Miracle-dz/UniFAR-FTUs huggingface dataset:
Learned Gradient Descent Performance in Bicubic Super-Resolution Task

In most cases, being an ill-posed problem, image restoration opts to restore a high-quality image from a low-quality one, assuming that some degradation model produced given low-quality input. A lot of restoration methods were proposed for the case when linear degradation operator and i.i. d. Gaussian likelihood is assumed. However, such methods are known not to generalize well. They show a sub-par performance on real data, for which the actual degradation model is neither linear nor even exactly known. The state-of-the-art machine learning allows for overcoming this issue and learn a restoration model to the real data. The main drawback of such approaches is overfitting since to learn an inverse mapping between the low-quality and high-quality samples, they rely entirely on data. They do not utilize limited but existing knowledge of how degradation was performed. In this paper, we study learned gradient descent based image restoration and synthesis. Both linear and non-linear known restoration problems are considered, and research on how a known degradation model may be incorporated in a learned gradient-based restoration procedure is provided. Our results demonstrate that explicit usage of the degradation model and its learned linear and non-linear approximations boost restoration quality compared to a baseline without this feature.
Non-Invasive Closed-Loop System Identification of an Active Rectifier

Industrial drive systems are characterized by dynamic peak loads and regenerative braking. These short-term loads lead to oversized supply infrastructure and low conversion efficiencies for prevalent partial loads. Energy storage systems (ESS) can be integrated into the DC link between rectifier and multiple inverters to increase energy efficiency and cover peak loads. To design a controller for the ESS, the underlying controller and process have to be modelled using physical laws and measured data. However, for each application this would mean having to extensively tune and adapt the controller to a specific manufacturing machine to ensure a stable, safe and energy-efficient operation. A self-tuning controller could solve this problem by automatically identifying the process and controller model of the closed-loop system. This paper aims to implement non-invasive closed-loop system identification for retrofitting an energy storage system to a machine tool. The validation of the approach shows that the actual DC link capacity and the controller parameters of the active rectifier can be identified reliably, e.g. with deviations of only 1.5 %.
A Survey on System-on-a-Chip Design Using Chisel HW Construction Language

This paper presents a survey of functional programming languages in System-on-a-Chip (SoC) design. The motivation is improving the design productivity by better source code expressiveness, increased abstraction level in design entry, or improved automation. The survey focuses on Chisel that is one of the most potential High Level Language (HLL) based design frameworks. We include 26 papers that report implementations ranging from IP blocks to complete chips. The result is that functional programming languages are viable for SoC design and can also be deployed in production use. However, Chisel does not increase the abstraction level in a similar way as High Level Synthesis (HLS), since it is used to create circuit generators instead of direct descriptions. Additional benefit is that Chisel offloads user effort from control and connectivity structures, and makes reusability and configurability improved over traditional Hardware Description Language (HDL) designs.
Dewatering of Sewage Sludge with Coal as a Conditioner Using Thermal Compression

Thermal compression is effective for solid-liquid separation of various materials. In this paper, thermal compression was applied on the dewatering of sewage sludge with coal as a filter aid. Experiments with 0.25-1 mm coal particles (0.2 kg coal/kg dry sludge solid) obtained cakes of solid content over 49%. Higher dosage of coal generated cakes with lower moisture content. The combustion behavior of the sludge cake was studied using TG/DTG analysis. The results showed that a total weight loss of 65.1% occurred from 25 °C to 800 °C, including two major weight loss stages corresponding to loss of moisture and volatile materials respectively.
Microscopy image reconstruction method based on convolution network feature fusion

In order to solve the problem of Fourier ptychographic microscopy imaging reconstruction, this paper proposes a new convolutional network feature fusion Fourier stacked microscopy reconstruction method to achieve image reconstruction from low resolution to high resolution. Use the open-source deep learning framework TensorFlow to build the network model and continuously adjust the hyperparameters of the network model. In order to train and test the network model, the FPM imaging model is used to construct a data set for network training and testing. At the same time, it is also tested on traditional methods and neural network-based reconstruction methods to evaluate network performance. The experimental results show that the model has better reconstruction effect and better robustness to noisy images.
Reduced bandwidth video for remote vehicle operations

Oak Ridge National Laboratory staff have developed a video compression system for low-bandwidth remote operations. The objective is to provide real-time video at data rates comparable to available tactical radio links, typically 16 to 64 thousand bits per second (kbps), while maintaining sufficient quality to achieve mission objectives. The system supports both continuous lossy transmission of black and white (gray scale) video for remote driving and progressive lossless transmission of black and white images for remote automatic target acquisition. The average data rate of the resulting bit stream is 64 kbps. This system has been demonstrated to provide video of sufficient quality to allow remote driving of a High-Mobility Multipurpose Wheeled Vehicle at speeds up to 15 mph (24.1 kph) on a moguled dirt track. The nominal driving configuration provides a frame rate of 4 Hz, a compression per frame of 125:1, and a resulting latency of {approximately}1s. This paper reviews the system approach and implementation, and further describes some of our experiences when using the system to support remote driving.
Frequency Selective Control of a Parallel Active Filter with Resonance Filter Regulators

Frequency Selective Control of a Parallel Active Filter with Resonance Filter Regulators The paper describes a method for frequency selective control of parallel active filters that are applied for compensation of the current harmonic distortions and the displacement power factor in symmetrical three-phase systems with a known harmonic spectrum. For simulation, an active filter for harmonic current compensation of a three-phase full-bridge diode rectifier was used. Comparative analysis has shown a good agreement between the results obtained experimentally and by simulation Paralēlā Aktīvā Filtra Frekvences Selektīvā Vadība AR Rezonanses Filtru Regulatoriem Rakstā ir piedāvāta frekvences selektīva vadības metode paralēlā aktīvā filtra pielietošanai simetrisku trīsfāžmu strāvas harmonisko kropļojumu un reaktīvās jaudas kompensēšanai ar zināmu strāvas augstāko harmonisko komponenšu sadalījumu. Doti izstrādātās vadības metodes datormodelēšanas un eksperimentālo pētījumu rezultāti, aktīvajam filtram kompensējot trīsfāžu tilta nevadāmā taisngrieža ieejas strāvas augstākās harmoniskās komponentes.
Design and Optimization of a Quadruple 3-Phase Permanent Magnet Assisted Synchronous Reluctance Machine

This paper deals with the design and optimization process of a quadruple 3-phase permanent magnet assisted synchronous reluctance machine (PM-SynRM) using a multiobjective genetic algorithm of Matlab’s Global Optimization Toolbox (gamultiobj). The implemented algorithm allows an operating point dependent optimization considering nonlinear flux linkage maps. The resulting 75 kW PM-SynRM with three flux barriers and six poles is intended for an automotive application with a DC link voltage of 48 V. In order to reduce the amount of rare earth magnets the PM-SynRM uses one NdFeB and two ferrite magnet(s). This machine type is called Hybrid-Magnet-SynRM in this paper and will be compared against a pure Ferrite-Magnet-SynRM. It will be shown that in this framework FM-SynRMs lead to high demagnetization ratios after a symmetrical short circuit. Hence the NdFeB-magnet is used to enhance the demagnetization withstand capability. Moreover the HM-SynRM will be optimized with two objective functions: active volume and weighted sum of efficiency considering three operating points.
Efficient Polyline Surface Mapping with Strong Error Bounds

We propose a novel surface mapping algorithm processing sensor data from, e.g., stereo or structure from motion video, triangulating ultrasonic, lidar, or tactile sensors. The algorithm does not rely on a grid and instead constructs a set of polylines as an extremely efficient representation of surrounding surfaces. We use ridges of a continuous occupancy to extract surfaces from location measurements which are subject to noise. Our algorithm for iterative map updates is efficient, stable, unconditionally convergent, gives strong guarantees about the map accuracy, and is invariant under permutation of the input data. It does not suffer from association errors, and operations such as shape updates, splitting, and merging of surfaces arise naturally and are not handled separately. The algorithm corrects wrongly mapped surfaces with future more accurate measurements, and the polyline approximation of the ridge can be tuned to arbitrary accuracy. Runtime measurements demonstrate the feasibility of the approach for embedded applications.
CONTROL OF AIRCRAFT STALL VIA EMBEDDED PRESSURE SENSORS AND DEPLOYABLE FLOW EFFECTORS

Experimental investigation of a transparent stall control system utilizing dynamic pressure sensors, deployable flow effectors, and a closed-loop feedback controller is presented. Findings on the distinctive nature of pressure fluctuations during pre-stall conditions served as the actuating signal for the controller to trigger the flow effectors. The deployable flow effectors act as micro-vortex generators that enhance the mixing of high momentum fluid flow outside the boundary layer with low momentum fluid flow within the boundary layer to prevent flow separation and thereby delay dynamic stall. Both open- and closed-loop flow control experiments were conducted on a 30°-sweep NACA 0020 airfoil at a Reynolds number of 0.6 x 10 6 for angles of attack ranging from 0° to 23°. Results from open-loop experiments show a 54% increase in the pressure coefficient (-Cp) prior to airfoil stall and a delay of stall angle of attack from 19° to 21°. Closed-loop experiments demonstrated the ability of the controller to successfully detect pre-stall conditions based on the information from dynamic pressure sensors. Detection and control of flow separation over flight control surfaces using the presented active flow control technique offer significant improvement to the performance of air vehicles.
Analysis of Vernier Machine with Stator-V-Shaped Permanent-Magnet Arrangement

This paper presents a new vernier permanent magnet machine (VPMM) with stator-V-shaped permanent-magnet arrangement, termed as (SV-VPMM). The key is to adopt unevenly distributed V-shaped PMs in the stator, which exhibits flux concentration effect and generates abundant working harmonics, thus improving the torque density and PM utilization ratio. Based on the air-gap field modulation theory, the working mechanism of the proposed SV-VPMM is investigated from two perspectives including stator-PM and rotor-PM fields. Then, the electromagnetic performance comparison between the proposed SV-VPMM and an existing VPMM is conducted by using finite element analysis (FEA).
VSG Control Strategy Based on Model Predictive for Islanded Microgrid Inverter

Most of the microgrid inverters are based on PI-based voltage and current double-loop control structure to achieve control target, but the controller parameter setting is complicated and the dynamic response speed is slow. The paper adopt Predictive Models Control (MPC) replace traditional voltage and current double-loop and modulation to improve the dynamic response speed and parameters tuning. In order to maintain the damping character of microgrid and overcome droop control frequency change rate, this paper adopts the strategy of Virtual Synchronous Generator (VSG) in the power loop to make the system have inertia and enhance stability. The simulation comparison verifies the proposed control strategy is effective and the faster response speed.
Drug Classification using Machine Learning and Interpretability

In this paper we have performed drug classification using machine learning models like random forest, decision tree, and logistic regression. To further understand how these models came to their conclusion, we find the interpretability of these models using LIME and SHAP. Towards the end, we found that LIME and SHAP can be used to gain insights into a Machine Learning model and conclude which feature is responsible for the deviation of the results. According to the LIME and SHAP results, Random Forest and Decision Tree ML models are the most suited models for drug classification as both achieved an accuracy of 0.975. Also, Na to K and BP were found to be the most essential features in predicting the outcome.
A Bike-sharing Demand Predicting Model with Integrating Temporal Convolutional Network and Self-Attention

As a green and convenient means of transportation, bike-sharing has received significant attention and made rapid development worldwide. However, due to the differences in urban areas and users' riding habits, the supply and demand of bike-sharing is unbalanced, which affects the full utilization of bike-sharing. Given this, it is necessary to predict the demand for bike-sharing. In this paper, we propose a Bike-sharing Demand Predicting (BDP) Model with integrating Temporal Convolutional Network (TCN) and Self-Attention. BDP model takes multiple inputs to extract the feature information from multi-source data and employs the parallelism of TCN and Self-Attention to improve the training speed. Moreover, we bring the time signals for reflecting the periodicity of bike-sharing demand in the unit of day, week, and year. To evaluate the research work, we select five deep learning models, and two alternated BDP models with different inputs as the baselines. The results show that BDP model outperforms the baseline models with the highest prediction accuracy and the better indicators of MAE, MAPE, SMAPE, and RMSE.
Multilevel Inverter with a New Modulation Method Applied to Solid-State Transformer in PV Applications

According to recent improvements in microgrid and Renewable Energy Sources (RESs), it is vital to use an alternative system for conventional transformers due to its lack of controllability and having high volume and weight. So, the senses gathered to the Solid-State Transformer (SST) because of its relevant advantages. A power electronic converter converts a low-frequency ac voltage waveform to a medium frequency voltage with variable amplitude in most SST structures. First, there is a need to convert the DC voltage to a pure sinusoidal one for using the High Voltage DC (HVDC) in Distributed Generator (DG) sources such as Photovoltaic (PV) farms and microgrids. Then an ac-ac converter is used to produce a medium frequency waveform. These structures need two stages of energy conversions. This paper proposes a new configuration with a specific modulation method to convert the DC voltage waveform directly to a medium frequency voltage waveform with variable maximum amplitude. The simplicity of the control system, reduced volume, and weight, as well as the reduced number of energy conversion stages are the advantages of the proposed system.
Development of VHDL‐AMS neuro‐fuzzy behavioral models for RF/microwave passive components

The new generation of System‐on‐Chip (SoC) incorporates digital, analogue, RF/microwave and mixed‐signal components. Such circuits impose to reconsider the traditional design methods. Mixed‐signal designers need novel design methodologies which will have to include accurate behavioral libraries of devices and processes into hierarchical design flows. Thus, this paper describes a behavioral modeling approach which generates neuro‐fuzzy‐based models for RF/microwave devices. The models, so obtained, can be easily integrated into a VHDL‐AMS simulator. This modeling approach is applied to a microwave tunable phase shifter and it is illustrated by the development of a VHDL‐AMS model library for RF/microwave applications. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.
Smart Vehicular System: Demand, Curiosity, Challenges, And Power Electronics Skills

In modern days, to achieve low emission of Green-House Gases (GHG), clean system, easy maintenance, high starting torque, and high efficiency, Internal Combustion (IC) engines are gradually being changed by electric motors. Smart vehicular systems (SV) either functioned from moderately or fully electrical energy generated from renewable sources. The energy storage, i.e., battery or ultra-capacitor system, gains energy from the IC engine. Alternative the electric grid, e.g., IC engines and electric grids are used to charge the battery in Hybrid Smart Electric Vehicle (H-SEV) and Plug-in Smart Electric Vehicle (PSEV), respectively. Power Electronics skills play an imperative role in the energy conversion between Grid-vehicle-Wheel (G-VW) and Wheel-vehicle-Grid (W-V-G). This letter discusses smart vehicular systems with their demand, curiosity, challenges, and power electronic skills.
An Improved Voltage Control Strategy Based on Finite-Time Theory for Virtual Synchronous Generator

As an original inverter control method, the virtual synchronous generator (VSG) control technology has aroused much attention in recent years since it can achieve better dynamic voltage regulation when micro grid (MG) operates on island or load changes suddenly. Further, aiming at the superior voltage control of VSG, this paper proposed a double closed loop control strategy based on finite-time control (FTC) to improve the dynamic performance of the system. Compared with the traditional proportional integral (PI) control strategy, the proposed strategy can achieve the quick respond speed and strongly robustness when the event of load change occurs. Finally, the simulation and experimental results demonstrate the feasibility and correctness of the proposed control method.
A Detailed Loss Model of Current-Fed Half-Bridge AC-DC Converter Considering Swinging Boost Inductor

On-board charger (OBC) power handling capability has been increasing over the past few years for reducing the charging time. This requires a much detailed analysis of the losses incurred in OBCs for better volumetric design and improved effi-ciency. This paper presents a comprehensive loss model of a single stage (1-S) single phase (1-ϕ) OBC. The OBC is realized using a current-fed half-bridge converter at grid side with swinging boost inductor and a full-bridge converter at the battery side, galvanically isolated by a high frequency transformer (HFT). Swinging boost inductor maintains high power factor and keeps the inductor current in continuous conduction mode (CCM) for a wide load range. The effect of swinging boost inductor on the losses of the converter is presented. A laboratory prototype connected to 230 V/50 Hz mains voltage with the output voltage range of 300-400 V is also developed to validate the theoretical analysis.
Distributed Cooperative LFC Protocols for Regulation Synchronization for Networked Multi-area Power Grid Networks

In this paper, we propose consensus based distributed cooperative LFC schemes for leader-less networked multi-area power grid network systems in the presence of uncertainty. The LFC schemes design combine local states with the states of the neighboring area with directed communication topology. We propose two distributed cooperative LFC schemes. First, robust LFC schemes are designed by assuming that the bounds of the uncertainty associated with the power network dynamics are available. Then, we remove the demand of the bound on the uncertainty from LFC schemes by designing robust adaptive learning algorithm. Robust adaptive control terms uses to deal with the presence of uncertainty associated with the power networks and external fault disturbance. Lyapunov and graph theory uses to show that the proposed distributed cooperative design can reach an agreement with control areas and solve regulation synchronization problem. Analysis shows that the state of the control areas can reach an agreement and ensure both finite-time and asymptotic consensus property. Evaluation results on a four-area interconnected power grid networks are presented to show the effectiveness of the proposed consensus based distributed LFC algorithm for real-time applications.
InAs-InGaAs quantum dot VCSELs on GaAs substrates emitting at 1.3 µm

Pulsed lasing at 1.3 µm via the exciton ground state is demonstrated for vertical cavity surface emitting lasers containing three uncoupled sheets of InAs quantum dot active layers. The structures are grown directly on GaAs substrates and when fabricated include selectively oxidised AlO current apertures, intracavity metal contacts, and AlO/GaAs distributed Bragg reflectors. Experimental devices operate pulsed at room temperature with threshold currents below 2 mA and differential slope efficiencies above 40%.
Modeling and Design Considerations in the Control of an Isolated Bidirectional Electric Vehicle Charger

With the advent of new electric vehicle technologies, research into electric vehicle chargers have also progressed immensely in the recent times. This paper discusses the modeling and design considerations involved in a bidirectional isolated electric vehicle charger. An electric vehicle charger comprises of a front end rectifier with power factor correction capability, with or without bidirectional feature cascaded with an isolated or non isolated DC-DC converter. The small signal impedance models of these converters are developed which aids in analysing the interaction between the two converters, while they try to meet their own control objectives. It is imperative to ensure that these interactions will not induce unintended oscillations at the DC link or instability in the cascaded system. Detailed simulation results of this cascaded system are presented to validate the analysis.
High Frequency PCB Trace Current Measurement in Power Converters Based on Tunnel Magnetoresistance

In power converters, current sensing is essential for control and protection. This paper employs tunnel magnetoresistance (TMR) to non-invasively measure the PCB trace current in high frequency power converters. The operating principle of the TMR sensor is briefly introduced. Then we discuss the influence of skin effect on TMR measurement delay and bandwidth. Based on the finite element method (FEM) simulations, optimal install locations of the TMR sensor are found to avoid the influence of skin effect. An empirical formula is provided to summarize the optimal install locations under different trace widths. By putting the TMR sensor at the optimal location, the measurement delay is reduced from 517ns to 102ns according to experimental results. Next, a comb-shaped shield is proposed to suppress the switching spikes of TMR sensor output caused by common mode noise. The parameters of the comb-shield are chosen such that the electric field are shielded without affecting the magnetic field distribution. With the proposed comb-shaped shield, the TMR output spikes is reduced from 0.20V to 0.05V, whereas the measurement delay is almost not affected (W/O-102ns, with114ns).