Software In-The-Loop Simulation of an Advanced SVM Technique for 2ϕ-Inverter Control Fed a TPIM as Wind Turbine Emulator

An appropriate modulation scheme selection ensures inverter performance. Thus, space vector modulation (SVM) is more efficient and has its own distinct advantages compared to other pulse width modulation (PWM) techniques. This work deals with the development of an advanced space vector pulse width modulation (SVM) technique for two-phase inverter control using an XSG library to ensure rapid prototyping of the controller FPGA implementation. The proposed architecture is applied digitally and in real time to drive a two-phase induction motor (TPIM) for small-scale wind turbine emulation (WTE) profiles in laboratories with minimum current ripple and torque oscillation. Four space voltage vectors generated for the used SVM technique do not contain a zero vector. Hence, for an adequate adjustment of these four vectors, a reference voltage vector located in the square locus is determined. Considering the asymmetry between the main and auxiliary windings, the TPIM behavior, which is fed through the advanced SVM controlled-two-phase inverter (2ϕ-inverter), is studied, allowing us to control the speed and the torque under different conditions for wind turbine emulation. Several quantities, such as electromagnetic torque, rotor fluxes, stator currents and speed, are analyzed. To validate the obtained results using both Simulink and XSG interfaces, the static and dynamic characteristics of the WTE are satisfactorily reproduced. The collected speed and torque errors between the reference and actual waveforms show low rates, proving emulator controller effectiveness.

Diagnostics of the technical condition and predic-tion of accident-free guaranteed operating time of heavily loaded machines gear wheels

Problem. This article highlights the current state of diagnostics of gears on the level of accumulated fatigue damage in operation. The generalization of the known information on determination of a technical condition of separate gear wheels, especially with big modules (m> 20 mm), allows to outline prospects of development of such diagnostics of a technical condition of gear wheels directly in the course of operation. Goal. The goal of this study is to develop an algorithm for diagnosing the technical condition and forecasting a fault-free operation of the gears of heavy-duty machines. Methodology. The least squares method and the confidence interval method are used to predict accident-free guaranteed gear operation. Results. A procedure for diagnosing the technical condition of gears by measuring the hardness of the metal has been developed, which includes five main stages: selection of the device; choice of measurement scheme; selection of the number of measurements, number and relative position of measuring points; development of a design of a template for measurements; development of a device for fastening templates. When determining the accident-free guaranteed operating time on the basis of several criteria of the limit state, the final decision is made on the criterion that determines the minimum term. Originality. The method of forecasting accident-free guaranteed operating time of gears is chosen taking into account the stages of running-in and stable in linear function accumulation of damage in operation. Practical value. The use of the developed procedure for diagnosing the technical condition of the gears of gearboxes of heavy-duty machines will significantly increase the efficiency of their operation due to the transition from scheduled preventive maintenance to maintenance according to the actual technical condition.

Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

An energy-autonomous UAV swarm concept to support sea-rescue and maritime patrol missions in the Mediterranean sea

Purpose For the crews and assets of the European Union’s (EU’s) Joint Operations available today, but a vast area in the Mediterranean Sea to monitor, detection of small boats and rafts in distress can take up to several days or even fail at all. This study aims to outline how an energy-autonomous swarm of Unmanned Aerial System can help to increase the monitored sea area while minimizing human resource demand. Design/methodology/approach A concept for an unattended swarm of solar powered, unmanned hydroplanes is proposed. A swarm operations concept, vehicle conceptual design and an initial vehicle sizing method is derived. A microscopic, multi-agent-based simulation model is developed. System characteristics and surveillance performance is investigated in this delimited environment number of vehicles scale. Parameter variations in insolation, overcast and system design are used to predict system characteristics. The results are finally used for a scale-up study on a macroscopic level. Findings Miniaturization of subsystems is found to be essential for energy balance, whereas power consumption of subsystems is identified to define minimum vehicle size. Seasonal variations of solar insolation are observed to dominate the available energy budget. Thus, swarm density and activity adaption to solar energy supply is found to be a key element to maintain continuous aerial surveillance. Research limitations/implications This research was conducted extra-occupationally. Resources were limited to the available range of literature, computational power number and time budget. Practical implications A proposal for a probable concept of operations, as well as vehicle preliminary design for an unmanned energy-autonomous, multi-vehicle system for maritime surveillance tasks, are presented and discussed. Indications on path planning, communication link and vehicle interaction scheme selection are given. Vehicle design drivers are identified and optimization of parameters with significant impact on the swarm system is shown. Social implications The proposed system can help to accelerate the detection of ships in distress, increasing the effectiveness of life-saving rescue missions. Originality/value For continuous surveillance of expanded mission theatres by small-sized vehicles of limited endurance, a novel, collaborative swarming approach applying in situ resource utilization is explored.

Research on Input Scheme Selection of a Novel Parallel Mechanism

When the two arms of the robot are transporting the heavy loads together, a new parallel mechanism is formed. The actuator input selection and optimization of the parallel mechanism are basic and important problems in mechanism research. In this paper, a 2-RPPPS dual-arm robot is taken as the research object. Firstly, based on the screw theory and input selection principle, 158 reasonable schemes are obtained. Then, an evaluation mechanism is established to screen out the schemes that do not conform to the input selection principle. Then, the end effector of the parallel mechanism moves along two different trajectories. Using the particle swarm optimization algorithm, the inverse kinematics solution of each trajectory is obtained, and the velocity and acceleration of each actuator under different trajectories are obtained. Finally, the motion stability of each actuator is evaluated, and the best scheme is selected. The results show that the best input scheme can be selected according to different trajectories, so as to improve the performance of the parallel mechanism. To the authors’ knowledge, no one has done any research on selecting the appropriate input scheme according to the trajectory of the end effector.

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual’s external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.