Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Loss Investigation of Series-Connected Current Source Inverters

Series-connected current source inverters (SC-CSIs) are suitable for high-voltage and high-power applications. Traditionally, SC-CSIs consist of several identical CSIs connected in series. Recently, a new topology that employs fewer switches is proposed. Compared with the traditional topology, it features fewer switches and lower complexity. However, the power losses and the power loss distribution of this new topology have not been studied. In this work, the total power losses and the power loss distribution of these two topologies are investigated and verified by simulation.
Detecting Shaft-to-Seal Rubbing in Power Generation Turbines With the Acoustic Emission Technology

This paper presents results of an investigation to assess the potential of the acoustic emission (AE) technology for detecting seal-to-rotor rubbing in power generation turbines. The experimental program involved inducing rubs at various axial locations along a 412ton test rotor, rotating at 3000rpm and supported by 7in. (178mm) journal bearings. Whilst there was no change in vibration levels throughout the tests, bearing mounted AE transducers successfully detected all rubbing induced on the rotor.
On the Development of Short Axial Length Turbine Stator With Outlet Mach Number 2

This contribution deals with development of a small-size axial steam turbine stator with nominal outlet Mach number equal to 2. Shortening of existing stator geometry was the main goal in order to improve rotor dynamic behaviors. The stator inlet angle appreciably differs in a real machine, therefore it was very important to ensure insensitivity to the inlet angle in a wide range of it. Technological simplicity was also followed to decrease production costs. Description of the new profile geometry development is introduced and results from numerical simulations done by turbine producer Prvni Brnenska Strojirna (PBS) are summarized. Developed geometry of a linear cascade and also stator geometry was studied experimentally at the research institute Vyzkumny a Zkusebni Letecky Ustav (VZLU) in the next step. A comparison of the numerical and the experimental results is discussed in this paper and some remarks are mentioned.Copyright © 2010 by ASME
A Power Model for Monitoring Environmental Parameters on the Edge

In the Internet of Things, connected devices capture real-time data which is sent to the Fog or Cloud layer for processing. The transfer of large data volumes is subject to latency and variable transfer rates. Edge computing is an emerging trend that aims at processing data nearer to its source thereby reducing data transfer and the need for continuous connectivity. The main contribution of this paper is to investigate the power requirements of a battery-powered Raspberry Pi which acts as an Edge node. The latter monitors essential environmental parameters such as air temperature, air humidity, soil moisture and light intensity. The sensors are connected to an Arduino interfaced with the Raspberry Pi. A software-based power model relative to CPU utilization is proposed to measure the power consumption of the Raspberry Pi. A Java program is used to capture and save the sensor values in a database for further analysis. Experiments demonstrate that the power usage increases linearly with CPU utilization. The proposed power model has a root mean squared error of 0.023 and 0.036 respectively when one and two Arduinos are connected. The power requirement of a single Raspberry Pi with four Arduinos is estimated to be 2.58 Watt when the Java process is running. It is also projected that a 6,500 mAh battery can power this type of environmental setup that monitors plant growth for 12 hours.
Improvement of Electromagnetic Force and Acceleration in an Asymmetrical Star-Delta Winding IPMSM through Stator and Rotor Geometrical Modifications

Asymmetrical star-delta winding interior permanent magnet synchronous motor (IPMSM) is introduced as a capable option in supporting higher torque and lower torque ripple characteristics compared to the symmetrical star-delta winding IPMSM. This is at the expense of having higher radial electromagnetic (EM) force and potential vibration-related concerns including high acceleration. EM force and vibration reduction in the asymmetrical star-delta winding IPMSMs have not been well addressed in the literature so far. In order to improve the EM force and acceleration characteristics of the asymmetrical star-delta winding IPMSM while keeping its developed torque within the desired ranges, various stator and rotor geometrical parameters are defined and are changed individually. A sensitivity analysis is utilized to introduce the most effective geometrical design variables for the highlighted objectives. The reduction in the EM force, through the Maxwell-Stress tensor method, and the acceleration on the outer surface of the motor housing of the improved asymmetrical star-delta winding IPMSM structure over a base asymmetrical star-delta winding IPMSM model are investigated and reported via EM and structural simulations.
SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation

With the rapid development of automation tools including polymorphic and metamorphic engines, generic packers, and genetic programming, many variants of malware have emerged, which pose a significant threat to the Internet security. To effectively detect malware variants, researchers have developed visualization-based approaches that can visualize malware adaptations for in-depth malware analysis. However, most existing visualization approaches rely on the binary image of a malware sample, which fail to provide an effective texture feature representation and thus often result in low efficiency in coping with challenging malware samples. In this paper, we propose SpecView, a malware spectrum visualization framework with singular spectrum transformation. SpecView converts malware binary code into one-dimensional time series spectrum data, and leverages the singular spectrum transformation method to obtain the structural changes preserved in the time series spectrum data. Then, we utilize the particle swarm optimization algorithm to optimize the singular spectrum transformation performance in SpecView. We apply SpecView in the task of malware classification. Extensive experimental results show that SpecView is effective and efficient in malware classification on the Malimg, Malheur, Drebin, and PRAGuard Malgenome Class Encryption datasets, with classification accuracy exceeding 99%, and it can effectively identify malware variants that use evasive techniques such as packer and encryption obfuscation. The proposed method outperforms the state-of-the-art methods on all datasets and the classification accuracy reaches 100% for 5 malware families packed by the UPX packer on the Malimg dataset, as well as 9 malware families that use Class Encryption obfuscation techniques on the PRAGuard Malgenome Class Encryption datasets.
Hardware In The Loop Simulation of the Smart Grid with the inclusion of IEC61850 Communication Protocol

Smart gird is intelligent and interactive among its power devices using the communication network running a protocol for reliable transfer of messages from one device to another or with the system operator for critical operation and control of the substation. Employing protocol such as IEC61850 globally in the electrical substation need to be well tested for time critical operations, physical security of the substation with proper logic implementation for power flow in case of an event. If the substation is connected to the internet then the problem gets more complex with respect to cyber security challenges faced over this protocol.In this work, Hardware-in-the-loop simulation methodology of Smart grid including the physical IEC61850 communication net-work layer using Intelligent electronic devices of circuit breakers is presented. GOOSE messages and MMS server communication using IEC61850 in the smart grids are investigated inside the real-time simulation boundaries. The unique aspect of this work is the logic implementation among the protection devices are tested with real communication delays over the protocol that can occur between them, and these results are more realistic matching the real world scenario.
Simulation of Unsteady 3-D Wet-Steam Flows Through Three-Stage Stator-Rotor Blade Rows With Equilibrium and Nonequilibrium Condensations

Unsteady 3-D wet-steam flows through three-stage stator-rotor blade rows in a low-pressure steam turbine model developed by Mitsubishi Heavy Industry (MHI) are numerically investigated. In this study, a new and simple meshing strategy is introduced to improve the prediction of end-wall flows near the shroud and the hub. Dry and wet-steam conditions are taken into account and the calculated results changing the grid system are compared with the experiments. As a practical usage, coarse-grid computations using a PC cluster are also conducted and the results are compared with those using a fine grid and a supercomputer. In addition, a super-cooled condition is assumed and the computation is further conducted. Finally, the effect of homogeneous nucleation and the nonquilibrium condensation on the present flow is evaluated.Copyright © 2012 by ASME
Absolute Angle Calculation for Magnetic Encoder Based On Magnetic Flux Density Difference

Encoders are the most commonly used position measurement sensors in industrial machines and robots. Compared to potentiometers, encoders have advantages in terms of environmental resistance and cost, and their detection speed and resolution are comparable to those of potentiometers, so the use of encoders in the field of precision positioning continues to expand. Since the accuracy of motor control depends on the accuracy of angle and angular velocity obtained from the encoder, encoders with high reliability and high accuracy are required. The authors have developed a high-resolution and high-accuracy absolute rotary encoder that applies eccentric rotation. However, eccentric rotation causes vibration due to disproportionate force, which may lead to deterioration of accuracy and lifetime.In this paper, we proposed a new angle calculation method that calculates the absolute angle from the signal characterized by the use of magnets with different magnetic flux densities. With the proposed method, the absolute angle is obtained with the same accuracy as the conventional method.
Analytical EMI Modeling of an Active Neutral Point Clamped Inverter

This paper studies the electromagnetic interference (EMI) noise modeling for a three-level active neutral point clamped inverter. Although time-domain detailed modeling leads to more accurate results, frequency-domain modeling techniques are commonly used as a faster prediction method. Compared with the conventional differential mode (DM) EMI modeling of power inverters that use only one current source, it is shown that two current sources are required for more precise modeling. A new unified model is proposed for an active neutral point clamped inverter based on the existing equivalent circuit models for common mode (CM) EMI and the developed DM model. Both CM and DM EMI emissions are predicted by the proposed model and compared with the time-domain results. Simulation results indicate that the modeling precision is improved by 56%.
Deep Learning of CSI for Efficient Device-free Human Activity Recognition

Over the years, wireless sensing is gaining popularity in the applications of indoor localization and human activity recognition (HAR). As wireless signals are sensitive to human motion, they reflect and scatter in different directions depending on the activities performed by people. The channel state information (CSI) stores the combined effect of changes in the environment, and such stored pattern is utilized to recognize different human activities such as walking, standing, and sitting. Prior studies on activity recognition mostly differentiate human activities by classifying one complete series into an activity. However, these approaches require massive datasets to give accurate results in real-time scenarios, and the classification is in fact based on short-term activity samples instead of the complete activity series. In this paper, highly accurate sample-level activity recognition is achieved by exploiting a special type of convolutional neural network (CNN), U-Net. The data collection setup does not require manual feature extraction and can efficiently classify short-term activity samples. Our experimental results indicate that the proposed architecture can classify different levels of human activities with an accuracy of 98.57%, which outperforms conventional Deep Neural Network by 14.67% for the same dataset.
Comparative Analysis of Quadratic Buck-Boost Converters: Topology, Electric Stress, Reliability

In this article, a comprehensive review of quadratic buck-boost converters has been presented. Due to the wide conversion ratio, these converters are suitable for intermittent renewable sources like PV systems for power generation. This article aims to compare the quadratic buck-boost converters in terms of structural difference, the total electric stress experienced by power semiconductor devices, efficiency, and few other features. Further, the reliability of these converters is estimated with the help of the Markov chain model, and converters are compared with a mean time to failure period. Finally, the merits and demerits of each converter are explained.
Optimal Switching Sequence Model Predictive Control for Single-Phase Cascaded H-Bridge

The importance of Cascaded H-Bridge (CHB) converters in several applications has led to the introduction of advanced control strategies like Model Predictive Control (MPC). This paper proposes a new adaption of the Optimal Switching Sequence MPC (OSS-MPC) for application in a Single-Phase CHB. The control region of CHB converters is studied in order to apply this new strategy with the objective of tracking grid current and balancing the capacitor voltages of the modules. Another important contribution is the fixed switching period, which is an improvement over other MPC techniques. Starting from the control region, switching sequences are designed considering both switching states and time duration of each state. The method is tested through simulations.
Aggregate Spread Pulse Modulation in LPWANs for IoT applications

In low-power wide-area networks (LPWANs), various trade-offs among the bandwidth, data rates, and energy per bit have different effects on the quality of service under different propagation conditions (e.g. fading and multipath), interference scenarios, multi-user requirements, and design constraints. Such compromises, and the manner in which they are implemented, further affect other technical aspects, such as system’s computational complexity and power efficiency. At the same time, this difference in trade-offs also adds to the technical flexibility in addressing a broader range of IoT applications, both static and mobile. This paper introduces a physical layer LPWAN approach based on the Aggregate Spread Pulse Modulation (ASPM), and provides a brief assessment of its properties in additive white Gaussian noise (AWGN) channel. The initial discussion is constrained to the binary ASPM, where the control of the quality of service is performed through the change in the spectral efficiency, i.e., the data rate at a given bandwidth. Implementing M-ary encoding in ASPM further enables controlling service quality through changing the energy per bit (in about an order of magnitude range) as an additional trade-off parameter, which is discussed in a separate publication.
Dynamic Phasor-Based Modeling and Analysis of Dual-Loop Controlled DC-DC Converters

To design efficient and reliable direct current-based energy systems, telecommunication equipment, transportation systems, and healthcare devices, accurate DC-DC converter models are required. In addition, computationally efficient models are required to expeditiously simulate large DC systems (with multiple converters). In this paper, the dynamic phasor (DP) method is used to develop computationally efficient multi-frequency average models of dual-loop controlled DC-DC converters (buck, boost, and buck-boost) which are suitable for accurate modeling and analysis of ripples. By recognizing that high frequency components of inductor current and capacitor voltage are small compared to DC components, in addition to being equal to zero over a switching cycle, the control system is designed to target the average value of current and voltage. This simplifies the control process compared to existing DP-based closed-loop models of DC-DC converters. Small-signal modeling technique is used to obtain suitable control gains. The DP-based DC-DC converter models built on MATLAB/Simulink are validated against detailed models built on Simulink/Simscape. Simulation results confirm the effectiveness of the proposed control strategy as well as the huge computational advantage of DP-based DC-DC converter models over detailed models.
Modeling and Simulation of a Reduced-Order Single-Phase PQ Inverter Using the Dynamic Phasor Method

The accurate study of an inverter-based energy source is usually performed with the detailed inverter model. However, to perform system-level studies in microgrids, the time to simulate the system increases exponentially as more de-tailed inverter models are added. This paper presents a computationally efficient model of a single-phase PQ inverter based on the dynamic phasor (DP) method. By using the PQ theory and establishing a relationship between the direct-quadrature axis components of the synchronous reference frame and the DP real and imaginary components, independent control of the active and reactive power is realized. The fidelity of the DP-based single-phase PQ inverter model is verified by validating it against a detailed single-phase PQ inverter model implemented in Simulink/Simscape (SS). Simulation results reveal that the DP model can predict transients accurately, using a step size 125 times larger than the detailed SS model’s step size. The proposed DP-based PQ inverter model is suitable for fast-paced system-level studies of a multiple inverter based single-phase microgrid.
Dynamic Modelling Requirements for Tuning of Cascaded Voltage and Current Loops in VSMs

This paper identifies the importance of considering the full-order virtual synchronous machine (VSM) dynamics while tuning parameters in its cascaded voltage and current control loops. Via a numerical example, we highlight that the conventional tuning method of the voltage and current loops may not ensure system stability. This is because the tuning method does not consider full-order system dynamics. Thus, we improve the tuning method for the VSM voltage and current loops using small-signal analysis of the full-order VSM model. The proposed tuning method ensures system stability in the small-signal sense, and it is computationally inexpensive. We verify the effectiveness of the proposed tuning method via numerical simulations.
Suppression of Lower Order Harmonics using a 21-Concentric 42-sided polygonal Space Vector Structure for Induction Motor Drive Applications

A 21-concentric 42-sided polygonal space vector structure (SVS) for suppression of lower order harmonics for Open-End Induction Motor drive applications is proposed in this work. The Open-End Induction Motor is fed with primary and secondary inverters from either side. The primary inverter providing active power for motor operation is fed with an active DC link. The primary inverter is switched at low switching frequency to minimize stress on high voltage switching devices. As a result, the primary inverter consists of lower order harmonics in the pole voltage. The secondary inverter is employed only to suppress the harmonics generated by primary inverter and does not contribute any active power for motoring operation. Hence, the secondary inverter can be fed with a capacitive supply balanced at a nominal voltage. The secondary inverter, operating at lower voltages is switched with high frequency to suppress the lower order harmonics upto 39th order in the motor phase voltage. The advantages of polygonal space vector structures are retained throughout the modulation index, by generating 21-concentric layers of 42-sided polygons. This results in lower switching losses in low frequency switching main inverter and low voltage secondary inverter. A single DC link is employed in the power circuit topology to facilitate four-quadrant operation of the inverter. The proposed scheme is tested in MATLAB simulations and laboratory prototype. The simulation and experimental results are presented.
Joint UAV Deployment and Energy Transmission Design for Throughput Maximization in IoRT Networks

On account of the difficulty of deploying the ground base station (BS) and the limited power consumption of smart devices in Internet of Remote Things (IoRT) networks, it is necessary to construct a system assisted by unmanned aerial vehicles (UAVs) for sustainable communication. This paper aims to study a resource allocation problem in the wireless powered communication network (WPCN) while a UAV serves as an aerial BS. The optimization goal of this paper is to maximize the minimum throughput of ground terminals (GTs) by jointly optimizing the UAV deployment, time resource allocation, and power control. Nevertheless, the optimization problem is nonlinear and non-convex, which is difficult to solve directly. Therefore, it is transformed into two sub-problems, which can be iterated alternately to maximize the minimum throughput of each device in WPCN. The successive convex approximation (SCA) technique and variable substitution are used to transform two non-convex sub-problems into solvable problems. Finally, an iterative algorithm combining two sub-problems is designed to obtain the resource allocation strategies. The numerical results show that the communication resource can be fully shared by adjusting the position of the UAV, and the efficiency and performance of the algorithm are verified.
Network Flow Delay Prediction Model Based on LSTM

Quality of Service (QoS) is an important evaluation index of networks. Network delay is of great significance in understanding network behavior and analyzing network security status. The prediction method based on traditional measurement technology has shortcomings in timeliness and accuracy because it ignores some unexpected delay changes whose duration is less than the monitoring granularity. This paper proposes a new model, namely Nefopam, which adopts LSTM to predict the network flow delay. The paper makes the following contributions. (1) Taking into account the spatio-temporal characteristics of network flow data, Nefopam samples data by using multi-time scale fusion technology. (2) Nefopam employs long-short term memory network (LSTM) and graph convolutional neural network (GCN) to model the temporal correlation and spatial correlation of network flow data, respectively. (3) Nefopam uses multi-component fusion technology to generate network flow delay prediction model. The experimental results show that, comparing other three popular prediction methods, Nefopam improves the prediction accuracy.