Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of a High-Efficiency All-SiC Dynamic Voltage Restorer for Wide-Range Sag/Swell Mitigation

This paper presents the design, modeling, and simulation process of a 30-kVA dynamic voltage restorer (DVR) layout for the compensation of voltage sags and swells ranging from 0.2 to 2 p.u. of the nominal grid voltage for up to 60 s. The converter is supplied by a battery energy storage system (BESS) and reaches analytical peak efficiency above 99%, by using silicon carbide (SiC) power MOSFETs and an improved circuit architecture. The circuit topology combines a T-Type inverter and a three-phase-interleaved bidirectional dc-dc stage in a dual-dc-port structure, where most of the energy flows from/to the battery directly through the inverter, while the dc-dc stage is only enabled to handle heavy sags/swells at low battery charge. The architecture, control strategy and efficiency analysis are examined in this work, relying on a simulation workflow developed in MATLAB/Simulink. A comparison of the proposed converter design to other DVR topologies is also made to demonstrate the efficiency improvement.
Surface micromachined micro-diaphragm pressure sensors

A micro-diaphragm pressure sensor with a silicon nitride diaphragm of 100 mu m diameter has been developed using surface micromachining (type-C). To increase the yield in the diaphragm forming process, the generation of hydrogen bubbles is reduced by eliminating the anisotropic etching of the silicon substrate and adopting a flat vacuum chamber fabricated by undercut etching of a polysilicon sacrificial layer. The sacrificial layer etching is carried out with a concentric circle around a central etch hole to reduce the stress concentration in the etching process. A tetramethyl ammonium hydroxide solution compatible with the LSI process is utilized as an etchant for polysilicon sacrificial etching. The arrangement of the polysilicon piezoresistors is determined according to stress analysis using a finite element method. The yield of the diaphragm forming process is improved to more than 90%. The pressure sensitivity of approximately 10 mu V/V/kPa is five times that of the conventional micro-diaphragm pressure sensor (type V). <<ETX>>
Sparse Filtering Under Non-Random Bounded Exogenous Disturbances

We propose a simple and universal approach to the solution of a sparse filtering problem via use of a reduced number of outputs under arbitrary bounded exogenous disturbances using an observer. The approach is based on the LMI technique and the method of invariant ellipsoids. Application of this concept made it possible to reduce the original problem to SDP that can be easily solved numerically. The approach is simple and easy to implement and equally covers both continuous- and discrete-time cases.
Use HF Signal Injection for Simultaneous Rotor Angle, Torque and Temperature Estimation in PMSMs

Use of high frequency (HF) signal injection-based methods with PMSMs has been widely investigated for several purposes including rotor angle/speed estimation, permanent magnet (PM) temperature estimation and torque estimation. This paper analyses the opportunities of multi-estimation. The physics behind these methods are analyzed, from which the number and characteristics of the HF signals to be injected can be deduced. Based on this, injection of a pulsating HF current 45° shifted from the d- axis and a rotating HF current in the stationary reference frame is found to be a suitable solution for simultaneous rotor angle, torque and magnet temperature estimation.11This work was supported in part by the Research, Technological Development and Innovation Programs of the Spanish Ministry of Science and Innovation, under grant PID2019-106057RB-I00.
Risk Assessment Methodology for Underground Construction Projects

This paper presents a risk assessment methodology for underground construction projects. A formalized procedure and associated tools were developed to assess and manage the risks involved in underground construction. The suggested risk assessment procedure is composed of four steps of identifying, analyzing, evaluating, and managing the risks inherent in construction projects. The main tool of the proposed risk assessment methodology is the risk analysis software. The risk analysis software is built upon an uncertainty model based on fuzzy concept. The fuzzy-based uncertainty model is designed to consider the uncertainty range that repre- sents the degree of uncertainties involved in both ~1! probabilistic parameter estimates and ~2! subjective judgments. Other tools devel- oped in this study include the survey sheets for collecting risk-related information and the detail check sheets for risk identification and analysis. The paper finally discusses a detailed case study of the developed risk assessment methodology performed for a subway construction project in Korea.
Nonlinear system identification using genetic algorithm

This paper presents an evolutionary system identification method for mechatronics systems which include various nonlinearities. In our research, the saturation in power converters and the friction in mechanisms are considered as the nonlinear elements, some of which are generally difficult to detect, and/or identify directly. The proposed method can determine the structure of linear and nonlinear elements of the system simultaneously by genetic algorithm. In the polynomial model by the proposed method, the observable input/output variables express the linear components, and the power series of variables and nonlinear functions express the nonlinear ones. Genetic algorithm is utilized to optimize the combination of these variables. The effectiveness of the proposed method is verified by the experiments using a 2-mass resonant, vibration system.
Anomaly Detection Based on Edge Computing Framework for AMI

Aiming at the cyber security problem of the advanced metering infrastructure(AMI), an anomaly detection method based on edge computing framework for the AMI is proposed. Due to the characteristics of the edge node of data concentrator, the data concentrator has the capability of computing a large amount of data. In this paper, distributing the intrusion detection model on the edge node data concentrator of the AMI instead of the metering center, meanwhile, two-way communication of distributed local model parameters replaces a large amount of data transmission. The proposed method avoids the risk of privacy leakage during the communication of data in AMI, and it greatly reduces communication delay and computational time. In this paper, KDDCUP99 datasets is used to verify the effectiveness of the method. The results show that compared with Deep Convolutional Neural Network (DCNN), the detection accuracy of the proposed method reach 99.05%, and false detection rate only gets 0.74%, and the results indicts the proposed method ensures a high detection performance with less communication rounds, it also reduces computational consumption.
Approaches for Improving Lumped Parameter Thermal Networks for Outer Rotor SPM Machines

This work is about the transient modeling of the thermal characteristics of outer rotor SPM machines by considering a lumped parameter thermal network based approach. The machine considered here poses particular challenges for the modeling, e.g., due to the semi-closed stator surrounded by a rotor bell that provides a speed-dependent cooling of the stator coils. Starting from a simpler basic network configuration, model extensions and refinements are presented and discussed. The subsequent parameter identification is done by means of an initial design of experiments based sampling, and a subsequent single-objective and also a multi-objective optimization of error functions for the components’ temperatures. Analyzing the therefrom derived Pareto fronts and the consequent tradeoff regarding achievable minimum modeling errors for different system’s components gives insights into where and how the modeling can be further improved. All the investigations are based on experimental results obtained through operating a particularly developed test setup.
Composite right/left‐handed band‐pass filters with wide fractional bandwidth based on dual‐metal‐plane structure

Composite right/left‐handed band‐pass filters with wide fractional bandwidth are implemented based on the proposed dual‐metal‐plane configuration.With proper design of the symmetric composite right/left‐handed unit cell composed with arbitrarily adjustable lumped‐elements, the passband and the fractional bandwidth can be constructed. Specifically, the implemented filters possess relatively achievable wide bandwidth in comparison with the conventional filters. For demonstration, two dual‐plane filters at different center frequencies with distinct factional bandwidth are designed and fabricated. These two filters exhibit good frequency selectivity around the passband. In addition, the measured results show good agreement with the simulated ones. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1810–1813, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25344
Propulsion System of Electric Vehicles: Review

Commercialization of electric vehicles (EVs) and light electric vehicles (LEVs) play a major role in reduction of greenhouse gases and environmental pollution. In this paper, the main reasons for popularity of the LEVs are explained based on the statistics available in literature. The propulsion system of the LEVs (including the electric motor, transmission system, battery package, and inverter) aims to offer high efficiency over a wide range of torque-speed operating points. Hence, the optimal design and performance improvement of such vehicles have attracted attention in literature. The investigated drivetrain systems in various LEVs are reviewed in this paper to highlight their advantages and disadvantages. The popular electric motors used in the LEVs are discussed and their characteristics are emphasized. The available research gaps in this field are highlighted at the end of the paper to provide a guide for the new researchers in this field.
IoT DDoS Detection Based on Stream Learning

The Internet of Things (IoT) represents a new reality, as smart devices spread quickly and a higher number of applications arises. This attracts the attention of not only legitimate users but also attackers aiming to jeopardize the entire IoT infrastructure. Intrusion detection mechanisms are paramount in this networking environment as its first line of defense. Hence, this work proposes a Network Intrusion Detection System (NIDS) that deals with the Distributed Denial of Service (DDoS) attack, one of the most critical attacks that occur through IoT. The proposed NIDS uses stream learning to detect DDoS attacks in the IoT network and is designed to be deployed in a fog infrastructure. The detection model, built on Hoeffding Anytime Tree (HATT) algorithm, achieved a 99% accuracy and a 99% recall.
Control Strategy to Attenuate Voltage Oscillations under Unbalanced Power Distribution in Large Scale Photovoltaic Cascaded Multilevel Converters

In this paper, a control system is developed to address the power imbalance in a large-scale grid-tied photovoltaic (PV) cascaded multilevel inverter. The proposed approach helps handling the unequal irradiance distribution among independent PV arrays located as input sources at each one of the stages of the multilevel converter, balancing all dc link voltages and improving the transient response of the buses. Depending on the power transferred to the grid, the individual control signals for each H-bridge cell are adjusted through a direct relation between the output of the individual dc link voltage controllers and the corresponding general voltage controller. Simulation tests are developed to evaluate the performance and transient response of the balancing method under sudden unbalanced irradiance variations.
High-speed underwater acoustic communication for multi-agent supervised autonomy

Service providers for subsea inspection, maintenance, and repair (IMR) generally utilize remotely operated tethered vehicles (ROVs) to inspect and to intervene on underwater infrastructure. The cost of these operations can be considerable due to the need of deploying a large surface vessel and a highly skilled crew to support the IMR campaign. This work describes a novel multi-agent autonomous system comprising an autonomous underwater vehicle (AUV) and unmanned surface vessel (USV) incorporating a proprietary high-bandwidth acoustic communication system capable of video transmission to the surface with effective data rates in excess of 100 kbps at 2 km. Real-time data stream transmission from the AUV was demonstrated at a depth of 840 m as well as a shallow water test in which the video stream of a World War 2 fighter plane wreck was transmitted to the surface.
Toward Robots’ Behavioral Transparency of Temporal Difference Reinforcement Learning With a Human Teacher

The high request for autonomous human–robot interaction (HRI), combined with the potential of machine learning (ML) techniques, allow us to deploy ML mechanisms in robot control. However, the use of ML can make robots’ behavior unclear to the observer during the learning phase. Recently, transparency in HRI has been investigated to make such interactions more comprehensible. In this work, we propose a model to improve the transparency during reinforcement learning (RL) tasks for HRI scenarios: the model supports transparency by having the robot show nonverbal emotional-behavioral cues. Our model considered human feedback as the reward of the RL algorithm and it presents emotional-behavioral responses based on the progress of the robot learning. The model is managed only by the temporal-difference error. We tested the architecture in a teaching scenario with the iCub humanoid robot. The results highlight that when the robot expresses its emotional-behavioral response, the human teacher is able to understand its learning process better. Furthermore, people prefer to interact with an expressive robot as compared to a mechanical one. Movement-based signals proved to be more effective in revealing the internal state of the robot than facial expressions. In particular, gaze movements were effective in showing the robot's next intentions. In contrast, communicating uncertainty through robot movements sometimes led to action misinterpretation, highlighting the importance of balancing transparency and the legibility of the robot goal. We also found a reliable temporal window in which to register teachers’ feedback that can be used by the robot as a reward.
An Approach to Predicting Conducted Noise from DC-DC Converter Accounting for Switching Fluctuation

Switching fluctuation, which causes jitter, occurs in a DC/DC converter's clock signal, which causes conducted noise to be time variant. An oscilloscope's averaging mode in the measurement partially removes high-frequency noise. Thus, the accuracy when predicting the noise by model identification based on the black-box equivalent circuit model is affected by switching fluctuation. In this paper, we first focus on removing the effect of switching fluctuation and improving the accuracy of noise prediction by using the noise signal decomposition method. We propose a method for decomposing a measured time-domain noise signal into ripple noise and turn-on and turnoff spike noises to prevent the accuracy from degrading in parameter identification. The waveform decomposition method for peak detection can be used to predict the noise spectra without switching fluctuation within a 3-dB prediction error for up to 200 MHz. In reality, switching fluctuation spreads the power density of the measured signal. Thus, our second area of focus is predicting the reduced noise spectrum when switching fluctuation occurs. The noise level of the predicted spectrum agrees well with that of the measured one.
Integrated mobile robot control

This paper describes the strucwre implementation and operation of a real-time mobile robot controller which integrates capabilities such as: position estimation path specification and hacking human interfaces fast communication and multiple client support The benefits of such high-level capabilities in a low-level controller was shown by its implementation for the Naviab autonomous vehicle. In addition performance results from positioning and tracking systems are reported and analyzed. © (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
A Harmonic Power Flow Method for Steady-State Analysis

Harmonic analysis in power systems is a significant challenge due to the increasing prominence of harmonics and owing to customer requirements for high power quality. This paper proposes a new harmonic power flow method and considers different key categories of nonlinear loads: loads with defined nonlinear V-I functions, thyristor-controlled converters, and inverters. A novel formulation for each device is derived and the effect of these harmonic sources are studied in single-source and multiple source scenarios. This method is not only simpler than conventional methods but also reveals the same precision. The method is implemented on the IEEE 14-bus system and different scenarios are studied. The results are compared to other methods in literature which proves the precision, applicability, and efficiency of the proposed method.
Optimization of Reactive Power Distribution in Series PV-Battery-Hybrid Systems

Series topologies have been gaining interest in photovoltaic-battery-hybrid (PVBH) applications. In previous research, the reactive power contribution from PVBH systems is distributed in a way to maintain the apparent power of all converters at the same level. However, the prior-art strategies cannot ensure optimal reactive power distribution, as the calculated reactive power references are not the analytical optimal solutions. In most cases, the apparent power of certain converters can be higher than others. This will lead to the overloading of certain converters and even the power curtailment of PV converters in extreme cases. To improve the performance of the reactive power distribution, an optimized reactive power distribution method based on the particle swarm optimization (PSO) algorithm is proposed in this paper. By doing so, the optimal reactive power reference for each converter can be obtained, and thus, preventing the potential overloading of certain converters. Experimental results have validated the effectiveness of the proposed solution.
Design Methodology and Considerations to Energy Efficient Switched Reluctance Motor for Ceiling Fan Application

In this paper, a robust design methodology is presented for a switched reluctance motor (SRM) for ceiling fan application. In the first section, 6/8, 8/6, 8/10, and 12/16 pole configurations of SR motor are designed in Maxwell 2D software for the same dimensions and magnetomotive force (MMF) excitation for a ceiling fan. A comparative analysis is done to Figure out the effect of phases, rotor poles, and multiplicity. Further, a new design of a 12/16 pole SR motor is proposed for ceiling fan application. The static and dynamic analyses of the motor are done to find out the motor performance. A look-up table-based model of 12/16 pole SR motor in MATLAB Simulink platform is simulated. Mechanical transient performance is also investigated using the Maxwell based external circuit connection. The look-up table-based model and external circuit connection simulation model with mechanical loading have good agreement with each other.
A Review on Cascaded Multilevel Inverter Control Strategies

Cascaded multilevel inverter topologies have gained a lot of interest in industry due to their modularity and scalability for high power and medium voltage levels. A set of structured controllers are required to coordinate each cell for achieving a broad variety of objectives. This paper presents a comprehensive overview of different control architectures developed for monitoring and coordinating cascaded multilevel inverters across a diverse set of applications.
Two Stage Particle Swarm Optimisation for Long-term Operation of a Hydroelectric Power System

The problem of determining the optimal long-term operation of a hydroelectric power system has been the subject of numerous publications over the past sixty years. A major problem encountered in operating long-term hydroelectric power system is their dimensionality. A great effort to decrease or eliminate possibility of dimensionality problem is addressed through developing innovative optimization techniques, such as genetic algorithms, artificial neural networks, and so on. Particle swarm optimisation (PSO), a newly developed evolutionary technique, is a population based stochastic search technique with reduced memory requirement, computationally effective and easily implemented compared to other evolutionary algorithm. However, there exist some difficulties in applying PSO to hydropower system. Constrained by complex constraints and hydraulic relationships between upper and lower reservoirs, it is unfeasible to use stochastic search algorithms of PSO directly for most initial populations. In this paper, a two stage PSO algorithm is presented to solve the optimal long-term operation of a hydroelectric power system. The maximisation of electricity generation and maximisation of minimal mean power of the hydropower system are alternatively used as the objective of longterm planning of hydroelectric power for the two stage problem. The maximisation of minimal mean power of the hydropower system is chosen as the objective at the first stage and an initial feasible solution will be generated using PSO. The system objective, ie the maximisation of electricity generation is selected as the objective at the second stage and the optimal result of the first stage will be used as the initial feasible solution. The proposed method is implemented to the optimal long-term operation of a hydroelectric power system in the Yunnan Power Grid which is located in the Yunnan Province of China and consists of 17 dominated hydropower plants with an installed capacity of 3,942.5 MW. The results show that the two stage PSO can give reasonable and efficient solution and that applying PSO to the long-term operation of a hydroelectric power system is feasible.
Network-Aware AutoML Framework for Software-Defined Sensor Networks

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the internet of things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework, which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using the metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.