Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback–Feedforward Compensation of a DC Motor

The separately-excited DC motor is a high-performance variable speed drive vital for industrial applications such as robotics, actuation, control and guided manipulation because of its precision, simplicity, continuous control feature and wide speed range. Hence there is need to regulate and drive the motor at desired speed in the presence of parameter variations, model uncertainties and external disturbances. The ubiquitous Proportional-Integral-Derivative controller is not the optimal control strategy to achieve this objective because of its oscillatory response, and sensitivity to load disturbances and motor parameter variations. Hence this paper proposes a Feedforward – Feedback control scheme, for armature voltage speed control, that will permit the design of two distinct control objectives; that is setpoint tracking with acceptable robustness and disturbance rejection. This combined scheme ensures high performance for the DC motor speed where the model-based tuned feedback action is used to guarantee reference speed tracking and to compensate for conceivable model inaccuracies while feedforward compensation maintains the output speed at the setpoint in spite of arbitrary changes in the disturbance variable. Computer simulations are then presented to show the effectiveness of the proposed scheme.
Ultrasonic structural health monitoring - current applications and potential

The trend to the application of structural health monitoring (SHM) as an alternative to periodic nondestructive evaluation (NDE) is discussed and examples are referenced of the use of both ultrasonic guided waves and bulk waves. Dealing with the volumes of data generated to produce useful information is a significant issue and fruitful areas for future research are given.
Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid

With various developments, the concept of the smart city has attracted great attention all over the world. To many, it is a good intelligent response to the needs of people’s livelihoods, environmental protection, public safety, etc. A weather-smart grid is an important component of the smart city, and the health of the weather-smart grid will directly affect the health of the smart city. Efficient and accurate predictions about air quality levels can provide a reliable basis for societal decisions, safety for smart transportation, and weather-related disaster prevention and preparation. To improve the time performance and accuracy of prediction with a large amount of data, this paper proposes an improved decision tree method. Based on an existing method, the model is improved in two aspects: the feature attribute value and the weighting of the information gain. Both accuracy and computational complexity are improved. The experimental results demonstrate that the improved model has great advantages in terms of the accuracy and computational complexity compared with the traditional methods. Additionally, it is more efficient in addressing classification and prediction with a large amount of air quality data. Moreover, it has good prediction ability for future data.
Performance Analysis of Mixed-ADC MIMO Systems Over Rayleigh Channels Based on Random Matrix Theory

The mixed analog-to-digital converters (ADC) architecture is a promising solution to the problem of high energy consumption of multiple-input multiple-output (MIMO) systems. In such a scheme, part of the received signals at the base station are quantized by high-resolution ADCs, while the others are quantized by one-bit ADCs. The signals quantized by the mixed-ADCs architecture are correlated and non-identically distributed, which is different from the one-bit quantized MIMO systems. Moreover, unlike the previous works focusing on the achievable rate of mixed-ADC MIMO systems under linear detectors, we derive a closed-form expression of the ergodic mutual information between the transmit signals and the quantized outputs of the mixed-ADC architecture over Rayleigh channels, where the statistical property of the equivalent channel is characterized by random matrix theory. The results show that in uplink mixed-ADC MIMO systems, reducing the transmit power will not sacrifice the transmission efficiency when the number of receive antennas increases. Furthermore, constrained by a given hardware energy consumption, the theoretical mutual information shows obvious gains over the one of unquantized MIMO systems for low user transmit power.
CEPHEID: the infrastructure-less indoor localization using lighting fixtures' acoustic frequency fingerprints

This paper deals with a new indoor localization scheme called “CEPHEID” by using ceiling lighting fixtures. It is based on the fact that each lighting fixture has its own characteristic flickering pattern. Then, the author proposes a technique to identify individual light by using simple instruments and DNN classifier. Thanks to the less requirements for hardware, CEPHEID can be implemented by a few simple discrete electronic components and an ordinary smartphone. A prototype “CEPHEID dongle” is also introduced in this paper. Finally, the validity of the author's method is examined by indoor positioning experiments.
Deep-Learning based Identification of Frames Containing Foetal Gender Region During Early Second Trimester Ultrasound Scanning

Sex-selective abortions, based on ultrasound foetal scanning of pregnant women, is a rampant problem in developing countries like India. Although prenatal gender screening has been outlawed, poor enforcement of it has abetted a steady rise in their number. Currently, the B-scans are displayed on the screen in real-time and the operator can make out the gender from the displayed image. Therefore, enforcement of the secrecy has not been effective and has only led to limitations on the usage of the technology. Hence, it may be useful to develop a method that can identify frames containing gender-indicative features in real-time and block it out automatically from the display, thus, preventing unauthorized viewing. In this work, deep learning-based techniques have been explored to detect images containing the gender-defining features among the entire set of images in cine-loop, with a conforming accuracy averaging above 80%.
Wafer Level Characterization of Row-Column Addressed CMUT Arrays

This paper presents a measurement methodology for wafer level characterization of row-column addressed (RCA) capacitative micromachined ultrasound transducers (CMUT). Characterization of a 62+62 element RCA CMUT is presented. To facilitate wafer level electrical characterization measurements between adjacent electrodes can be used to characterize the device. This allows for determination of the individual element capacitance. Current-voltage measurements between adjacent top or bottom electrodes provides valuable information about process yield.
E2E Delay Optimization for Smart Grids Mission-critical Slices in Core Networks

Based on software defined networking (SDN) and network function virtualization (NFV) technologies, network slicing can be customized to meet the end-to-end (E2E) quality of service (QoS) requirements of various power services in smart grids. E2E mission-critical services in smart grids should satisfy high reliability and low latency requirements, such as distribution automation, etc. In this paper, we slice the resource in core networks and flexibly coordinate different service function chains (SFCs) on the substrate physical network to enable different E2E mission-critical service delivery. Since the E2E average transmission delay is related to the congestion of each link in core networks, E2E transmission delay optimization problem is equivalent to minimize the maximum link congestion factor problem, which is Mixed Integer Linear Programming (MILP). Considering that the constraint of this problem is large and there are integer variables, the Generalized Benders Decomposition is adopted. The numerical results demonstrate the effectiveness of our proposed algorithm.
Improved Tone Reservation Method Based on Deep Learning for PAPR Reduction in OFDM System

This paper utilizes deep learning (DL) in tone reservation (TR) to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM). We propose TR based on DL (DL-TR) algorithm by considering each iteration of the classical TR algorithm as a layer of a deep neural network (DNN), and then train the network offline to obtain the clipping ratio of each layer which can minimize the loss function that is the sum of PAPR and the increased transmit power. Compared with the conventional TR method, the simulation results show that the proposed DL-TR provides a better PAPR reduction and bit-error-rate (BER) performance.
Investigations on the Experimental Identification of AC-Copper Losses in Permanent Magnet Synchronous Machines using a Motor Sub-Assembly

Analysis of AC copper losses in electrical machines is still a challenging task. Due to high utilization of high power density automotive drives, stray fields in the slots cause a significant copper loss increase compared to DC copper losses. Especially at large cross-sectional areas of the wires up to several times the DC-losses may occur in copper. This paper presents a new method of identifying these additional copper losses in electrical machines directly through measurement and without any finite-element co-simulation, which has not been shown in literature so far. Therefore, a motor sub-assembly consisting of three stator-teeth of an automotive machine and a rotor back iron are manufactured. Different adaptations of the motor sub-assembly are analysed using finite-element software to ensure proper flux-paths and AC copper losses close to the motor. Finally, measurements are performed and discussed.
Theoretical Analysis of Nonlinear Amplification Effects in Massive MIMO Systems

To fulfill 5th Generation (5G) communication capacity demands, the use of a large number of antennas has been widely investigated, and the array gain and spatial multiplexing that are offered by massive multiple input multiple output (mMIMO) have been used to improve the capacity. Fully digital architectures are not feasible for a large number of antennas, and hybrid analog/digital systems have emerged as options to retain a high number of antennas without as many radio frequency (RF) chains. However, these systems have, as consequences, non-avoidable nonlinear effects due to power amplifiers functioning in nonlinear regions. The strong nonlinear effects throughout the transmission chain will have a negative impact on the overall system’s performance. Being able to access this impact is very important. For this purpose, we propose analytical and semi-analytical tools that allow for the evaluation of the nonlinear effects of a hybrid analog/digital orthogonal frequency-division multiplexing (OFDM) system. The proposed analysis starts with the characterization of the power amplifier’s (PA) nonlinear response. This response is then used to derive a semi-analytic bit error rate expression. The theoretical tools are validated by using numerical results from two different cases: in the first one, the nonlinear PA response is assumed to follow an analytical model found in the literature and, in the second, the used nonlinear polynomial model mimics the response of a real amplifier. Using these two scenarios, the proposed tools are shown to be accurate making it possible to predict the nonlinearities’ penalties in hybrid analog/digital OFDM systems and/or to assess the optimal operation point for a specific nonlinear amplifier.
A Novel FCS Model Predictive Speed Control Strategy for IPMSM Drives in Electric Vehicles

Interior permanent magnet synchronous motors (IPMSM) are widely used in electric vehicles (EV) because of its advantages of high-power density and compact structure, etc. However, EVs have placed intensive demands upon good performance of their traction drive systems. Therefore, high-performance control strategies are required to be developed. In order to obtain simple control structure and avoid tedious parameter tuning work for PI controllers, this paper proposes a novel single-loop based finite control set (FCS) model predictive speed control (MPSC) strategy. Firstly, two challenges of achieving an FCS-MPSC controller is discussed. On this ground, an FCS-MPSC controller by estimating d, q-axis currents firstly and then predicting the future speed information is developed to solve the problem that the optimal switching state cannot be determined by the current states. Then, a sliding mode load torque observer (SM-LTO) for load torque estimation is proposed to to calculate the future speed. Simulation results prove the effectiveness of the proposed FCS-MPSC controller and load torque observer.
A Novel Bag-of-Optimised-Clusters Front-End for Speech based Continuous Emotion Prediction

Almost all current speech based emotion prediction systems employ front-ends that approximately represent the distribution of frame based features over a suitable window, typically via a set of statistical functionals or the use of Bag-of-Audio-Words (BoAW) features. These front-ends are designed either by manual selection of appropriate statistical functionals, via a feature selection approach or unsupervised clustering of the frame-based features and may not be optimal for the task at hand. This paper proposes a novel front-end that discriminatively learns feature clusters to generate a codebook optimised for emotion prediction, which is then used to generate a Bag-of-Optimised-Clusters (BoOC) feature set. Moreover, this front-end is implemented as a sequence of neural network layers that allow both the proposed front-end and a suitable deep learning backend to be jointly trained. The Bag-of-Optimised-Clusters frontend is tested on the RECOLA database and results show that it outperforms the well-established BoAW features.
Mamdani Type Sliding Mode Fuzzy Control of Non-Affine Nonlinear Systems

Mamdani type PID fuzzy controllers with sliding modes to deal with non-affine nonlinear systems are proposed in this paper. The design framework relies on particle swarm optimization applied to a nonlinear process model, in order to come up with the controllers gains. The main contribution is a robust control approach based on fuzzy control and on sliding mode control using a PID sliding surface. Results from simulations and tests on a nonlinear benchmark process show the effectiveness of the proposed control approach.
Resource Allocation in Switched-Beam Based mmWave MIMO CRAN

This paper focuses on the joint beam, subcarrier and power allocation in switched-beam based massive multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) system working at the millimeter wave (mmWave) frequency band, with the target of maximizing the weighted sum rate. The idea of cloud radio access network (CRAN) is also introduced into the system. To solve the optimization problem, we first divide it into two subproblems: 1) beam allocation for fixed subcarrier and power allocation; 2) subcarrier and power allocation for fixed beam allocation. For the first subproblem, a heuristic beam allocation algorithm is proposed. For the second subproblem, we transform it into a convex problem and solve it by using the proposed optimization algorithm based on the difference of two convex functions approximation (DCA) method. A low-complexity algorithm is also proposed to further reduce the complexity. Simulation results demonstrate that the proposed algorithms can enhance the weighted sum rate of the wireless transmission system significantly.
On the Power Sharing Dynamics of Parallel-Connected Virtual Oscillator-Controlled and Droop-Controlled Inverters in an AC Microgrid

Accurate active and reactive power sharing is of utmost importance in an inverter-interfaced AC microgrid. Power droop-control has been proven as a good design tradeoff and communication less technique to achieve load sharing. Implementation of power droop-control requires both the voltage and current tapping at the inverter output to calculate the power and achieve synchronization with rest of the system. On the other hand, recently proposed virtual oscillator controller (VOC) features inherent synchronization using only the current measurement. However, VOC being a non-linear oscillator introduces harmonic distortion in the output. This paper investigates the power sharing capability, dynamic response and harmonics profile for a system consisting of parallel-connected (i) droop-controlled inverter and (ii) virtual oscillator-controlled inverter. It has been demonstrated that power sharing between these two different control techniques can be achieved by properly designing both the droop-controller and VOC-parameters. The harmonic distortion present in the output of VOC results in the harmonic current to flow between the two inverters. The harmonic distortion can be reduced by designing a tightly regulated VOC inverter. However, this results in a slow dynamic response. Several scenarios including the plug-n-play capability, step change in load, and the design trade-off in between dynamic response and harmonic content present in the output is demonstrated through the simulation results.
Analysis of Eye Tracking of Physiotherapist during Walk Rehabilitation

The measurement of walking is important for evaluating the health of elderly people. This evaluation is performed by experts in gait assessment (such as doctors or physiotherapists), who vary in terms of skill. To guarantee evaluative quality, engineering of gait-measurement solutions is desired. This paper describes a method of quantifying tacit physiotherapist knowledge of walk rehabilitation by tracking and analysing their eyes. We constructed the eye-tracking system to extract the points of gaze of multiple physiotherapists. The result shows that proficient physiotherapists exhibit special eye movements, which we defined as ‘rhythmical’. We evaluated the proficiencies of all subjects based on their rhythmical-eye movement. It is clear that proficiency does not increase linearly with rhythmical-eye movement, but instead exhibits a staircase-like behaviour. Consequently, we elucidated that the ‘rhythmical-eye movement’ is one part of tacit knowledge of the proficient physiotherapists. And it is useful to establish the walking measurement closer to the proficient physiotherapists.
Fast Identification Method for Voltage Sag Type and Characteristic

The problem of characterizing voltage fluctuation is addressed as a means of the sag/swell identification in electricity networks based on the particle swarm optimization (PSO) in this paper. A new objective function of optimization algorithm to identify the voltage sags caused by short circuit fault(s) is obtained to describe the depth and types of sag/swell. Due to only a few sampling points are used in the objective function, the parameters of the ellipse could be recognized in subcycle to classify the sag. The results show that proposed method can identify not only the single stage sag but also the multistage sag within a cycle. MATLAB-based simulation results are discussed in detail to support the concept and validate the feasibility of proposed method.
On-line Model-based Short Circuit Diagnosis of Lithium-Ion Batteries for Electric Vehicle Application

Battery short circuit (SC), including both internal short circuit (ISC) and external short circuit (ESC), is an important stage before thermal runaway (TR). Therefore, on-line incipient battery SC detection is of vital importance to guarantee a safe and reliable operation of Lithium-Ion Batteries (LIBs). In this paper, based on a slightly modified battery equivalent circuit model (ECM), the purpose of battery incipient SC detection is achieved from the perspective of fault estimation. The proposed soft SC diagnosis method is independent of battery intrinsic properties as much as possible. A robust fault estimator, under the form of proportional-integral observer, is designed by solving two linear-matrix-inequality (LMI) constraints. Simulation studies based on an $A123-M1$ cell have verified the effectiveness of the proposed SC detection method.
Unscented Transform based Unbiased Minimum Variance Filter for Generator State Estimation with Unknown Inputs

Dynamic state estimation of synchronous generators is associated with various state space model based monitoring and control strategies. Estimation algorithms based on Kalman Filter are mainly used for estimation of the states locally using the generator input and output measurements. Whereas, wide area power system monitoring and control strategies require remote estimation of states and relies on Phasor Measurement Unit's (PMU) measurement signals which are limited to the generator output measurements like voltage, current, and frequency. In case of basic Kalman Filter based techniques, apart from output measurements, input measurements are also required for the estimation of states. This paper proposes an improved extended Unbiased Minimum Variance Filter algorithm that utilises Unscented Transform for remote state estimation with limited PMU measurements. The estimation is performed in the absence of input measurements. The performance of the filter under variation in input, measurement errors and the effect of initial estimate are analysed.
Model of a permanent magnet linear generator

This paper proposes a tubular permanent magnet linear generator (TPMLG) for a wave energy converter. The proposed TPMLG is built with an array of permanent magnets and independent coils. Advantages and disadvantages of the proposed device are determined with experimental and simulated results. A lumped parameter method is applied to analyze the amplitude of induced voltage and magnetic field mathematically. A finite element analysis is employed in the simulation software to obtain the dynamical behavior of these variables. The translator is tested at three different speeds. A prototype is built to confirm the theoretical analysis and simulations. Simulation and experimental results confirm the mathematical relationship between speed and the induced voltage.
Dynamic DC-Link Voltage Control of Back to Back Modular Multilevel Converter for Drive Applications

In this work a dynamic control of the dc-link voltage in a hybrid Back-to-Back (BtB) Modular Multilevel Converter for medium-voltage drive applications is presented. The proposed control scheme relates the speed of the machine with the dc-link voltage magnitude to reduce the large circulating currents and common-mode voltage of the converter when the machine operates at low speed. Simulation results, performed in the software PLECS, validate the model of the BtB converter and the proposed control scheme, reducing circulating currents and common mode voltage in almost 70% and 60% respectively, when the machine operates at very low speed.