Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Comparison of Traditional and JBS Integrated SiC MOSFETs in Si/SiC Hybrid Switch

The Si/SiC hybrid switch concept of paralleling a main Si IGBT and an auxiliary SiC MOSFET offers an improved cost/performance tradeoff in power converters. However, high reverse drop voltage and potential degradation of body diode in SiC MOSFET lead to high power loss and reliability problems of Si/SiC hybrid switches. To inhibit body diode conduction, the junction barrier controlled Schottky rectifier integrated silicon carbide MOSFET (SiC JMOS) in replacement of traditional silicon carbide MOSFET in the Si/SiC hybrid switch is proposed. In this work, the static and dynamic characteristics of the Si/SiC hybrid switch with SiC JMOS (JMOS pair) and the Si/SiC hybrid switch with traditional SiC MOSFET (DMOS pair) are measured and compared. The results show that JMOS pair has lower reverse conduction voltage drop, better reverse recovery performance and smaller turn-on switching losses than DMOS pair. Then a single-phase H-bridge VSI is built to compare the efficiency and thermal performances between the two hybrid pairs. Experimental results show that JMOS pair solution can achieve maximum 0.2% higher conversion efficiency and maximum 5°C lower case temperature of power switches than DMOS pair solution in same condition.
A case study of the utilization of adjunct faculty in a private university

There have been few qualitative studies examining the roles and responsibilities of adjunct faculty in higher education. These found that increased utilization of adjunct faculty raised concerns about an erosion in tenure positions for full-time faculty, lack of involvement in academic and research programs by adjunct faculty, and that institutional integrity was increasingly at risk for student learners. The literature is split between two sources: (1) concern in the growing corporatization of American colleges and universities and the shifting labor market; and, (2) collections of anecdotal stories by adjuncts reflecting their own personal histories. Increased reliance on adjunct faculty has created issues regarding labor parity and the effectiveness and quality of teaching. This research investigated the status of adjunct faculty in terms of lltili7lltjon, institutional support, inclusion, and changes in delivery of education to a broader student/consumer base. The adjunct participants for this research were those who teach composition courses at a Private University and whose terms ofhire (COUISe limitation, pay, duties, and conditions) were defined by institutional contracts for adjuncts. This research used the term "adjunct" and "part-time" faculty to apply to the same group. The participants within this group were identified as those whose livelihood mostly depended upon their employment as adjunct faculty at one or multiple institutions of higher education in permanent positions. Many wanted to teach full-time in higher education. Most of the adjunct faculty considered themselves entrepreneurs who patched together courses while many acknowledged support from their spouse's income.
Towards a Digital Future: an Innovative Semantic IoT Integrated Platform for Industry 4.0, Healthcare, and Territorial Control

One of the challenges launched by the emergence of the Internet of Things (IoT) phenomenon is the integration and harmonization of data produced by cyber-physical systems with those already present and generated by classical information systems. Things, on the other hand, are items that have physical sensors, devices actuators, or other tools to generate data and receive information from other sources. In this complex scenario, we can already talk about the Internet of Everything (IoE), an immense network of heterogeneous and complex elements. In this paper, we present a novel business solution supported by the Italian POR FESR 2014 – 2020 of Apulia Region, namely the Digital Future project, that acts as a high-level framework of applications for real-time platforms, integrating a wide variety of IoT services, leveraging data into more useful information, into interoperable industrial control networks to put humans in the loop, connecting them in a more relevant, valuable ways.
Low Inertia Grids: towards a power electronics-based power system

The energy transformation is bringing more and more power electronics into the power system. As result, we are moving from a classical electromechanical system to a fully electronic driven system. This change is significantly affecting the dynamics of the power system but also opening completely new opportunities. The presentation will introduce recent studies in the field of dynamic integration of inverters in power grids showing that power electronics can be seen more as opportunities than as a problem. While a full concept of automation for future power system is still part of research, interesting options are emerging showing how the future power electronics-based power system could be actually more reliable than the classical power system.
Novel Control Method for Ultra-High Step-Down Converter

A novel dual PWM control method for achieving an ultra-high step-down DC-DC converter is proposed. Both high-frequency and low-frequency PWM waveform generators are used in synthesizing the dual PWM control of buck converters. This resulting PWM waveform will lead to various switching cycles, and five operation modes of the buck converter can be defined accordingly. The operation principle of the proposed converter and its small-signal switched model are studied in details. One salient feature of the proposed design is that if the duty cycle of the high-frequency PWM is exactly equal to that of the low-frequency PWM, by exploring the volt-sec balance condition, the voltage gain can be expressed as the quadratic formula of the duty cycle. Thus, an ultra-high step-down conversion ratio and significant reductions of the inductor current ripple can be ensured simultaneously. Since no modifications of the conventional circuit topology are made, the proposed scheme is very cost effective. Simulation studies and hardware prototype circuit implementations are conducted to validate the proposed design with satisfactory performance.
Leakage Current Mitigation in Transformerless Z-Source/Quasi-Z-Source PV Inverters: An Overview

The Z-Source Inverter (ZSI) and quasi-Z-Source Inverter (qZSI) are single-stage configurations with high boosting ratios. They might be promising solutions for transformerless PV systems, requiring a high-step voltage conversion. The lack of galvanic isolation becomes the new challenge. High common mode voltages (CMV) may appear and thus potentially induce electrical hazards. To address this issue, topological and modulation strategies have been extensively discussed in the literature. However, it still lacks a general benchmark. In this paper, prior-art solutions for leakage currents mitigation in ZSI/qZSI-fed PV systems are reviewed. It serves to initiate further research to advance the performance of transformerless ZSI/qZSI-fed PV systems.
Stabilization of Positive Switched Linear Time-Varying Systems With Delay Under State-Dependent Switching

In this paper, we focus on the stabilization of positive switched linear time-varying systems with delay. By constructing Lyapunov-Metzler inequalities, state-dependent switching signals have been designed such that the system is asymptotically stable, even if each subsystem is not asymptotically stable. Both the continuous-time and the discrete-time positive switched linear time-varying systems have been taken into consideration. Numerical examples are also given to verify the validity of our results.
Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces

Objective: This paper aims at reducing the calibration effort of EEG-based brain-computer interfaces (BCIs). More specifically, in the context of cross-subject classification, we correct covariate shift of EEG data from different subjects, so that a classifier trained on auxiliary subjects can also be applied to a new subject, without any labeled trials from the new subject. Methods: We propose two approaches to enhance the performance of a state-of-the-art Riemannian space transfer learning (TL) algorithm: 1) trials selection, which resamples trials from the auxiliary subjects so that they become more consistent with those of the new subject; and, 2) channel selection, which reduces the number of channels and hence makes the Riemannian space computations more accurate and efficient. Results: We tested the proposed approaches on two motor imagery datasets. The results verified that they can enhance the performance of the state-of-the-art TL algorithm. Conclusion and significance: Our proposed approaches make the state-of-the-art TL algorithm more effective and efficient.
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization

Many current behavior generation methods struggle to handle real-world traffic situations as they do not scale well with complexity. However, behaviors can be learned off-line using data-driven approaches. Especially, reinforcement learning is promising as it implicitly learns how to behave utilizing collected experiences. In this work, we combine policy-based reinforcement learning with local optimization to foster and synthesize the best of the two methodologies. The policy-based reinforcement learning algorithm provides an initial solution and guiding reference for the post-optimization. Therefore, the optimizer only has to compute a single homotopy class, e.g. drive behind or in front of the other vehicle. By storing the state-history during reinforcement learning, it can be used for constraint checking and the optimizer can account for interactions. The post-optimization additionally acts as a safety-layer and the novel method, thus, can be applied in safety-critical applications. We evaluate the proposed method using lane-change scenarios with a varying number of vehicles.
Long-Term Knowledge Distillation of Visual Place Classifiers

Most of the current state-of-the-art frameworks for cross- season visual place recognition (CS-VPR) focus on domain adaptation (DA) to a single specific season. From the viewpoint of long-term CS-VPR, such frameworks do not scale well to sequential multiple domains (e.g., spring → summer → autumun → winter → ⋯). The goal of this study is to develop a novel long-term ensemble learning (LEL) framework that allows for a constant cost retraining in long- term sequential-multi-domain CS-VPR (SMD-VPR), which only requires the memorization of a small constant number of deep convolutional neural networks (CNNs) and can retrain the CNN ensemble of every season at a small constant time/space cost. We frame our task as the multi-teacher multi-student knowledge distillation (MTMS-KD), which re- cursively compresses all the previous season’s knowledge into a current CNN ensemble. We further address the issue of teacher-student-assignment (TSA) to achieve a good general- ization/specialization tradeoff. Experimental results on SMD- VPR tasks validate the efficacy of the proposed approach.
Vehicle Tracking for Heavy Road Vehicle Collision Avoidance with an Inexpensive Solid State Laser Sensor

The focus of this paper is on the development of a vehicle-tracking algorithm using a solid-state LIDAR sensor for application to Collision Avoidance Systems (CAS) for Heavy Commercial Road Vehicles. Solid State LIDARs are relatively inexpensive compared to RADARs and point cloud LIDARs, and hence could accelerate commercialization of Advanced Driver Assistance Systems (ADAS) especially in cost-sensitive markets. The suitability of an inexpensive LIDAR sensor for Rear End Collision Avoidance application is analyzed first. Then, using the measurements from the sensor, an Interacting Multiple Model filter and a linear Kalman Filter are used for estimating the longitudinal and the lateral motion variables respectively, for various classes of road vehicles. Good tracking accuracy is achieved in the lateral direction despite the sensor’s low angular resolution. The proposed estimation algorithm is first evaluated in a vehicle dynamics software, IPG TruckMaker®, and then through experiments, and the results are presented.
MulseBox: Portable Multisensory Interactive Device

This paper presents MulseBox, a new multisensory interactive device, which can simulate real-life situations in order to help learning and transfer of learning in daily-life. Indeed, multisensory interaction is known to improve learning performances. MulseBox can easily be enriched with real objects and virtual objects. It is evolving. MulseBox was designed because the literature highlighted the lack of a unique multisensory device to work on common problematics. Indeed, six disciplines at least are investigating multisensory with the shared secondary objectives to increase users’ Quality of Experience and to design for all (that is taking disabilities into account). MulseBox may also be a test-bed for researchers to solve identified problematics. An exploratory evaluation of acceptability showed promising results which make us think that MulseBox is relevant.
A 10-MHz isolated Class DE resonant DC/DC converter based on GaN HEMT

The power density of DC/DC converter could be extremely increased when the switching frequency increases to the very high frequency (VHF, 30-300MHz generally). In addition, the soft-switching characteristic of power switches could contribute to the improvement of converter's efficiency. Therefore, VHF power converters with soft-switching characteristic have the merits of high power density and high efficiency, which keep in with the developmental tendency of advanced power electronic applications. In this paper, a 10-MHz 5W isolated resonant DC/DC converter based on GaN HEMT is designed and validated. The power stage of this $20\mathrm{V}_{\mathrm{in}}/5\mathrm{V}_{\mathrm{out}}$ converter consists of a Class DE inverter, a Class E rectifier and an air-core transformer. The output voltage of DC/DC converter is regulated by ON/OFF control. The zero-voltage switching (ZVS) operating principle and design procedures are amply introduced. The results obtained from simulation and experiment prototypes illustrate that ZVS characteristic of GaN HEMT can be achieved. In addition, air-core transformer and ON/OFF control are feasible functionally.
Automatic Generation of Training Data for Image Classification of Road Scenes

There is an ever increasing demand for semantically annotated images to train and evaluate image classifiers. Currently this data is obtained by manually labeling each individual image thus making the process labor intensive and costly. We therefore present an approach that generates semantically annotated images for classes road and curb in a fully automatic way. The advantage of our method is that it relies on cues from range sensors only thus making its output suitable for training and evaluating image classifiers. We use a normal based curb detector and extract the road by running an active contour on these detections. Since our algorithm does not rely on parametric models it is possible to detect a wide range of road geometries in different environments. Sequences of up to a minute can be accurately labeled without any user interference in less than a minute.
Consideration of the Essential Topics for Role Estimation for AIWolf

In recent years, research on using artificial intelligence (AI) to play the Werewolf game has been actively conducted. The Werewolf game is a multiplayer communication game, where the players are divided into two teams who each aim to eliminate the opposing team through discussion. In the Werewolf game, a different role (possibly repeated) is assigned to each player. The role is referred to as the "Role. " The Role of a player is known only to himself/herself. Good strategies for the Werewolf game are predicting and inferring other players' Roles. The AI player that plays the Werewolf game is called "AIWolf. " In studies on AIWolf, the research on estimating the Role using machine learning has attracted a great deal of attention. However, most of the research on estimating the Role has not sufficiently discussed the required information for Role estimation, although it has described the efficiency of Role estimation. In this paper, we focus on this problem and aim to clarify the type of utterance, which is a feature that is sufficient for estimating the Role, by analyzing log files. The type of utterance is referred to as the "Topic. " First, we select the required features from the Topic based on the results of the analysis of log files and propose the features required for the estimation of the Role. Second, we design and implement a prediction model with proposed features to evaluate their effectiveness. Additionally, we also implement prediction models from related research and compare them with our model. Finally, we evaluate the effectiveness of the proposed features for estimating the Role by comparing the Role estimation accuracy for the prediction models. From the experimental results, we confirmed that the accuracy of the prediction model in this paper is higher than that of one of the prediction models in the related research, and is statistically sufficient for estimating the Role. Therefore, we demonstrated that the features selected by analyzing the log files are effective in estimating the Role of AIWolf.
Evaluation of BESS Management Strategies for Grid Primary and Enhanced Frequency Response

Maintaining the electrical power system frequency is becoming a severe challenge due to the increasing penetration of renewable generation schemes, which are highly dynamic and intermittent. System operators are investigating ways of solving this problem. The battery energy storage system (BESS) with a proper control strategy can be a suitable candidate to assist in regulating frequency. However, there are only a few reported specifications and management strategies for the use of BESS to assist the grid. This paper investigates the feasibility of two published frequency reserve strategies, and Primary Frequency Response (PFR) and Enhanced Frequency Response (EFR). A Lithium-ion (Li-ion) battery model is developed for this study. The battery cell model has been previously experimentally tested and dynamically assessed. The cell model is then expanded to a representative BESS of suitable size for future distributed power systems using published data for larger scale battery systems. This paper investigates the performance of BESS when battery terminal voltage variation, current, internal resistance and SoC when subject to PFR and EFR power demands. Four different energy management strategies are compared and alternative battery size and configuration analyzed. In addition, battery performance following existing strategies in real running conditions is examined and discussed in the paper.
Output Sine-Wave Filter Design and Characterization for a 10 kW SiC Inverter

SiC semiconductor devices allow for an increased switching speed in motor drive applications and offer large volume and weight savings for inverter design. Due to the low switching losses, the switching frequency can be extended to more than 100 kHz which allows the use of motor filters with small component size. Nevertheless, the increasing switching speed and frequency also raise concerns regarding the conducted EMI emmissions. First, this paper presents the passive component sizing for a sine-wave filter for a 10 kW SiC inverter. Afterwards, the components are characterized and modelled in detail to predict the filter attenuation up to several MHz. Finally, first practical tests carried out at full output current and reduced DC-link voltage with a passive load and then a motor and drive integrated together verify the system performance.
A PCB Integrated Winding Using a Litz Structure for a Wireless Charging Coil

The market of electric vehicles has experienced a rapid growth in the recent time due to global warming, increasing oil prices and pollution regulations. Wireless charging has gained recent attention through the increased customer convenience and the higher robustness compared to conventional charging systems. A key design factor for the vehicular coil is the limited space – thus, high power density is mandatory. This paper presents a novel planar secondary coil design. The utilization of PCBs interconnected by soldering pins results in a very cheap design while having a low height. To overcome high conductions losses, the PCB utilizes litz wires designed using FEM analysis. A three-step design procedure is described. In a first step, different litz wire geometries are compared. In a second step, the PCB's height is optimized to minimize losses for the chosen geometry. Finally, the strand width is chosen according to a loss optimization. The produced prototype shows a significant reduction of the winding losses by more than 50% compared to the non-litz design.
Smart wig for patient with chemotherapy induced alopecia: emphasis on fall detection and emergency alert

This study proposes smart wig for patient with alopecia induced by chemotherapy. Smart wig is developed mainly for health management so that it can have high usability for chemotherapy patients. Especially, we focus on the function of fall detection and emergency alert in this paper. Smart wig utilizes an acceleration sensor and a gyroscope to detect fall of a patient. We constitute a fall detection algorithm that identifies normal state and fall state based on acceleration and slope obtained from the two sensors. Also, we test the validity of the fall detection algorithm to comparison expected state with actual state.
An index of coincident indicators for the Euro Area based on monthly and quarterly time series

The paper proposes a new coincident index of business cycle for the Euro Area. The methodology we follow extends the proposal by Mariano and Murasawa (2002), which moves from the Stock and Watson probability approach and combines a set of monthly indicators with the quarterly real GDP. Nevertheless, our set-up is formulated in the levels instead of first differences of the representative series, modelling the logarithms like the original proposals. In this respect, the non-linear state space form for the treatment of temporal aggregation is developed. The application is carried out first to the well known US dataset, comparing our results with the estimates coming from the original proposals and then to the Euro Area. For the latter we use the OECD database for the quarterly series of GDP and total employment, along with the monthly industrial production and retail sales indexes. Finally, for both the applications we derive a monthly estimate of GDP.
Bevacizumab and erlotinib in the treatment of patients with metastatic renal carcinoma (RCC): Update of a phase II multicenter trial

4540 Background: Several new drugs which target signaling pathways downstream from the VHL gene product defects characteristic of clear cell RCC have shown antitumor activity in advanced RCC. We previously reported promising results with a combination of bevacizumab and erlotinib in patients (pts) with metastatic RCC. These drugs target 2 pathways critical to the growth and dissemination of clear cell RCC. We now present updated efficacy data after a median follow-up of 16 months. Methods: Eligibility criteria included: metastatic clear cell RCC, 0 or 1 previous systemic regimens, previous nephrectomy, ECOG PS 0 or 1, adequate organ function, no active CNS metastases, no bleeding abnormalities, informed consent. All pts received bevacizumab 10mg/kg IV q 2 weeks, and erlotinib 150mg po daily. Pts were evaluated for response (RECIST criteria) after 8 weeks; treatment continued until tumor progression. Results: Between 2/03 and 1/04, 63 pts were treated: median age 61 years; male/female, 50/13; ECOG 0/1, 36/.