Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Trust Using Personality Aspects Extracted from Texts

Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human’s decision-making processes. So far, there is a lack of understanding about how these aspects impact user’s trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.
Line-Start Axial-Flux PM Motors: Introduction of a New Machine Topology

This paper presents the design and analysis of an axial-flux permanent-magnet synchronous motor with a novel line-starting capability using an induction cage. This machine can be used as a higher-efficiency substitute for the induction motors. It also offers higher efficiency and lower cost compared to conventional radial-flux line-start permanent-magnet motors. This paper is a baseline study which compares the performance of the proposed design with a similar rating radial-flux motor. Both 2D and 3D finite-element analysis are used to predict the performance of the two motors under transient and steady-state conditions. A double-stator single-rotor line-start AFPM motors is also studied to increase the output power.
Object (B)logging: a Decentralized Cognitive Paradigm for the Industrial Internet of Things

This paper proposes object (b)togging, a general framework for managing object networks in the Semantic Web of Things. By applying standard supervised machine learning algorithms on raw sensor data along with non-standard semantic-based reasoning services, this approach enables resource-constrained components to produce rich and meaningful representations of real-world environments and events as in a log. The acquired knowledge is progressively enriched during object’s lifetime and exposed to the outside world as in a blog, in order to trigger complex interactions through an advanced resource discovery. A case study of predictive maintenance and early results support the feasibility of the proposal to Industrial Internet of Things settings.
A Multiscale TCAD Approach for the Simulation of InP DHBTs and the Extraction of Their Transit Times

A multiscale technology computer-aided design (TCAD) simulation methodology is presented to calculate the intrinsic transit time of InP double heterojunction bipolar transistors (DHBTs). A 2-D hyd-rodynamic (HD) simulator is employed to produce the dc characteristics and electrostatic potentials of the selected devices. Utilizing the cuts of the obtained potential profiles as inputs, a 1-D full-band, atomistic quantum transport (QT) solver is then deployed to determine the ballistic electronic transport properties of these components. The transit times of DHBTs based on InGaAs/InP and GaAsSb/InP technologies are finally investigated with the proposed approach. Good qualitative agreements are observed with the experimental measurements. The observed underestimations of the experimental transit times are justified with the physics-based compact model HiCuM, thus providing a basis for structure and performance optimizations toward real terahertz transistors.
LSTM-based Classification of Multiflicker-SSVEP in Single Channel Dry-EEG for Low-power/High-accuracy Quadcopter-BMI System

Quadcopters, typically known as drones, are being used in an increasing range of scenarios such as unmanned aerial vehicles. The goal of this research is to use electroencephalography (EEG) to establish a method for controlling drones using a brain–machine interface system based on the steady-state visual-evoked potential (SSVEP). To reduce the load on participants during a long-time usage, such a system must be simplified. The proposed method is, therefore, limited to one EEG channel. Drones can exhibit five types of movement: taking off (rising), moving forward, turning right, turning left, and landing. Participants are therefore presented with five multiflickers simultaneously. However, concerns arise over the effect on classification accuracy with using only one channel of the SSVEP. We, therefore, evaluated the classification accuracy using long-short-term memory, which is a method of deep learning that has garnered significant attention. After conducting an experiment with four healthy men, the results indicated a high accuracy of 96% on average. A second experiment was conducted in which the three participants flew actual drones in a series of movements consisting of taking off, moving forward, and landing. We subsequently compared the accuracy of those movements and the flight times.
A Multi-Intruder Robust Collision Avoidance System for Integration of Unmanned Vehicles in Civil Transport

So far, due to Aviation Authorities′ constraints for flight safety, Remotely Piloted Aircraft System (RPAS) can only fly in segregated areas, making their integration in the civil airspace a great challenge. In this scenario, the Detect and Avoid (DAA) system has been identified as a key enabling technology for the full integration of RPAS into the controlled airspace.With these motivations, the Italian Aerospace Research Centre (CIRA) is developing a Collision Avoidance (CA) module, named MIRCA (Multi-Intruder Robust Collision Avoidance), as part of a full DAA system. Such module tries to overcome key issues and limitations of the current CA proposals by deploying new concepts and effective data processing algorithms for assessing the threats to own vehicle simultaneously coming from both air traffic and fixed obstacles. Moreover, a robust (i.e. that is typically not subject to change) and optimized collision avoidance (CA) maneuver is automatically generated taking into account all above threats, sensor measurement uncertainties, air traffic reliable prediction and the own aircraft nominal or degraded (e.g., after a failure) performance limitations.
Class E Resonant Low dv/dt Rectifier Using Common Grounded Switch Controlled Capacitor

Class E resonant low dv/dt rectifier using common grounded switch controlled capacitor (SCC) is proposed. SCC is a simple method to regulate the output voltage in the rectifier side by changing the capacitance with switches. Class E resonant low dv/dt rectifier contains parasitic components of both diode junction capacitance and lead inductances which cause voltage spike at the instants of switching and increase switching loss and noise. A prototype circuit was designed, experimented, and simulated for three different load resistances. In the circuit experiments, the output voltage was maintained around 11.0 V when the load resistance changed to 30Ω, 40Ω, and 50Ω at the operating frequency 1 MHz, and the measured efficiency of the circuit was 74.9% to 83.1%.
An Efficient Preference-Based Sensor Selection Method in Internet of Things

While it is well understood that the Internet of things (IoT) can facilitate numerous applications (e.g., environmental supervision, forest fire prevention and Intelligent farming), it also brings a significant challenge for efficiently selecting sensors that meet users’ preference and specific requirement from millions of heterogeneous sensors. In this paper, we propose an improved fast nondominated sorting algorithm for efficiently preference-based sensor selection in IoT. Specifically, this proposed method mainly includes three parts: 1) Offline constructing R-tree to search sensor resources and narrowing the size of dataset according to user’s preference; 2) Using an improved fast nondominated sorting approach to get nondominated front; 3) Employing TOPSIS to characterize every sensor option of the nondominated front. In order to illustrate the usability of the model, we conduct experiments on several simulation datasets. Experimental results show that this method outperforms several baselines in terms of both response time and accuracy.
Design and Experimental Validation of a Delta Connected 36-Slot 28-Pole Permanent Magnet Machine for Hybrid Traction Applications

In this research design and experimental validation of a 28-pole permanent magnet machine for hybrid traction applications is presented. The electromagnetic design as well as thermal and mechanical considerations are summarized. Key design choices to deal with high electrical frequency are discussed in detail. It is shown that a winding connected in delta can reduce overall joule losses by decreasing the AC loss component. A full scale 42.5 kW prototype is used to validate the design. Basic, steady state and efficiency measurements are presented. Additional losses due to inverter operation are discussed. Circulating currents are measured in no load and full load operation validating the machine design.
Model Predictive Control with Space-Vector Modulation for a Grid-Connected Converter with an LCL-Filter

This paper presents an indirect model predictive control (MPC) strategy with space-vector modulation (SVM). The controller is used to control a high-power voltage-source inverter (VSI), connected to the grid via an LCL-filter. The controller evaluates a multi-variable and convex cost function over a long prediction horizon in order to determine an optimal sequence of references for a space-vector modulator. The MPC strategy is tested in a Matlab simulation. The results of the simulation show excellent steady-state behaviour as well as a fast response during transients. The controller successfully dampens the resonant frequency of the filter and exhibits low grid-current harmonic distortion.
Enhancement of Fault ride through strategy for single-phase grid-connected photovoltaic systems

Increasing efficiency and decreasing cost of Photovoltaic (PV) panels has led to the rise of many PV Plants. Considering their evolution, Grid standards require PV systems to remain connected and support the grid by providing reactive power during a fault. In order to achieve this, a control system that provides Fault-Ride-Through (FRT) capability for grid-connected PV systems is proposed. The FRT requirements, i.e., injecting a minimum 2% reactive current for every 1% change in the voltage, and limiting the current overshoot of the voltage source converter switches are met by the designed controller. During a fault, when the active power of the PV plant is decreased, the controller allows reactive power to be injected into the grid. In case the fault prolongs over a certain stipulated time as described by grid codes then the PV system is disconnected from the grid until the fault is cleared. Simulations verify that the proposed control system achieves the FRT requirements when providing FRT for symmetrical and asymmetrical faults.
A Concept for Human-Machine Negotiation in Advanced Driving Assistance Systems

In this paper a new negotiation model is introduced for the design of Advanced Driver Assistance Systems (ADAS). The objective is to establish an emancipated ADAS capable of cooperative decision making. This can be seen as a step from Shared Control to Cooperative Control. Due to the descriptive nature of negotiation theory of cooperative human decision processes, a high human user acceptance is expected if the ADAS is designed accordingly. However, conventional negotiation theory requires adaptation towards the ADAS context. The first extension enables the necessary consideration of a dynamical environment. The second extension is a new asynchronous negotiation protocol, allowing a more realistic human-machine interaction model. Furthermore a new opponent model is introduced to identify human negotiation behavior. This enables the ADAS to adapt itself during the interaction with the human driver, leading to potentially faster negotiations. Our simulation results show that a successful negotiation is possible with the proposed model, motivating further investigations in real applications.
Modified Control Algorithm for Detection and Correction of Incorrect Phase Sequence Connections of Grid-feeding Inverter

Three-phase grid-feeding inverters are widely used to inject active and reactive power into the utility grid. It is important to ensure positive phase sequence of connections because these inverters have specific control structure. Use of phase rotation indicator is a popular method to manually determine the correct phase sequence of connections. This paper proposes a modified control algorithm for three-phase grid-feeding inverters. Using this method, during black-start of the inverter, it can distinguish between correct and incorrect phase sequence connections and automatically modify control structure to ensure the proper functioning. The proposed algorithm consists of two parts: (a) identification of incorrect phase sequence connections and (b) corrections in the control structure. Current controller and switching pulses are not activated during execution of identification algorithm. After the identification of correct/incorrect sequence connections, regular/corrected current controller structure and switching pulses are activated. Simulation and experimental results are presented to establish the effectiveness of the proposed method, showing that inverters can feed power to the utility grid irrespective of correct or incorrect phase sequence connections.
Comparison of Inductive Stored Energy in the Dual Active Half Bridge with and without Auxiliary Inductor Splitting

This paper presents a study of inductive stored energy and transformer RMS currents for a Dual Active Half Bridge (DAHB) operated with and without auxiliary inductor splitting. It is shown that splitting the auxiliary inductance between transformer windings is beneficial under unbalanced loading conditions because it reduces the inductive energy required to transfer a given power when compared to the lumped case.
A Coordinated Two-Stages Virtual Network Embedding Algorithm Based on Reinforcement Learning

Network virtualization allows multiple heterogeneous networks to be deployed on the same physical network. One of its main challenges is how to effectively allocate resources to the virtual network, which is known as the Virtual Network Embedding (VNE) problem. Due to the NP-hard characteristic of VNE, most of the existing approaches are based on the heuristic algorithms tended to converge to local optimal solutions. In this paper, we propose a VNE algorithm based on reinforcement learning (RL) methods. Firstly, the VNE problem is formulated as a combinatorial optimization problem. A pointer network including two Long Short-Term Memory modules is used to set up node mapping strategies. Then, an algorithm named active search based on policy gradient is designed to optimize the pointer network, and does not need the pre-training process and the search optimal policy for VN requests. The experimental results show that the proposed algorithm can improve the performance in average physical node utilization and long-term revenue to cost ratio.
Group Wi-Lo: Maintaining Wi-Fi-based Indoor Localization Accurate via Group-wise Total Variation Regularization

Wi-Fi fingerprint-based localization is known to be prominent for indoor positioning technology; however, it is still challenging on sustainability of its performance for long-term use due to distribution drifts of the signal strength across time. Therefore, the laborious continual surveys on fingerprint are inevitable. In this paper, we propose a new scheme for solving the large cost of maintaining common Wi-Fi fingerprint-based localization with machine-learning-based way by efficient incremental learning (retraining). Specifically, we propose a brand new retraining method, called GroupWi-Lo, that focuses on minimization of parameter variation with respect to the incremental surveys on fingerprint (i.e., calibration). Our method tries to keep the parameters of the previously trained model unchanged while minimizing the error on the dataset obtained in the last surveys. This formulation is helpful to keep robustness against overfitting from the limited size of the dataset per survey. The experimental results both in the lab and the uncontrolled environment show that GroupWi-Lo achieves competitive performance among the state-of-the-art methods, while its computational cost retains independent of the number of surveys compared with existing the semi-supervised approach and standard incremental training approach.
Load-Modulation Technique for Next Generation Mobile

In this research, an asymmetrical load-modulation power amplifier is proposed and described. The designed amplifier uses two GaN HEMT transistors with 25W and 45W peak power. The designed amplifier is targeting the sub-6 GHz 5G spectrum with 200 MHz bandwidth at a center frequency of 3.4 GHz to achieve a 70 W peak power with peak efficiency at 8 dB back-off. The performance of the fabricated circuit was able to show good efficiency at the back-off region. In addition, the amplifier was tested using a continuous wave (CW) and an 8 dB peak-to-average power ratio (PAPR). The measurements results using CW reveal a power-added efficiency (PAE) of 45% at the back-off region and more than 60% achieved at the peak power. Whereas the measurements using the modulated signal achieved 42% at 40 dB average output power with a 31 dB adjacent channel power ratio. Good isolation between the amplifiers' input is needed to minimize the effect of the reflected waves on the load-modulation operation.
Enhanced Initialization for Track-before-Detection based Multibody Motion Segmentation from a Moving Camera

Vision-based motion segmentation provides key information for dynamic scene understanding and decision making in autonomous navigation. In this paper, we propose an enhanced initialization strategy which is tightly coupled to a multibody Structure from Motion (SfM) segmentation employing a monocular camera. The method relies on epipolar geometry, RANSAC formulation and motion estimation for segmenting ego-motion and eoru-motions. The proposed strategy is intended to enhance the initialization procedure employed in [1] obtaining a 50 times speed-up factor. This result is used as input for the Track before Detection (TbD) methodology which efficiently simplifies the existing multibody Structure from Motion implementation approaches. An evaluation using images from a publicly available dataset with dynamic traffic scenarios and large camera motions confirms the effectiveness of the method.
Hierarchical Regional Control for Traffic Grid Signal Optimization

Centralized traffic control in a large-scale grid is quite challenging due to the large search space of the policy. To deal with this problem, we propose a hierarchical regional control framework that can learn more quickly and efficiently, based on prior knowledge. Specifically, the traffic at intersections is controlled by local controller based on well-adjusted policies. The coordination of the local controllers is decided by a master controller that is trained by using reinforcement learning. The control of the whole grid is handled solely by learning a master policy. The master controller continuously observes the state of the traffic network and predicts the best possible traffic control strategy for the current state. In this way, the dimension of the action space is dramatically decreased, and it is much easier to explore the optimal policy. We verify our method by implementing a series of experiments in SUMO. The numerical experiments demonstrate that our method outperforms the traditional methods and the new control methods based on deep reinforcement learning in various typical scenarios. We also demonstrate that our method is easy to train and operates robustly.
Frequency Reconfigurable Antenna Array with Compact End-Fire Radiators for 4G/5G Mobile Handsets

Recently, the need of frequency reconfigurable antenna has been widely increased in the field of mobile communications due to its attractive features. A new design of reconfigurable MIMO antenna for smartphone applications is introduced in this paper. The structure of the design consists of four reconfigurable C-shaped slot radiators with miniaturized sizes etched on the corners of the mobile handsets. The reconfigurability characteristic is obtained by controlling the ON/OFF states of the integrated PIN diodes into the slot elements. The operation frequency of the antenna can work at two frequencies: 2.6 and 3.5 GHz for supporting 4G and 5G mobile terminals. A good impedance bandwidth (S11≤ −10 dB) of 400 MHz and 1 GHz have been obtained in ON and OFF states of the diodes, respectively. For S11≤ −6 dB, these values are from 2.4-2.95 GHz and 3–4.5 GHz. The efficiency characteristic of the MIMO antenna in the presence of human-hand has been studied.
Experimental Validation of Single-Stage Three-Phase Non-Isolated Cuk Rectifier

To reduce the range anxiety of EVs, three-phase topologies have surfaced. In this paper, a single stage non-isolated three-phase integrated magnetics based Ćuk rectifier is proposed. A comprehensive ANSYS analysis of the integrated magnetics for achieving ripple cancellation is delineated. The rectifier is operated using a discontinuous modulation scheme (DMS) and a dual-loop control mechanism. The controller gains are tuned using the small signal model of the Ćuk rectifier and a trade-off is required between achieving stable dynamic performance and RC damper losses. To validate the control architecture and integrated magnetics design, a 2-kW experimental prototype of the Ćuk rectifier has been designed and validated. The experimental results displaying the steady-state and transient operation at 2-kW are provided at an input rated voltage of 208 Vrms L-L. The experimental results validating rectifier operation with a THD and power factor of 1.9 % and 0.998, respectively are provided. Further, efficiency breakdown and parametric plot of efficiency with variation in output power are also provided.
DC-link Capacitor Reduction in Low Voltage and High Power Integrated Modular Motor Drives

The development of 48V high power (>15 kW) automotive propulsion drives demands close integration of the power electronics and the electrical machine. Due to the large operating currents distributed multi-phase topologies are needed and physically separating the power converter from the e-machine would involve heavy cabling and costly connections. The volume and cost of the filter capacitor represents a major challenge in closely integrated high power integrated modular motor drives and techniques are needed to minimize this. The DC-link capacitor requires a large ripple current handling capability and cost, size and reliability limitations result in suitable candidate technologies being those with low volumetric energy densities. Use of interleaved multi three-phase machines are attractive due to the associated capacitor ripple current reduction. Discontinuous modulation schemes may also be employed as a technique for reducing the current ripple. In this paper techniques for capacitor ripple reduction are assessed through simulation and compared.