Miracle-dz/UniFAR-FTUs huggingface dataset:
State-Space Controller as a FOTD Based Generalization of ADRC

This paper extends ideas of some introductory papers interpreting advanced disturbance rejection control (ADRC) as a generalization of state-space controller design focused on using simplified integral plant models. Advantages achieved by this approach are evaluated by comparing robust constrained controllers based on extended state observer (ESO) design using integrative and first order time delayed (IPDT and FOTD) plant models. Thereby, this comparison may help in discovering new interesting properties of both considered types of resulting controllers. Theoretical analyses is illustrated by real time control of a thermal process.
Bi-Directional CLLC Resonant Converter with Integrated Planar Transformer for Energy Storage Systems

This paper presents a bi-directional CLLC resonant converter for Energy storage Systems applications, with an integrated planar transformer to enhance power density and curtail the parasitic elements in the transformer. The passive elements in any converter account for the bulkiness, and it can be vanquished by increasing the switching frequency, which can only be entailed by a planar magnetic structure and wide bandgap devices like gallium nitride (GaN) and silicon carbide (Si). Further, FEA analysis is performed to estimate leakage, AC resistance, and core losses for an integrated planar transformer. A simplified voltage/current loop control is implemented on a 32-bit digital signal processor (TMS320F28379D) for battery charging. The advantage of the proposed transformer design is validated through a 300-350 kHz, 500-750 W CLLC resonant converter with a peak efficiency of 96.60%
Leaky Surface Acoustic Wave with Velocity 10 km/s and Suppressed Leakage in LiNbO3 Plate Bonded to Sapphire

The high acoustic wave velocities required for high-frequency SAW devices can be combined with strong piezoelectric coupling and low propagation losses if a thin plate of LiNbO3 (LN) is bonded to a high-velocity sapphire substrate. This paper presents a numerical investigation of acoustic modes propagating in an LN/sapphire substrate under a periodic Al grating, demonstrating the occurrence of longitudinal leaky SAWs (LLSAWs) with a unique combination of high velocities exceeding 10 000 m/s, an electromechanical coupling coefficient of 4.5%, and negligible leakage into the substrate. The leakage was suppressed by optimizing the LN plate and Al electrode thicknesses. Compared with conventional SAW substrates, the LN/sapphire substrate offers 2- to 2.5-fold higher frequencies for periodic electrode structures and enables the production of 6-GHz devices by standard photolithographic processes while the required LN plate and Al electrode thicknesses remain compatible with existing wafer bonding technologies. In the optimal LN/sapphire structure, LLSAW behaves as a perfect Rayleigh SAW unperturbed by interactions with spurious modes in a wide range of frequencies.
Comparison of IPMSM Parameter Estimation Methods for Motor Efficiency

Efficiency of an IPMSM motor is influenced by the operating point (OP) of the machine. The optimal operating point can be found by using either direct search methods or model-based methods. Model-based methods are sensitive to parameter uncertainties of the equivalent circuit of the drive. In this paper, the impact of various parameter estimation methods on the motor efficiency is compared. Studied methods are Recursive Least Squares (RLS), frequency domain identification at standstill, and the flux linkage map method. Results are compared to direct search methods, where the efficiency is evaluated using the power consumption measurement and direct measurement of the torque. Comparison is performed on a grid of various setpoints (currents and speed). The RLS is tested in two versions of linearization of the flux: one using only the inductances, the second estimating also their offset. Each method is able to obtain good results for some OPs and bad results for other OPs. Overall, good performance was obtained for direct search, offline identified parameters and flux linkage map and RLS with flux offset. RLS without the flux offset does not yield consistent results which implies significantly lower efficiency.
SOC Balance and Economic Dispatch Using Distributed Cooperative Control for Distributed Energy Storage systems in DC Microgrid Applications

The distributed energy storage unit (ESU) will be a key component against the intermittent and stochastic character of the renewable energy resource in the islanded DC microgrid. The vanadium redox batteries (VRB) are a promising ESU for microgrid applications. However, one challenge in devising an islanded microgrid with ESUs is synchronizing the state of charges(SOC) of the ESUs. This paper proposes a consensusbased distributed cooperative control for alleviating voltage fluctuation, prolonging battery lifetime, and improving battery efficiency simultaneously. Furthermore, the real-time equilibrium of injected and output power and the constraints of the SOC are considered to simulate a more reasonable condition. Finally, this paper employs numerical simulation methods to test the effectiveness of the proposed algorithm in a DC microgrid composed of six DERs.
Real-time Power Doppler on an Ultrafast High-Frequency Hardware Beamformer

This work presents a custom-developed high-frequency ultrafast power Doppler imaging system with real-time hardware-based processing and display. Previous work was presented on the development of an ultra-fast high-frequency hardware-based beamformer capable of performing diverging wave imaging with up to 0.72 kHz frame rates. In this study, modifications to the ultra-fast beamformer enabled power Doppler imaging at a real-time display frame rate of 20 Hz. Using a 40 MHz 64 element phased array endoscope, the system transmitted 16 diverging waves/frame for at a frame rate of 0.72kHz. The high acquisition rate enables the capture of 38 B-Mode frames at a 20 Hz display rate. A 6-tap elliptic infinite impulse response (IIR) filter with a cut-off at 75 Hz was implemented on a field-programmable gate array (FPGA) to suppress tissue motion. The IIR filter required 20 frames to initialize the filter and used the remaining 16 frames to generate a power Doppler image.
Autonomous UAV Navigation in Dynamic Environments with Double Deep Q-Networks

With the rapidly increasing number and complexity of unmanned aircraft systems (UAS), enabling high-density operations becomes the most important goal for UAS operations in congested airspace. However, it is difficult to capture the global environment information such as geolocation of other unmanned aerial vehicles (UAVs) and the steep terrain in real-time. As a result, avoiding dynamic obstacles rather than static ones is challenging. Previous work demonstrates the feasibility of using traditional Q-learning to solve the navigation problem in a static environment, but this method is problematic when facing a dynamic environment because it usually causes the overestimation of action values. To address this challenge, this paper presents a framework based on double deep Q-network with priority experience replay (DDQN-PER) which allows the UAVs to navigate and avoid obstacles in a dynamic environment. The model is built upon convolutional neural networks (CNNs) whose input is raw pixels of the local known environment and whose output is an action after estimating future rewards. We set up multiple experimental scenarios with static and moving obstacles for different tasks which are ranging from single-agent navigation to multi-agent navigation. Then this model is applied to other pre-defined environments, without adjustment of the architecture or learning algorithm, to validate its generalization. Experimental results demonstrate that our proposed models can allow the UAVs reach the goal successfully in new dynamic environments.
Progressive Rain Removal via a Recurrent Convolutional Network for Real Rain Videos

Rain removal in videos is a problem that has attracted tremendous interest of researchers within the field of deep learning. Although deep-learning-based rain removal methods outperform large number of conventional vision methods, some technical issues that need to be resolved remain. In this article, we propose a new deep learning method for video rain removal based on recurrent neural network (RNN) architecture. Pseudo groundtruth was generated from real rainy video sequence by temporal filtering for supervised learning. Instead of focusing on various shapes of rain streaks similar to conventional methods, in this article, we focused on the changing behaviors of rain streaks. To accomplish this, images of progressive rain streaks were generated from the real rain videos and are sequentially fed to the network in a decreasing rain order. Multiple images with different amounts of rain streaks were used as RNN inputs to more efficiently identify rain streaks and then remove them. Experimental results demonstrate that our method is suitable for a wide range of rainy images. Moreover, experiments performed on both real-world and synthetic images demonstrate that our proposed method can achieve competitive results in comparison with the benchmarked and conventional approaches for rain streak removal from images.
Integrated Fuel Quantity Measurement

The importance of accurate fuel quantity measurement has gradually increased for its impact on range prediction. Available vehicle fuel measurement systems are only able to give inconclusive fuel level estimation. Vehicle fuel range anxiety on drivers is probably pushing researchers to present different measurement techniques, to improve on the status quo. This paper presents an integrated fuel quantity measurement technique, which is based on the segmented reed trigger points of a known fuel tank capacity, for continuous fuel density monitoring and the available fuel weight measurement. The technique gives accurate fuel quantity, as it accommodates fuel density variations, while eliminating the effects of fuel sloshing.
Reducing Dark Region Artifacts in Short-Lag Spatial Coherence (SLSC) Beamforming by Coherence Filtering of the Aperture-Domain Data

The short-lag spatial coherence (SLSC) beamformer measures the accumulated similarity of echoes, received by individual transducer elements as a function of spatial separation. It proved beneficial in suppressing incoherent clutter to improve detectability of hypoechoic and anechoic targets. However, with focused beams spatial coherence of backscattered echoes drops significantly away from the focal depth, where dark region artifacts occur due to high-level off-axis interference, reducing the effective depth-of-field when using SLSC. This study aimed to suppress this artificial dropout and keep the image uniformity through depths by filtering the aperture-domain data.
Phase Shift Adjustable Pilots for Channel Acquisition in Vehicle-to-Vehicle Underlay Wideband Massive MIMO

Decrease of the pilot overhead is an important issue in vehicle-to-vehicle (V2V) underlay wideband massive multiple-input multiple-output (mMIMO) communication. To reduce the pilot overhead for channel acquisition, we propose phase shift adjustable pilots (PSAPs) for V2V underlay wideband mMIMO systems employing orthogonal frequency division multiplexing (OFDM). Motivated by the sparse property of the angle-delay (AD) domain channels in mMIMO, we first investigate channel acquisition for V2V underlay mMIMO-OFDM with PSAPs, and derive the optimal conditions to achieve the sum mean square error (sMSE) minimums of channel estimation and channel prediction, respectively. The optimal conditions indicate that the minimum sMSE can be achieved when the AD domain channel power spectra of cellular users (CUs) and transmitters in V2V communication pairs are non-overlapping after appropriate allocation of the available pilot phase shifts. Then an AD domain channel power spectrum based pilot allocation (ADCPS-PA)algorithm is developed according to the optimal conditions. Simulation results indicate that the achievable spectral efficiency for both cellular and V2V links can be significantly improved with PSAPs compared with that obtained by the conventional phase shift orthogonal pilot approach.
A Decentralized Control Scheme for Adaptive Power-Sharing in Ships based Seaport Microgrid.

The increase of green house gases (GHGs) emissions and depletion in fossil fuels have urged an extensive use of renewable energy resources (RESs) and energy storage systems (ESSs) in short route ferries and yachts. In current times, most of the smaller seaports are not equipped with the cold ironing facility, hence, when fossil fuel based SMGs are berthed at the port their auxiliary engines are kept on, which pollutes the seaport. In order to address this issue, RES and ESS equipped SMGs can be interconnected via DC bus to formulate a sea port microgrid. The Dc bus interfaced sea port microgrid can be used for providing mobile cold ironing facility to the nearby ships that are integrated with fossil fuels to minimize the emissions, vibrations, and noise pollution. Generally, power sharing among several mcicrogrids is achieved through a communication based control strategies that adds up complexity and the cost. This study, alternatively, proposes an adaptive droop based control algorithm, which is applied to DC SMGs for communication less power-sharing among hybrid energy resources (Battery, Photovoltaic and Fuel Cell). The proposed scheme further helps to charge the battery banks that are below the threshold limits by using the RES integrated in several SMGs.
Intelligent Optimization Strategy Based on Statistical Machine Learning for Spacecraft Thermal Design

The thermal design of spacecraft becomes increasingly complicated as various advanced technologies are continuously introduced to the spacecraft. Determining and optimizing the uncertainties of a spacecraft thermal control system through global sensitivity analysis has long been an essential task for thermal engineers. It is a difficult task that relies heavily on engineering experience and is a time-intensive, trial-and-error endeavor that may not even lead to global optimization. Hence, an intelligent optimization strategy based on statistical machine learning for spacecraft thermal design, called IOSML, is proposed. An intelligent batch processing system (IBPS) based on MATLAB, Python, and NX/TMG real-time data interaction is designed. The IBPS uses a surrogate model to reduce the computational cost of global sensitivity analysis while using a detailed thermal mathematical model to maintain accuracy. We combine a Bayesian inference framework with a neural network surrogate spacecraft-thermophysical model that is $100\times $ faster than numerical solvers. This article first reports on a density-based global sensitivity analysis that evaluates the effect of design parameters on the temperature difference between the complementary metal–oxide–semiconductor and cold screen of the Lehman Alpha Solar Space Telescope detector. From 42 design parameters, the most sensitive four are selected for optimization, and the temperature difference and the boundary temperature are used as the objective function. Adopting IOSML, under no supervision, four design parameters are optimized through the IBPS, and the effectiveness of the algorithm is verified by comparison with traditional methods. Additionally, IOSML is versatile and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.
A Robust Second-Order Sliding Mode Control of Sensorless Five Level Packed U Cell Inverter

In this paper, a second order sliding mode based Super Twisting (ST) controller is designed and implemented on sensorless five-level packed U cell inverter (PUC5). The aim is to enhance the inverter performance and achieve better robustness. A low complexity modulation strategy is employed. It contains a few logic blocks and two level-shifted carriers, which reduces the computation time and eases the implementation. Moreover, it ensures quick and self-balancing of the inverter capacitor voltage. Employing the proposed super-twisting controller leads to a significant enhancement of dynamic response and steady-state performance of the PUC5. It provides better accuracy in the tracking of the reference current, which results in high power quality, and helps in fast self-balancing of the capacitor voltage. The feasibility and the effectiveness of the proposed controller has been verified by simulation and experimentally. The obtained results show the higher performance of the super twisting controller in stand-alone mode of operation.
Machine-Learning Models on the Edge to reduce Data Volume in Wide-Area Networks between various Production Sites

The availability of vast amounts of data in automated production systems reveals the potential for data-driven improvements. Jointly using this data across different sites or even across different companies will further increase the validity of data-driven models. However, the throughput in wide area networks is limited, limiting the large-scale transmission of data. Therefore, this paper proposes a data reduction approach to reduce network load based on regression and time series models directly on the shop floor. The machine-learning models are used to predict the signals of the automated production system to prevent the transmission of extensive raw data. It is shown that the approach reduces the network load significantly while still ensuring the fulfillment of the real-time control tasks of the programmable logic controller at any time. Thereby, the reduction of the data is dependent on the error of the reconstructed data that can be tolerated.
SAW Sensor based a Novel Hydrostatic Liquid Level Measurement

Hydrostatic liquid level measurement is constructed on hydrostatic pressure difference creates when the height (level) in the tank or container increases / decreases. Using this principle, a Surface Acoustic Wave (SAW) based hydrostatic liquid level measurement system has been proposed using a cantilever beam at the lowermost level (datum level) of the container. When the hydrostatic pressure increases/decreases it creates force on the cantilever beam which results into change in strain. SAW sensor sense the change in strain at different liquid level. Simulation and mathematical approach is reported in this paper. The proposed transducer is novel SAW based liquid level measurement technique in comparison to conventional liquid level measurement system.
Experimental study to evaluate the generation of reverberant shear wave fields (R-SWF) in homogenous media

In this work, we study the conditions in which a reverberant field is created by varying the number and locations of multiple mechanical sources, and then fitting axial and lateral autocorrelation profiles to theoretical models. Numerical simulation showed that at least 60 incident plane waves were necessary to generate a R-SWF. The general trend is that by applying more incident waves, the coefficient of determination improves and the error decreases. We report a bias error lower than 6% in the mean shear wave speed $(\pmb{C}_{s})$. Phantom experiments showed a similar tendency. Moreover, we demonstrated that the creation of a R-SWF based on the superposition of incident plane waves was possible. At least three vibration sources located at the top of the phantom surface were necessary to measure an average $\pmb{C}_{s}$ with an error less than 9%.
Energy Efficient Reduced Area Overhead Spin-Orbit Torque Non-Volatile SRAMs

This paper proposes two spin orbit torque nonvolatile static random-access memories (SOT-NVSRAMs) with reduced area overhead. One of the proposed cells comprises nine transistors (9T) and a pair of complementary SOT-MTJs and the other comprises seven transistors (7T), two MOM diodes stacked over a pair of complementary SOT-MTJs. The proposed 7T cell has only one overhead transistor compared to the standard 6T SRAM cell, achieving at least 50% reduction compared to other SOT-NVSRAM designs in the literature. Moreover, the always complementary MTJs aid to increase the read margin and speed, leading to at least 20% lower restore energy of our proposed 9T cell compared to others. Furthermore, the two SOT-MTJs are written serially resulting in at least 32% lower store energy of both designs than the counterparts in the literature. In addition, the two SOT-MTJs are totally disconnected from the 6T cell in the nominal SRAM operation, which aids in maintaining the conventional SRAM high performance. Our proposed cell achieves at least 2.2x and 1.9x improvement in a figure-of-merit defined as energy and area product compared to its counterparts in the literature.
Actual Output Power Levels of User Equipment in 5G Commercial Networks and Implications on Realistic RF EMF Exposure Assessment

Here, the results on actual output power levels of the fifth generation (5G) user equipment (UE) operating in commercial 5G networks are presented. Using a network management platform, output power data for several UE were collected over fifteen days. Out of about 545 million power samples obtained from UE operating in two 5G commercial networks, the time-averaged output power, of relevance for electromagnetic field (EMF) exposure assessment, was found to be always less than 43% of the maximum time-averaged UE output power considering time division duplexing. In addition, 95% of the time-averaged power samples were found to be less than 8% of the maximum while the mean value was less than 2%. Despite very conservative assumptions in the applied method, the time-averaged 5G UE output power levels were found to be well below the maximum. These results are in line with the output power levels observed for 3G and 4G UE in previous studies and is of importance to characterize the actual EMF exposure of 5G devices during operation.
Developing Neural Network Models for Partial Discharge Analysis

Neural networks in recent years have seen a rise in partial discharge related applications, with efforts mainly focused on measurements from ultra-high frequency sensors or high frequency current transformers. Existing works do not include neural network analysis of time-resolved partial discharge measurements on in-service cables generated with an external energising source. The inherent convoluted nature of these waveforms is a complicated recognition task which traditionally requires costly domain expert interpretation. This paper compares several neural network models and proposes a method that performs highly accurate recognition whilst reducing cost. The effectiveness of the proposed procedure is demonstrated by evaluating the performance across statistical measures.
Edge Caching for IoT Transient Data Using Deep Reinforcement Learning

Connected devices generate large amount of data for IoT applicatons. Assisted by edge computing, caching IoT data at the edge nodes is considered as a promising technique for its advantage in reducing network traffic and service delay of cloud platform. However, the IoT data is characterized by transient lifetime and cache capacity that is limited by the edge nodes. As a consequence, caching policy should consider both data transiency and storage capacity of edge nodes. Inspired by the success of deep reinforcement learning (DRL) in deal with Markov Decision Process (MDP) problem in unknown environment, A DRL-based algorithm for edge caching problem is proposed in this paper. The proposed Advantage Actor Critic (A2C)-based algorithm is aimed at maximizing the long-term energy saving without knowledge of the IoT data popularity profiles. Simulation results demonstrate that the proposed DRL-based algorithm can achieve higher energy saving and cache hit ratio compared with the baseline algorithms.