Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Derivation of Phase Current Waveform Eliminating Torque Ripple and Input Current Ripple of Switched Reluctance Motors under Magnetically Saturated Operation

Switched reluctance motors (SRMs) is beneficial in excellent cost-effectiveness and high thermal tolerance owing to their simple mechanical construction free from permanent magnets. Nevertheless, application of SRMs to vehicular propulsion is often hindered by their comparatively large input current ripple and torque ripple. For mitigating the problem, the previous study has proposed a phase current waveform that eliminates both of these ripples under the magnetization below the magnetic saturation. However, this previous technique still suffers from significant torque and input current ripples at large output torque, which tends to cause the magnetic saturation. This paper proposes an improved phase current waveform that eliminates these ripples even at large output torque. This waveform was derived analytically based on the nonlinear magnetic model of the SRM. The proposed phase current waveform was successfully verified by the experiment, suggesting the usefulness of the proposed technique for applying SRMs to vehicular propulsion.
Recurrent Conditional Generative Adversarial Networks for Autonomous Driving Sensor Modelling

Simulation of the real world is a widely researched topic in various fields. The automotive industry in particular is very dependent on real world simulations, since these simulations are needed in order to prove the safety of advance driver assistance systems (ADAS) and autonomous driving (AD). In this paper we propose a deep learning based model for simulating the outputs from production sensors used in autonomous vehicles. We introduce an improved Recurrent Conditional Generative Adversarial Network (RC-GAN) consisting of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) in both the generator and the discriminator networks in order to generate production sensor errors that exhibit long-term temporal correlations. The network is trained in a sequence-to-sequence fashion where we condition the output from the model on sequences describing the surrounding environment. This enables the model to capture spatial and temporal dependencies, and the model is used to generate synthetic time series describing the errors in a production sensor which can be used for more realistic simulations. The model is trained on a data set collected from real roads with various traffic settings, and yields significantly better results as compared to previous works.
Thermal Buffering Effect of Phase Change Material on Press-pack IGBT during Power Pulse

Managing transient temperature of power semiconductor devices induced by power pulses is a challenge to thermal design in power electronic packaging. Due to the limited thermal capacity, the extra heat generated by overload current cannot be absorbed immediately, leading to junction temperature rising beyond the limit and thermal breakdown of the device. Integrating phase change material (PCM) into the packed power devices can be effective to suppress the junction temperature surges by making use of the latent heat. In this paper, the PCM-integrated packaging of a press-pack IGBT device is proposed, and the thermal buffering effect of PCMs is analyzed using both finite element analyses and experiments. It is shown that as the dissipation of extra heat is absorbed by the PCM, the junction temperature rise is significantly reduced during power pulses. The proposed packaging structure contributes to improve the short-term overcurrent capability of press-pack IGBT-based devices and the corresponding gridconnected converters in many grid scenarios.
Grid-Based Micro Traffic Prediction using Fully Convolutional Networks

We propose an approach for the model-free prediction of multi-lane traffic scenes using Fully Convolutional Networks (FCN). In order to generalize the applicability of the approach, we use a grid-based, sensor-agnostic input representation. Positioning, velocity, heading, turn indicator as well as information about occluded areas are stacked on the road layout creating a multi-layer input for the FCN. Training and evaluation data is acquired using a simulation that provides plausible vehicle movement for vehicle following, lane changing and overtaking. We demonstrate that the FCN architecture is able to implicitly learn the underlying vehicle kinematics, cooperative driving behavior as well as track vehicles through occluded areas. We further propose an iterative training process that outperforms single step predictions and is also suitable for unsupervised learning. The impact of loss representation, network depth and input resolution are evaluated to point out further improvements. As an extension, we evaluated the epistemic uncertainty obtained via Monte Carlo dropout in an approximation of a Bayesian Neural Network that also yields important information about prediction inaccuracy and possible multi-modalities.
Modified Current Controllers for Closed-loop Control of Induction Motor with LC Filter

Low-pass filtering of pulse width modulated (PWM) output voltages of a voltage source inverter (VSI) by sinusoidal LC filters is an established solution for squirrel cage induction motor (SQIM) drive. It reduces high dv/dt, bearing current, leakage current through ground, and core losses in the motor. However, it also deteriorates the performance of the rotor-flux oriented control (RFOC) of SQIM because of sustained resonant frequency oscillations. Additional proportional-integral (PI) controllers or active damping (AD) techniques are generally used to refine the performance of the SQIM drive with filter. This paper proposes new q- and d-axis stator-current controllers, which are basically the PI controllers with time delay. It does not require any change in the controller parameters previously calculated for RFOC of SQIM when there was no output LC filter. It successfully damps out the high amplitude oscillations at resonant frequency without using any AD technique or additional PI controllers and thus assures the suitable performance of the drive. The proposed controller has been discussed with mathematical expressions and its effects have been shown through stability analysis. The stability of the drive in RFOC with the modified controllers (MC) has been analyzed in details about an operating point. Multiple simulation and experimental results have also been presented to exhibit its performance at various operating conditions.
Study on wake-up receiver design for IoT applications

Internet of Things is arising as the technology of the future where an estimate of many of devices around us will be connected to the internet. When all of these nodes and sensors get connected to the cloud, a huge portion of their energy is consumed by their wireless communication systems. However, most of them have a highly constrained power budget. With billions of data being transmitted through the IoT nodes, the specifications instruct an ultra-low power design with secure communication. Thus, this paper provides an overview of the design considerations and challenges of wake-up receivers as well as an analysis of state-of-the-art research. A comparison of some existing architectures is also detailed. Perspectives and future work to optimize wake-up receiver limitations are presented.
Fusion of Camera and Lidar Data for Large Scale Semantic Mapping

Current self-driving vehicles rely on detailed maps of the environment, that contains exhaustive semantic information. This work presents a strategy to utilize the recent advancements in semantic segmentation of images, fuse the information extracted from the camera stream with accurate depth measurements of a Lidar sensor in order to create large scale semantic labeled point clouds of the environment. We fuse the color and semantic data gathered from a round-view camera system with the depth data gathered from a Lidar sensor. In our framework, each Lidar scan point is projected onto the camera stream to extract the color and semantic information while at the same time a large scale 3D map of the environment is generated by a Lidar-based SLAM algorithm. While we employed a network that achieved state of the art semantic segmentation results on the Cityscape dataset [1] (IoU score of 82.1%), the sole use of the extracted semantic information only achieved an IoU score of 38.9% on 105 manually labeled 5x5m tiles from 5 different trial runs within the Sendai city in Japan (this decrease in accuracy will discussed in section III-B). To increase the performance, we reclassify the label of each point. For this two different approaches were investigated: a random forest and SparseConvNet [2] (a deep learning approach). We investigated for both methods how the inclusion of semantic labels from the camera stream affected the classification task of the 3D point cloud. To which end we show, that a significant performance increase can be achieved by doing so - 25.4 percent points for random forest (40.0% w/o labels to 65.4% with labels) and 16.6 in case of the SparseConvNet (33.4% w/o labels to 50.8% with labels). Finally, we present practical examples on how semantic enriched maps can be employed for further tasks. In particular, we show how different classes (i.e. cars and vegetation) can be removed from the point cloud in order to increase the visibility of other classes (i.e. road and buildings). And how the data could be used for extracting the trajectories of vehicles and pedestrians.
High Torque Density Fractional-Slot Concentrated-Winding Axial-Flux Permanent-Magnet Machine with Modular SMC Stator

In this paper the design and implementation of a single-stator double-rotor fractional-slot concentrated-winding (FSCW) axial-flux permanent-magnet (AFPM) machine with modular Soft magnetic composites (SMC)stator and oil immersed forced cooling are presented. Shorter end-winding length and higher fill factor with rectangular copper wire can be realized by employing the yokeless and segmented stator. A fully sealed stator with oil-immersed cooling is proposed to enable a 20A/mm2 current density in the armature windings for stable operation. Meanwhile, the robustness of stator and rotor are taken into account due to the stator modularity weakening effect on strength. The FEA is used to validate the electromagnetic and mechanical performance. The construction and experimental verification of a full-scale 30kW prototype are elaborated to verify the predicted performances in terms of torque, efficiency and cooling.
Semantic Segmentation And Segmentation Refinement Using Machine Learning Case Study: Water Turbidity Segmentation

Classical methods for image segmentation such as pixel thresholding, clustering, region growing, maximum likelihood have been used regularly and relied on for a long time. However, these classical methods have limitations, particularly on images where there are many overlapping pixel values between features, which is common in remote sensing images. The advent of machine learning, in particular, deep learning in computer vision and image analysis, has gained interest in the remote sensing field. Current deep learning architecture has been able to achieve high accuracy for image recognition, object detection, and segmentation. This study performed image segmentation on the coastal area with high water turbidity using Landsat-8 images. Currently, the standard tool to derive water turbidity data from Landsat-8 images is the level-2 plugin of SEADAS software. However, due to its rigorous processing method, the processing time using SEADAS Level-2 Plugin is quite long; for example, processing one Landsat-8 image took around 8 hours. As a consequence, the amount of time needed to process multiple images is increasing. Deep learning has advantages once the model trained, the inference or prediction process is quite fast. Therefore it has the potential to be used as a complementary tool to predict and segment high turbidity areas, because in deep learning. In this study, we implemented U-Net architecture with ResNet connection and used Generative-Adversarial Network (GAN) to refined segmentation results.
Application of Improved Particle Algorithm in Wireless Network Power Energy Efficiency

A power allocation algorithm based on route security connection probability in the paper is proposed. Through the improvement of the particle algorithm, the particle swarm with the largest safe capacity is selected to realize the relatively safe low-power routing energy-saving scheme. The algorithm not only improves the security of information transmission by maximizing the probability of routing secure connections, but also reduces the power consumption of the system. Not only that, because the reduced computational complexity, it has very good achievability and scalability, and is equally applicable to very large networks. The simulation test found that the performance comparison results from the total power of the route transmission and the probability of the secure connection can prove the superiority of the research scheme.
Human-like Highway Trajectory Modeling based on Inverse Reinforcement Learning

Autonomous driving is one of the current cutting edge technologies. For autonomous cars, their driving actions and trajectories should not only achieve autonomy and safety, but also obey human drivers’ behavior patterns, when sharing the roads with other human drivers on the highway. Traditional methods, though robust and interpretable, demands much human labor in engineering the complex mapping from current driving situation to vehicle’s future control. For newly developed deep-learning methods, though they can automatically learn such complex mapping from data and demands fewer humans’ engineering, they mostly act like black-box, and are less interpretable. We proposed a new combined method based on inverse reinforcement learning to harness the advantages of both. Experimental validations on lane-change prediction and human-like trajectory planning show that the proposed method approximates the state-of-the-art performance in modeling human trajectories, and is both interpretable and data-driven.
A New Heuristic Algorithm for 2-Optimality Consensus Determination

The use of consensus determination to solve problems is becoming increasingly common. The consensus determination of collectives, in particular, large collectives, is frequently extremely time-consuming. To resolve this problem, a multi-step consensus method is utilised. The development of a multi-step consensus method is based on algorithms, and the efficiency of this approach depends on the efficiency of algorithms used. An equilibrium between the running time and the consensus quality is essential; therefore, the algorithm H1 is utilised in the multi-step consensus approach. In this study, a new heuristic algorithm for consensus determination is proposed. The time complexity of the proposed algorithm is more 44% lower than that of the best previous algorithms. Analysis of the experimental results shows that the difference between the consensus quality determined by the proposed algorithm and that by the algorithm H1 is not statistically significant.
Study on Parasitic Capacitance Effect in High Power Inductive Power Transfer System

This paper reveals the parasitic capacitance effect in high power inductive power transfer (IPT) coil, and indentify its impact on conductive common mode noise. Both analytical calculation and FEM simulation method are used to estimate the parasitic capacitance value, and a CM noise model of IPT system is proposed to evaluate the impact of parasitic capacitances in the inductive coils. The result shows that the stray capacitance between IPT transmitter coil and metal-shielding layer is significant and plays dominant role in CM noise propagation path. Circuit simulation and experimental validation with an 11kW IPT prototype are conducted to verify the model accuracy. This paper provides an analysis method of parasitic capacitances in IPT coil and the guideline of conductive common mode EMI design for high power IPT system. The future work aims to find various methods to suppress the severe CM noise induced by parasitic capacitances.
Influence of RF-magnetron Sputtering System Parameters on the Process of Thin Films Nanostructure Formation

Effect of a radio-frequency magnetron sputtering system parameters on the process of thin films structure formation was analyzed. Dependence of energy delivered to the growing film by bombarding ions on the magnetron parameters and configuration was studied. Main parameters determining this energy are plasma potential, substrate ion current density, substrate bias and deposition rate. It was shown, that energetic conditions sufficiently influence on the structure formation of textured coatings. The influence of discharge gap distance and substrate bias potential on formation of hafnium diboride films structure was studied.
Evaluation of Bit Error Ratio in Differential Phase-Shift-Keying Modulation Applied to VLC LED Drivers

This work presents a binary differential phase-shift keying (DPSK) modulation technique for visible light communication (VLC) systems aimed at low to moderate data rate applications. VLC uses the modulated light of LEDs to transmit data wireless. Such feature is added into artificial lighting systems by controlling the light intensity of a source. However, in most cases, modulation capabilities for VLC are employed with the addition of extra power components, which may harm efficiency and cost standards. Therefore, single switch LED drivers are preferable to provide VLC along with average current regulation. They are standard low-cost solutions that are able to perform both functions providing a satisfactory efficiency. Hence, in this work, DPSK modulation is applied to a buck converter operating in continuous conduction mode at 250 kHz, providing a raw data rate of 125 kbps. DPSK is advantageous because it overcomes possible parameters changes at the converter, along with possible duty cycle changes given by the control system of the average current for lighting. Moreover, the system performance is evaluated by means of its bit error ratio (BER) over signal-to-noise ratio (SNR), in a line-of-sight setup with variable distances between transmitter and receiver, showing a good behaviour of a BER less than 10−3 for about 8.1 $m$ in a 8.4 W LED load.
Modeling and Analysis of Data Center Power System Stability by Impedance Methods

This paper presents a study of recently experienced power system instability and resonance problems in large data centers using impedance modeling and analysis. The approach developed to analyze the problem consists of 1) modeling each power supply unit (PSU) by its input impedance and the power distribution network by an equivalent source impedance; 2) converting the 3-phase system impedance model into separate positive-, negative-, and zero-sequence impedance models by ignoring asymmetries in the network impedance; 3) determining system stability in each sequence based on the Nyquist criterion; and 4) applying matrix perturbation theory and eigenvalue analysis to account for network asymmetries. Inclusion of the zero-sequence model is necessary because of the use of a three-phase, four-wire distribution system and single-phase power supplies operating between phase and neutral. The analyses presented in the paper identifies zero sequence as the weakest link in which instability occurs first. The simplified system model and analysis also lead to new specs and design guidelines for data center power systems to assure stability. Laboratory and field measurements are presented to demonstrate the nature of this new problem and to validate the proposed method.
Micromachining for Advanced Terahertz: Interconnects and Packaging Techniques at Terahertz Frequencies

It is difficult to package and interconnect components and devices at millimeter-waves (mm-waves) due to excessive losses experiences at these frequencies using traditional techniques. The problem is multiplied manifold at terahertz (THz) frequencies. In this article, we review the current state of THz packaging and describe several novel techniques. As we will show, micromachined packaging is emerging as one of the best choices for developing advanced THz systems.
Automatic Active Compensation Method of Cross-Coupling in Multiple-receiver Resonant Inductive Coupling Wireless Power Transfer Systems

The ability to charge to multiple receivers simultaneously with a single transmitter is one of the advantages of resonant inductive coupling wireless power transfer (RIC-WPT) technologies. However, in multiple-receiver RIC-WPT systems, each receiver often suffers from the magnetic coupling among receivers, i.e., cross-coupling. The cross-coupling not only complicates the control of the receivers but also significantly decreases the output power of each receiver. Hence, the purpose of this paper is to propose a compensation method of the influence due to the cross-coupling. To achieve this purpose, we first analyze the requirement to compensate for the influence of the cross-coupling. As a result, we reveal that it is necessary to control the phase of the current in all the receiver to be orthogonal to the phase of the transmitter current. Then, we propose the method to adjust the current phase of each receiver automatically to the desired phase by using only the phase information of the transmitter current, which results in the full compensation of the influence due to the cross-coupling. Furthermore, the proposed method also compensates for the influence of a detuning of the resonant frequency of each receiver due to the natural tolerance. Experiments of a two-receiver RIC-WPT system successfully verify the effectiveness of the proposed method.
High Efficiency single-phase Dual-Active-Bridge AC/AC Converter

This paper presents a 4 kVA single-phase isolated (230V/230V) ac/ac Dual Active Bridge (DAB) converter prototype suitable for Solid State Transformer (SST) applications. A solution for inductive load is introduced and a novel control scheme based on gain scheduling approach is employed to improve the DAB overall efficiency. Efficiency optimization is done based on global Zero-Voltage Switching (ZVS) analysis of the DAB converter operating range for dual phase-shift modulation. In this analysis, minimization of both the Reactive Power per Switching Cycle (RPSC) and the rms current of the high frequency inductance is taken into consideration to get the optimal modulation parameters. Simulation results are presented to validate the proposed control scheme.
Robust Allocation of Residential Solar Photovoltaic Systems Paired with Battery Units in South Australia

A huge portion of solar photovoltaic (PV) systems is installed in residential sector, recently. Considering the uncertain nature of PV generation, battery units are coupled with these systems to provide a better performance when trading power with upstream network. Although, pairing PVs with battery units (PV-battery) provides a promising operation, the uncertainties of load and solar radiation poses a noticeable effect on optimal sizing of such systems. Therefore, an appropriate characterization of these uncertainties is essential when sizing residential PV-battery systems. This can assist decision makers to 1) optimally design their system from both economical and operational perspectives, and 2) avoid non-optimal sizing decisions, regarding the huge investment costs of these systems. This study presents a robust optimization (RO) approach for sizing residential PV-battery systems, characterizing the uncertainties of load and solar radiation. The proposed model determines the optimal capacity of a residential PV-battery system while minimizing its operation costs considering the uncertain parameters' variations. The RO model is developed as a max-min optimization problem. According to the obtained results, the proposed robust model gives a greater sizing solution (50% increase in PV capacity), which is more reliable when the uncertain parameters deviate from the forecasted values.