Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Modulated Bidirectional Resonant DC/DC Converter for High-Voltage Bus-Based Energy Storage Systems

In high-voltage bus-based energy storage systems, an isolated bidirectional dc/dc converter is required to link the low voltage energy storage unit and the high-voltage bus. This paper proposes a series resonant dc/dc converter for this specific application. In the proposed converter, the step-up ratio is boosted by an active voltage quadrupler rectifier on the high voltage side. Thus, a transformer with a moderate turns ratio can be employed. Moreover, the voltage stresses of high voltage side MOSFETs are significantly reduced in comparison with conventional active rectifiers. This facilitates an easier selection of low-cost and highly-reliable Si MOSFETs. To optimize the converter performance, a unique phase-shift and frequency hybrid modulation scheme are proposed. The frequency modulation tightly regulates the output power, while the phase-shit modulation minimizes the conduction loss. A 500 W converter prototype, linking an 80 V-120 V battery pack and 760 V dc bus, is designed and tested to verify the concept.
Shooter Localization with a Microphone Array Based on a Linearly Modeled Bullet Speed

This paper addresses the problem of shooter localization using a single microphone array. Muzzle blast and shock wave are the two impulsive sounds generated by a projectile moving at supersonic speed. If a microphone array measures both sound waves, the shooter state in terms of shooter position and firing direction can be determined. Often, the projectile velocity is assumed to be constant. In this paper, the bullet speed is approximated by a linear model. For this model, an estimator of the shooter state is proposed and the corresponding Cramér-Rao bound is derived. Both cases with and without consideration of the deceleration are studied in Monte Carlo simulations and compared with the corresponding Cramér-Rao bound. The simulation results reveal that a superior state estimation accuracy can be achieved by using the considered projectile model.
Interpretation of Measured IPMSM Flux Tables for Parameter Identification

A widespread open loop torque control method for an interior permanent magnet synchronous machine (IPMSM) is using look up tables created from a flux measurement ([1], [2], [3]). To generate the calibration data a characterisation measurement is usually performed. This contribution describes methods to identify misalignment effects and evaluate the measurement data. The identified misalignment is used to correct the measurement result and to improve the calibration data. This improvement is necessary to reach the high requirements for torque accuracy, voltage usage and controller stability for an IPMSM, e.g. in hybrid drive systems. Furthermore precise calibration data can improve effectiveness of functions observing internal physical effects such as sensor less control or temperature observers.
Continuum Approximation Model for Optimizing Phased Development of Scalable Transit Corridors*

To best serve the growing demand in a long term, a continuum approximation (CA) model is formulated for multi-phase transit design in a linear monocentric corridor. The transit line can be accordingly extended as the population grows during the planning horizon (e.g., 20 years). The transit demand is derived from the commute need of households, whose housing location choices are accounted for. The objective of the formulated problem is to minimize the total transit agency cost and patrons’ travel cost over the entire planning horizon. A two-stage solution approach is developed to solve the problem. In numerical experiments, the accuracy of the proposed CA models is first validated by comparing the results against that recomputed in the corridor with specific station locations (i.e., discretization of the CA solution). The estimation errors of system cost generally remain less than 2% under various scenarios (smaller error is observed in the corridors with higher population). Secondly, we compare our model against the conventional static model (in which the transit line is optimized and built once for all). The results show that our model may not only reduce the transit agency’ cost (up to 6%), but also save the patrons’ travel cost (by 2%).
Non-uniform Spacing of Ferrite Bars for Optimizing a Solenoid-based Wireless Electric Vehicle Charger with Automatic Self-Alignment

This paper presents the design of a dual-coil charging pad for Wireless Power Transfer (WPT) in Electric Vehicles (EV). An electro-magnetic coil, denoted as the DC coil, generates a magnetic force that horizontally aligns a linear-slider-mounted transmitter pad to the receiver pad. A solenoid-based coil, denoted as the ac coil, is capable of 5kW WPT, which is performed only after the self-alignment of the pads. Two areas of optimization for the solenoid-based wireless charger are presented: optimum ferrite bar spacing and shield design. By using copper shields composed of a continuous sheet instead of multiple pieces connected with tape, the simulated eddy current loss is reduced by 29% while minimizing leakage flux from the ac coil. The optimum ferrite bar spacing is verified on a prototype of the dual-coil charging pad and allows the power capability to increase from 3.7 kW to 5 kW, without additional ferrite bars while achieving a peak DC-dc efficiency of 90.1%. The prototype of the DC coil generates a peak magnetic force of 3.51 N while also achieving a wide horizontal misalignment correction range of up to 240 mm.
The Latest in XRF Coatings Analysis Equipment for Micro-Scale Semiconductor Packaging

X-ray fluorescence (XRF) analysis is a widely used technique to measure plating thickness. Its main advantage resides in allowing simultaneous, non-destructive analysis of multilayered plated parts. The miniaturization and increasing complexity of electronic devices proliferated by advancements in smartphone functionality continues with the rise of microelectromechanical systems (MEMS). Consequently, the electronic components and features for these devices are becoming as miniscule as ever. In order to maintain the necessary quality of these components, the measuring and control of the plating thickness and composition is critical. For this application, an XRF plating thickness analyzer capable of high accuracy measurement in small areas is essential. Utilizing the latest X-ray focusing polycapillary technology with a high-sensitivity detector, modern XRF systems deliver high-precision measurements of nanometer-scale plating. The beam diameter of less than $20\ \mu \mathrm{m}$ (full width at half maximum, FWHM) not only allows plating thickness measurements, but also composition analysis, useful for solder bumps. This allows for an outstanding measurement repeatability, achieving precision of 1% RSD for gold plating at 100 Å. Furthermore, the levels of irradiated energy are lower than those utilized in wavelength dispersive techniques (WDXRF), thus considerably reducing the potential damage to the sample. This paper discusses XRF as an analytical technique for determining the thickness of plated or coated layers applied to a substrate. The effects of layer structure and properties on multilayer XRF thickness measurements are investigated.
Occupancy Grids Generation Using Deep Radar Network for Autonomous Driving

Artificial intelligence and deep learning are rev-olutionizing modern society. While deep-learning-based approaches gradually start dominating the area of the 2D image and 3D point cloud processing, traditional rule-based algorithms remain the mainstream solution in radar signal processing. To unify the approaches for image, radar, and lidar processing for autonomous driving and take advantage of neural networks, the automotive industry is actively seeking solutions for integrating radar into a deep-learning-based sensor fusion framework. In this paper, we propose deep neural networks that take raw radar data as input and generate occupancy grids within the radar’s field of view. Furthermore, we integrate the developed system in a test vehicle and demonstrate it on public roads. The proposed networks are the first step towards solving a multiple target detection problem with radar data using deep learning approach.
3-D Localization for Smartphones using a Single Speaker

In this paper, we describe a novel 3-D positioning method for a smartphone using only one external speaker. To obtain the smartphone’s 3-D position using this speaker, we apply the concept of a mirror-image speaker. For a mirror-image speaker, the primary reflected signals from the wall or floor are captured as signals transmitted from a "virtual speaker" mounted at a mirror-image position with respect to the speaker’s original position. In addition, we can solve the 3-D localization problem, which is difficult with a single microphone, and propose a 3-D localization model that uses the stereo microphones in a smartphone. Holding the smartphone sideways, we capture the signals from real and mirror-image speakers using the two built-in microphones in the smartphone. We can then calculate the smartphone’s 3-D position using the signal reception time obtained by each microphone and the baseline between the stereo microphones. In our experiments, we located the speaker and estimated the smartphone’s position at several places along a corridor. In our results, we obtained 90th-percentile errors of less than 73.23 mm for the 3-D positioning. Furthermore, we identified the limitations of localization via a single speaker using dilution-of-precision measures.
MIMO Processing for Satellites in the 5G Era

The integration of satellites in 5G networks is considered to complement terrestrial infrastructures and offer the availability and the data rates fulfilling the demands of future communication systems. This paradigm shift especially brings new challenges for the design of very high throughput satellites. In this work, the interest of the MIMO technology to meet 5G availability and data rate requirements with advanced digital satellite payloads is discussed. The advantages of the approach both for the feeder links and the user links of next-generation systems are presented.
A Hybrid Deep Learning Approach with GCN and LSTM for Traffic Flow Prediction*

Traffic flow prediction is an important functional component of Intelligent Transportation Systems (ITS). In this paper, we propose a hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA), to efficiently capture the spatial-temporal features in traffic flow. Firstly, we apply graph convolutional network (GCN) to mine the spatial relationships of traffic flow over multiple observation stations, in which the adjacent matrix is determined by a data-driven approach. Then, we feed the output of the GCN model to the long short-term memory (LSTM) model which extracts temporal features embedded in traffic flow. Further, we implement a soft attention mechanism on the extracted spatial-temporal traffic features to make final prediction. We test the proposed method over the PeMS data sets. Experimental results show that the proposed model performs better than the competing methods.
The Digital (Mission) Twin: an Integrating Concept for Future Adaptive Cyber-Physical-Human Systems

Top-down decomposition of a complex system of systems (SoS) requires representation of the behavior of multiple individuals and cyber-physical systems performing a wide variety of tasks, usually at different times and with different goals. In the future, as humans and machines learn and co-adapt in their mission context, systems engineering must develop approaches that support dynamic analysis and synthesis in both the design and operation of systems. The concept of a “digital (mission) twin” provides an integration framework for design and control of future complex cyber-physical-human SoS. The framework builds on mission function task (MFT) analysis, providing a basis for model-based systems engineering (MBSE) activities to identify and address needs for automation or other forms of advanced technology that improve overall system performance. However, human-driven adaptation and eventually machine adaptation of complex SoS poses organizational and methodological challenges. We discuss the need for and opportunities for convergence of three often segregated SE methodologies: product engineering, human systems integration, and mission/operations analysis by expanding the product or process engineering view of a digital twin to the mission level. In the present this gives us a framework to analyze and specify automation and adaptation opportunities at the mission level. In the future we envision this twin continuously operates with the real systems to manage both emergent mission conditions and lifecycle adaptation. We applied this framework to a complex military mission using a case where artificial intelligence can be incorporated widely across a set of mission tasks to significantly improve performance.
Analysis of the Effect of Various Input Representations for LSTM-Based Trajectory Prediction

The prediction of future trajectories of the surrounding traffic participants is a key component in modern autonomous driving systems. This work presents an analysis of the impact of various representations of the input data on the prediction quality. The analyzed data comprises information recorded by the ego vehicle, including object recognition and object tracking, as well as satellite images and map information. We propose a neural network utilizing long short-term memories (LSTMs) to capture the sequence-to-sequence nature of the underlying problem, as well as a convolutional neural network (CNN) to take the surroundings of the predicted object into account. The input to our network is both the past trajectory of the predicted object, as well as a bird’s eye representation of the scene surrounding the object, fusing various types of information on the scene, e.g., a satellite image and bounding boxes of other traffic participants. We achieve Euclidean distances between the predicted position and the ground truth position of 0.47 m and 6.19 m for a prediction time instant that is 1 s and 6 s in the future, respectively. Additionally, we show the potential of our approach to transfer knowledge from similar road topologies to unseen intersections.
Reliable Monocular Ego-Motion Estimation System in Rainy Urban Environments

Visual Simultaneous Localization and Mapping (SLAM) systems assume a static world. They usually fail under adverse weather conditions. In this paper, we propose a robust monocular SLAM system that is able to work under rainy conditions in urban environments reliably. To recover camera ego-motion from images with rain streaks, we apply a superpixel-based image content alignment method for the static background modelling. With coarse outputs estimated through averaging temporal matches, image details are recovered by a Convolutional Neural Network (CNN). Based on the statistic distribution of intensity variance between original and reconstructed image pairs, a robust and noise-sensitive weight function is explored for rejecting outliers when estimating camera poses. Quantitative evaluation results on the CARLA and synthetic KITTI datasets demonstrate the reliability of the proposed system and its superiority over the state-of-the-art approaches.
The Effectiveness of an SRSD Writing Intervention for Students with Epilepsy

Supervisory Committee Dr. Gina Harrison, Educational Psychology and Leadership Studies Supervisor Dr. Allyson Hadwin, Educational Psychology and Leadership Studies Departmental Member The current study investigated the efficacy of using a writing intervention based in the self-regulated strategy development (SRSD) approach for teaching paragraph writing skills to three students with epilepsy who struggled with writing. Individuals with epilepsy often have difficulties with the same cognitive processes that are involved in the writing process such as attention, working memory, and self-regulation. The study used a multiple baseline approach and participants' paragraphs were examined across the following WIAT-II paragraph scoring domains: number of words written, mechanics, organization, vocabulary, and total paragraph score. Effects on participants' self-efficacy beliefs towards paragraph writing were also examined. Results revealed an improvement in number of words written, paragraph organization, overall writing quality, and self-efficacy towards writing for all participants following the ten week intervention. Limitations to the study and implications for educators are discussed.
Audio-Visual Emotion Recognition Using a Hybrid Deep Convolutional Neural Network based on Census Transform

Over the last years, recognition of emotions based on multimodal channels has received increasing attention from the scientific community. Many application fields can benefit from multimodal emotion recognition, such as human-computer interactions, educational software, behavior prediction, interpersonal relations. Speech and facial expressions are two natural and effective ways to express emotions in human-human interaction. In this work, we introduce a hybrid deep convolutional neural network to extract audio and visual features from videos. Initially, for extracting audio data, we transform the audio signal into an image representation as input to a 2D-Convolutional Neural Network (CNN). For extracting visual data, we introduce a Census-Transform (CT) based on CNN. Then, we fuse both audio and visual features, reducing them through Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Logistic Regression (LR) and Gaussian Naïve Bayes (GNB) classifiers are employed for emotion recognition. Experimental results on RML, eNTERFACE05 and BAUM-1s datasets demonstrated that our model reached competitive recognition rates compared to other state-of-the-art approaches.
Smart Households' Available Aggregated Capacity Day-ahead Forecast Model for Load Aggregators under Incentive-based Demand Response Program

The rapid development of smart grid and smart appliances helps those smart households (SHs) more actively participate in the incentive-based demand response (IBDR) programs. As the agent facilitating the SHs' participation in the IBDR program, load aggregators (LAs) need to comprehend the available SHs' demand response (DR) capacity when trading with the system operator in the day-ahead market. This paper proposes a forecasting model aiming to aid LAs forecast the available aggregated SHs' DR capacity in the day-ahead market. Firstly, a home energy management system (HEMS) is implemented to perform an optimal scheduling for SHs and to model the customers' responsive behavior in the IBDR program; secondly, a customer baseline load (CBL) estimation method is applied to quantify the SHs' aggregated DR capacity during DR days; thirdly, several features which may have significant impacts on the aggregated DR capacity are extracted; finally, a support vector machine (SVM) based forecast model is proposed to forecast the aggregated SHs' DR capacity in the day-ahead market.
A Symmetrical Transformerless Hybrid Converter with Leakage Current Suppression

Considering the high efficiency and flexibility, the transformerless hybrid converters with multiple outputs attract more and more attention in residential PV applications. However, the leakage current issue should be addressed properly to meet certain stringent safety regulations and standards. In this context, this paper presents a new symmetrical transformerless hybrid converter with simultaneous AC and DC outputs, while at the same time suppressing the leakage current. More specifically, a symmetrical boost-filter inductor is employed in the proposed converter to maintain a constant common mode voltage. The corresponding modulation methods with leakage current suppression for this converter are also proposed. Additionally, the derivation of the hybrid converter is summarized and one of its family topologies is presented as an example. Simulations are performed on a 3-kW hybrid converter system. The results have validated the performance of the proposed converter in terms of strong leakage current suppression, high power quality and also flexible reactive power injection.
Generalized Multilevel Converter in DC-DC Application

In this paper, a novel non-isolated generalized multilevel DC-DC converter aiming for applications of connecting Low Voltage (LV) DC to Medium Voltage (MV) DC in high power has been presented. The proposed converter can achieve high voltage transfer ratio between LV and MV, and reduced DC inductance and output capacitance requirement by using low voltage power electronic devices with series interleaved switching techniques. The operating rules have been established and the switching states and switching sequences have been analyzed and selected. The design consideration and simulation results are presented for a 2 kW prototype with a voltage transfer ratio of 8. A 2 kW, 4-level converter has been designed and fabricated to validate the proposed converter. Experimental results are reported at 100 V input, 2 kW load.
Active Compensator for Multi-Paralleled Grid-Tied Inverters under variable Grid Conditions

To suppress the dynamic interactions between distributed multi-paralleled grid-tied inverters regarding unknown grid impedance, this paper proposes two kinds of active compensators. The ones, named as local compensators, are connected in series at the local inverter outputs to enhance the inverter output impedance and suppress the harmonic currents. The other, called as common compensator, is installed in series at the common outputs of multiple-paralleled inverters to decouple the effect of grid impedance. Thorough comparative evaluations without and with the proposed methods is conducted in the paper. Experimental results are presented to validate their effectiveness.
Seven-Level Active-Neutral-Point-Clamped Inverter Topology With Voltage Boosting Capability

This paper presents a novel seven-level inverter with boosting capability for grid-connected renewable applications. The topology is derived from a standard five-level active-neutral-point-clamped (ANPC) converter. The proposed modification includes the addition of three switches in a manner which enables the full utilization of the dc-link voltage. With the natural balancing of the dc-link capacitor voltages being preserved and the dc-link midpoint retained as the return path for the load current, eliminates the problem of ground leakage current. A simple series-parallel technique employed for balancing of the floating capacitor voltage further reduces the control complexity. Simple logic gate based pulse width modulation scheme is applied for the control of the proposed topology. A comprehensive comparison with other similar topologies is included to prove the merits of the proposed topology. A detailed simulation study is carried out to validate the feasibility and operability of the proposed topology and the results are elucidated.