Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

WFSM are included in the majority of large power generating units and special high-power motor drives, due to their high efficiency, flexible field excitation and intrinsic flux weakening capability. Moreover, they are employed in a wide range of high-end solutions in the low-to-medium power range. This contribution presents a comprehensive survey of classical and modern methods and technologies for excitation systems (ESs) of (WFSMs). The work covers the fundamental theory, typical de-excitation methods and all the modern excitation equipment topologies in detail. It also includes a description of the state-of-the-art and the latest trends in the ESs of wound-field synchronous motors and generators. The purpose of the paper is to provide a useful and up-to-date reference for practitioners and researchers in the field.

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Excitation System Technologies for Wound-Field Synchronous Machines: Survey of Solutions and Evolving Trends

Currently, with the increasing number of devices connected to the Internet, search for network vulnerabilities to attackers has increased, and protection systems have become indispensable. There are prevalent security attacks, such as the Distributed Denial of Service (DDoS), which have been causing significant damage to companies. However, through security attacks, it is possible to extract characteristics that identify the type of attack. Thus, it is essential to have fast and effective security identification models. In this work, a novel Intrusion Detection System (IDS) based on the Tree-CNN hierarchical algorithm with the Soft-Root-Sign (SRS) activation function is proposed. The model reduces the training time of the generated model for detecting DDoS, Infiltration, Brute Force, and Web attacks. For performance assessment, the model is implemented in a medium-sized company, analyzing the level of complexity of the proposed solution. Experimental results demonstrate that the proposed hierarchical model achieves a significant reduction in execution time, around 36%, and an average detection accuracy of 0.98 considering all the analyzed attacks. Therefore, the results of performance evaluation show that the proposed classifier based on Tree-CNN is of low complexity and requires less processing time and computational resources, outperforming other current IDS based on machine learning algorithms.

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Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network

A lightweight vehicles detection network model based on YOLOv5

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

This paper proposes a novel winding scheme to generate an additional sub-harmonic magneto-motive force (SH-MMF) component for the brushless (BL) operation of a wound-field synchronous machine. The existing SH generation schemes for BL operation use a dual inverter or a single inverter with asymmetrical winding and low slot fill factor in half of the stator. The proposed scheme comprises a single inverter with symmetrical stator winding distribution and the same fill factor for all stator slots. The generated additional SH-MMF component in the proposed scheme is induced in the harmonic winding wound on the machine rotor. A rotating bridge rectifier mounted on the rotor periphery connects the rotor harmonic winding with the rotor field winding, and rectified dc is supplied to the rotor field winding. The 2-D finite-element analysis (FEA) was carried out to analyze the principle, and the FEA predictions were experimentally validated.

Brushless Operation of a Wound-Field Synchronous Machine Using a Novel Winding Scheme

In this paper, a new consequent-pole hybrid excitation brushless wound field synchronous machine (CpHE-BL-WFSM) with fractional-slot concentrated winding is proposed. The proposed machine inherits the features of a brushless excited topology in which a rotor field is excited through an additional sub-harmonic magnetomotive force generated by the stator winding arrangement. In conventional BL-WFSMs, the zero starting torque and relatively low efficiency compared to that of permanent magnet synchronous machines (PMSMs) makes it difficult to replace expensive PMSMs in low- and medium-power applications. The advantages of the proposed machine are improved efficiency, improved starting torque, and high electromagnetic torque as compared to conventional brushless machines. A 2-D finite-element analysis is conducted to compare the proposed machine with the conventional BL-WFSM topologies. The results reveal that the proposed machine topology exhibits greater efficiency, improved starting torque, high average torque, and improved torque density.

Consequent-Pole Hybrid Excitation Brushless Wound Field Synchronous Machine With Fractional Slot Concentrated Winding

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.

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Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

https://www.mdpi.com/1424-8220/20/21/6218/pdf

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights.

In this article, a new low-cost small-scale three-phase brushless motor topology is proposed. This is a new topology for self-excitation of brushless wound rotor synchronous motor investigated initially for rated speed. For brushless excitation, the stator is designed to contain two windings, namely, three-phase armature winding and exciter winding. The rotor consists of two windings, that is, harmonic winding and field winding, and a diode rectifier. An inverter feeds the three-phase armature winding which is connected in series through an uncontrolled three-phase rectifier to a single exciter winding. When the current is supplied from the inverter, both the armature and exciter windings are energized eventually producing a flux in the air gap. Since the flux produced by the exciter winding is not synchronized with the field winding, it produces a harmonic to be intercepted by the harmonic winding on the rotor. Eventually, the voltage produced in the harmonic winding is used to feed dc current to the field winding for brushless operation. The results obtained by 2-D finite-element analysis (FEA) validate the brushless operation of the motor. The proposed motor is analyzed basically for topology verification with the FEA results. Since the topology can be verified, further effort can be put to investigate such low-cost topologies for brushless wound rotor synchronous motor. It can be an alternative to a costly permanent magnet synchronous motor for specific applications.

Muhammad Ayub

Papers

Brushless Operation of a Wound-Field Synchronous Machine Using a Novel Winding Scheme

Consequent-Pole Hybrid Excitation Brushless Wound Field Synchronous Machine With Fractional Slot Concentrated Winding

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights.

A New Small-Scale Self-Excited Wound Rotor Synchronous Motor Topology