characterization

This paper details a comparative study of the bonding process influence on steel sheet insulation for high speed actuators. In the present context where the performance of electric actuators is the main objective, improving performance of materials and industrial processes are key factors. In spite of highly accurate models and design tools being useful for this purpose, the efficiency is naturally limited to the materials’ performances. The proposed study compares 8 bonding configurations of sheet stacks for electric machines. The 8 configurations are based on a smart structural bonding process. High frequency iron losses measurements are used to determine the bonding influence on stack sheet insulation. The results show that adhesives and their implementation processes have both an important impact on high frequency iron losses through insulation quality and global stack performance.
Improved analytical calculation of axial AMB by means of fringing estimation

Active magnetic bearing (AMB) systems are designed to develop the force to compensate external forces acting on shaft alongside the support of the rotor system. The accurate analytical approach of attractive force calculation is demanded to avoid time-consuming finite element method (FEM) optimisation. According to the general theory of magnetic circuits, the model of iron is accurately modelled up to its saturation point. Lower accuracy is obtained for the air-gap permeance model. Fringing around the air-gap is usually neglected in the theory of linear magnetic circuit. In this paper fringing flux contribution to the air-gap permanence is studied. The fringing flux is modelled in FEM software for various geometry configurations focusing on the general solution without geometry dependencies. A new set of interlinking variables is presented in this paper. Tradition shape of thrust AMB is examined but the approach can be applied on similar geometries as well as to geometries beyond the scope of this paper. The result of the paper is a set of equations used to model fringing around air-gap.
Directly Cooled Windings in Switched Reluctance Machines

Compared to conventional electric machines, Switched Reluctance Machines (SRMs) are recommended to work in the saturation region to maximize the output energy. This is done by increasing the stator current to a certain limit that is usually identified by the maximal winding temperature. One method to cool the electric machine of concentrated windings is to insert cooling tubes between the windings in which a liquid is flowing. This paper investigates the influence of inserting cooling tubes between the windings of SRM on the losses and hence the resulting temperature distribution. A stator setup without rotor is employed. It is found that, in general, the losses in the cooling tubes are not significant, particularly for materials with a low electrical conductivity and a small thickness. However, the thermal analysis shows that a compromise between the thermal and the electrical conductivities for the tube material is preferred. Some experimental validations are obtained on a stator setup of 6 teeth.
Comparison among typical input signals of different types of Power System Stabilizers (PSS)

This article compares the typical input signals for different types of Power System Stabilizers (PSS). The comparison is performed in a way which is not available in the current literature since signals are shown herein from time-domain simulations for the sake of clearness. Speed, frequency and electrical power are analyzed as input signals for the case of PSS with single-input. Electrical power is combined with speed or frequency for the case of PSS with dual input, in order to obtain the accelerating power or its integral, which are also analyzed in this article. There are also other PSS types with multiple inputs, which are not considered in this article. For a well-known example from the literature, seven signals are analyzed without the effect of PSS, and it is shown that they can be grouped in two types: a) based on speed; b) based on accelerating power. Afterwards, it is shown that PSS parameters can be selected to obtain basically the same behavior with these seven types of PSS, assuming that mechanical power is constant. Under these circumstances, the required parameters for the PSS based on speed can be related to the required parameters for the PSS based on accelerating power, using the basic motion equation of the synchronous generator.
Large-Scale Analysis of Docker Images and Performance Implications for Container Storage Systems

Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the application’s execution environment. Containers are created from images which are shared between users via a registry. The amount of data registries store is massive. For example, Docker Hub, a popular public registry, stores at least half a million public images. In this article, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3 percent of the files in images are unique while others are redundant file copies, which means file-level deduplication has a great potential to save storage space. Furthermore, we carry out a comprehensive analysis of both small I/O request performance and copy-on-write performance for multiple popular container storage drivers. Our findings can motivate and help improve the design of data reduction and caching methods for images, pulling optimizations for registries, and storage drivers.
Detection and State Analysis of Drowsiness using Multitask Learning with Neural Networks

Infrastructure has seen tremendous growth globally. There is an increase in the number of vehicles commuting from one place to another. The increase in vehicles has also seen a gradual increase in the number of road crashes annually. The World Health Organization (WHO) has declared road traffic injuries and deaths as a severe global problem. 2015 accounted for 1.25 million deaths due to car crashes, and 2018 accounted for 1.35 million deaths. 23.5% of all the accidents are due to drivers driving in a state of drowsiness. In this paper we propose two models. The first model can classify whether a driver is drowsy or not. The model achieved an accuracy of 92.40%. The second model is a multi-class model which can identify the state of the driver such as yawning, blinking, talking etc. A total of 12 states can be identified by the model. Multitask learning encourages features in one sub task to be used in classification for other sub tasks. Using multitask learning, indicators of drowsiness in a driver is identified.
Cavity-Backed SIW Antenna With X Shaped Slot for Satellite Communication Frequency Band

The proposed manuscript demonstrates designs and analyzes the frequency band response of cavity backed substrate integrated waveguide (SIW) antenna. Single X shaped slot is used to achieve the satellite communication frequency band. The designed antenna resonates at satellite communication frequency band of 11.2 GHz with a gain of 2.7 dBi.
Power Supply System For Aircraft With Electric Traction

The advanced aircraft electrical power generation with voltage ±270 V DC BUS is presented. The possible consumption capacities and power consumption on regional aircraft with electric traction on takeoff and on cruise flight segment are analyzed. A battery-generator electric supply system is proposed, in which electric engines provide a traction of the plane, battery with permanent magnet generator are electric energy source. Upon that the combustion engine that works in economically mode and rotates shaft of permanent magnet generator. A power circuit of active rectifier is presented. It generates a bipolar output voltage ±270 V DC, provides a sinusoidal waveform of phase current and keeps power factor close to unity. The appliance of Enhanced Phase-Locked Loop for control system is presented. The developed special analytical and numerical computational procedures and modification of Lyapunov function method allow to determine stability areas and reveal unintended latent attractors in respect to proposed battery-generating electrical supply system. This article is of interest to developers of electrical supply systems with bipolar voltage DC in particular with a level of 270 V DC BUS
Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p < 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<40 Hz) frequencies. Conclusions: These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.
Stability of the composite thin films MOx – GO in photocatalytic processes

Vis-active composites based on metal oxide matrix and graphene derivatives fillers were intensively investigated in photocatalytic applications. Titanium dioxide and zinc oxide are the mostly investigated semiconductors and, coupled with graphene oxide, Vis-active composites are developed with suitable photocatalytic properties. Besides the adequate photocatalytic properties, the stability in the working condition was discussed based on the morphology and roughness changes before and after the photo-degradation processes of the methylene blue pollutant.