Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Tendencies of Users’ Expectations about Privacy on Connected-Autonomous Vehicles

Connected-Autonomous Vehicles (CAV) is an emerging and fast-paced transportation field where diverse companies, as traditional transportation companies and software companies, compete for dominance. CAV’s benefits are promising, and this has led to an increasing interest in the literature. Despite different levels of autonomy, people might stay (to some extent) on the control of CAV, because full autonomy remains rare in transportation. Consequently, human errors are vulnerabilities that remain as potential reasons for malware infections in CAV. Usability becomes a vital attribute to mitigate the occurrence of such errors or prevent them from occurring. We aimed to understand users’ expectations of privacy towards CAV’s most autonomous levels, part of users’ satisfaction and CAV’s usability. Our survey gathered 50 responses, among 40 vehicle owners and ten non-owners. Responses showed six tendencies of users’ behavior about CAV and its privacy issues. From the tendencies, we discuss implications for the design of CAV for future works.
Optical distortions in VR bias the perceived slant of moving surfaces

The magnifying optics of virtual reality (VR) head-mounted displays (HMD) often cause undesirable pincushion distortion in the displayed imagery. Eccentrically increasing magnification radially displaces image-points away from the optical axis, causing straight lines to curve outwards. This, in turn, should affect the 3D perception of surface shape by warping binocular and monocular depth cues. Previous research has shown that distortion-induced biases in perceived slant do occur in static images. However, most use cases in VR involve moving images. Here we evaluate the impact of motion on biases in perceived slant. An HMD was used to present flat, textured surfaces that varied in slant and were either stationary, or translated laterally by the observer. In separate studies we varied the degree of distortion and evaluated the impact on perceived slant at several locations along the surface. We found that, irrespective of whether the surface was moving or stationary, distortion introduced significant bias into local slant estimates. The pattern of results is consistent with the surface appearing to be concave (as if viewing the inside surface of a bowl), as predicted from the warping of binocular and monocular cues. Importantly, the intermediate distortion level produced the same, but weaker, pattern of biases seen in the fully-distorted condition. When an appropriate level of pre-warping was applied, slant perception was veridical. Overall, our results highlight the importance of sufficiently correcting for optical distortions in VR HMDs to enable veridical perception of surface attitude.
Blockchain-Enabled Federated Learning With Mechanism Design

Federated learning (FL) is a promising decentralized deep learning technique that allows users to collaboratively update models without sharing their own data. However, due to its decentralized nature, no one can monitor workers’ behavior, and they may thus deviate protocols (e.g., participating without updating any models). To solve this problem, many researchers have proposed blockchain-enabled FL to reward workers (or users) with cryptocurrencies to encourage workers to follow the protocols. However, there is a lack of theoretical discussions concerning how such rewards impact workers’ behavior and how much should be given to workers. In this article, we propose a mechanism-design-oriented FL protocol on a public blockchain network. Mechanism design (MD) is often used to make a rule intended to achieve a specific goal. With MD in mind, we introduce the concept of competition into blockchain-based FL so that only workers who have contributed well can obtain rewards, which naturally prevents workers from deviating from the protocol. We then mathematically answer the following questions with contest theory, a novel field of study in economics: i) What behavior will workers take? ; ii) how much effort should workers exert to maximize their profits? ; iii) how many workers should be rewarded? ; and iv) what is the best proportion for reward distribution?
Glue: Enhancing Compatibility and Flexibility of Reinforcement Learning Platforms by Decoupling Algorithms and Environments

Reinforcement Learning (RL) platforms play an important role in translating the rapid advances of RL algorithms into the successes of real-world tasks. These platforms integrate multiple simulation environments, allowing testing, evaluating and finally applying RL algorithms in different scenarios. However, the algorithm code is required to execute in the same runtime system with the underlying environments, which limits platforms’ compatibility when adapting an algorithm and flexibility when switching between different algorithms. We propose GLUE to resolve this issue, by decoupling the executions of algorithms and environments first, then leveraging the RPC protocol to orchestrate a seamless workflow between them. GLUE is further implemented as a library, which hides the handling of language-specific RPCs from users. We evaluate GLUE by adapting 6 RL algorithm implementations to a representative RL platform. Compared with the baseline approach, GLUE enables algorithms to achieve competitive performance, but reduces lines of algorithm code to be changed in adaption by 27.77% , at the cost of 5.40% longer training time, on average.
DC Electrical Tree Characteristics in Epoxy Resin at Cryogenic Temperature

Epoxy resin has been widely used in the cable terminations and current lead insulation of a HVDC high temperature superconducting (HTS) power transmission system. The electrical tree is one of the main reasons for the long-term degradation of electrical insulations. This paper presents the DC electrical tree characteristics and partial discharge patterns during the growth of the trees of the pure epoxy resin at room and cryogenic temperatures. The results show that the inception probability and tree lengths under positive voltages are higher than negative voltages at both temperatures. The cryogenic temperature inhibits the initiation and growth of the electrical tree in the epoxy resin. The partial discharge measurement is performed using similar test conditions as that of the electrical tree measurement. Significantly higher partial discharges are observed during the voltage-rise and voltage-drop stages. Moreover, the higher amplitude partial discharges with lower repetition rate occur at the cryogenic temperature than the room temperature. In addition, the tree shape of the DC trees is also affected under the cryogenic temperature. During the electrical tree propagation at the room temperature, more number of lateral branches could be observed than in LN2.
A Dynamic-Inductive-Compensation-Based Method for Improving Tap-off Power from Insulated Ground Wires of Overhead High Voltage Transmission Lines

Lack of stable and high power supply for large-scale online monitoring and inspection devices, will restrict the development of intelligent operation and maintenance technology for transmission lines. Tapping power from the insulated ground wire of high-voltage overhead transmission lines by electrostatic induction and inductive compensation, is expected to be one of the possible ways to solve this problem. By taking the effects of the line sags and landform on the equivalent circuit parameters of power tapping into consideration, this paper investigates a dynamic-inductive-compensation method for improving the tap-off power from insulated ground wires of overhead high voltage transmission lines, by employing a magnetic powder core to achieve a self-adaptive negative-feedback regulation. The field-circuit coupled transient simulation is performed to verify the proposed method by varying the load resistance at the low-voltage side from $\mathbf{1}\ \Omega$ to $\mathbf{50}\ \Omega$. The simulation results show that when the equivalent capacitance decreases from 19.4 nF to 17.5 nF, the maximum tap-off power decreases from 977.6 W to 819.4 W, and the maximum ground-wire voltage increases from 35.93 kV to 37.57 kV.
Tri-Objective LPV Controller Design for the Thermal Management of Motor Drive Parameters in an Electric Vehicle

The increase in consumption of fossil fuels in transportation sector causes global warming and greenhouse gas emissions. Electric vehicles (EVs) provide an alternate solution to this combustion of fossil fuels. They use traction motor drives (mostly induction type) instead of combustion engines, but these drives suffer from parameter (stator, rotor resistance and mutual inductance) variations that result in the deterioration of drive as well as vehicular performance. The increase in the ambient and operating temperatures experienced by EV in its desired driving cycle is the major cause of such parameter variations. This paper proposes a tri-objective linear parameter varying (LPV) controller and observer design that allows these variations without affecting the performance of motor drive and vehicle. The controller design incorporates the linear matrix inequalities (LMIs) to guarantee the inherent system stability and L2 gain bound. The performance of LPV controller is compared with that of proportional-integral-derivative (PID) controller in case of motor drive and vehicular dynamics. The MATLABbased nonlinear simulations are carried out and the results are presented in terms of driveś terminal characteristics and highway fuel economy test (HWFET) drive cycle. These results ensure the excellent robust performance of the proposed control technique.
Colour Quantisation using Human Mental Search and Local Refinement

Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image which are then represented by a colour palette. Selection of appropriate entries in this palette is challenging since the quality of the quantised image is directly dictated by the palette colours. In this paper, we propose a novel colour quantisation algorithm based on the human mental search (HMS) algorithm and subsequent refinement of the colour palette using k-means. HMS is a recent population-based metaheuristic algorithm that has been shown to yield good performance on a variety of optimisation problems. In the first stage, we use HMS to find a high-quality initial colour palette. In the second stage, this palette is refined using k-means to converge towards a local optimum and thus to further improve the quality of the quantised image. We evaluate our algorithm on a set of benchmark images and compare it to several conventional and soft computing-based colour quantisation algorithms to demonstrate excellent image quality, outperforming the other methods.
Transient Stability Enhancement of a Multi-Machine Power System Using Modified Flux-Coupling-Type Superconducting Fault Current Limiters

This paper investigates the application of modified flux-coupling-type superconducting fault current limiters (SFCLs) in enhancing the transient stability of a multi-machine power system (MMPS). The mathematical model of the modified SFCLs and the transient energy function of the MMPS are established in the study. Theoretical analysis indicates that applying the modified SFCLs enables to reduce the transient energy at fault clearance time and increase the robustness of the MMPS. Furthermore, using the MATLAB platform, the simulation test of the generator rotor swing curves with and without the modified SFCLs is carried out in a benchmark IEEE 9-bus system. The simulation results verify the correctness of the theoretical analysis.
Methods of Extracting Useful Discussion Cases on Social Media for Promoting Novices’ Experience

Due to the growing importance of skills for proactively finding problems and solutions, many educational institutions provide student-centered exercises such as PBL. In particular, discussion skills are some of the most important for exchanging opinions with other students and summarizing one’s own thoughts. For inexperienced students, gaining the experience necessary to understand the flow of discussions and speak precisely is important but difficult. Currently, discussions on social media have become popular, and this is a promising source that should be utilized as a reference for unskilled people. In this research, we aim to develop a mechanism for helping unskilled students gain discussion experience on social media. Specifically, we initially develop methods for extracting from Twitter discussions that have topics similar to a discussion of interest. Moreover, we develop methods for extracting discussion structures from accumulated utterances on the basis of an analysis of utterance-response relationships. On the basis of these methods, we finally develop a system for helping students gain such experience by visualizing discussion cases. In this paper, we mainly describe the methods for extracting cases along with an overview of the support provided. In addition, we describe an experiment using actual tweets and discuss the characteristics of our methods on the basis of the results.
A 3D-Printed Lightweight and Broadband Metamaterial Absorber Made by Copper-based Conductive Composite

Metamaterial absorber (MA) with resistive pattern shows broadband absorbing performance. The PLA and 3D-printable copper-based conductive composite were selected to fabricate the perforated substrate and resistive pattern of the designed metamaterial absorber, which had the characteristics of ultrathin, low density, and broad absorbing bandwidth. Foremost, such a complex perforated structure can be fully fabricated by Fused Deposition Modeling (FDM) 3D printing, which realizes convenient fabrication of metamaterial absorber with conductive material as well as improves manufacturing accuracy and reliability.
Design and Analysis of a Dual-PM-Excited Motor Considering Harmonic Characteristics

In this paper, a dual-permanent-magnet-excited (DPME) motor is designed, where the harmonic characteristics of dual PM sources are considered respectively. And thus, the torque density can be improved by both effective harmonic designs of stator PM (SPM) and rotor PM (RPM). Firstly, based on the bidirectional flux modulation effect, the dominant airgap harmonics can be obtained. Secondly, by the analysis of harmonic characteristics of dominant harmonics, the effective harmonics excited by stator PM (SPM) or rotor PM (RPM) can be individually selected. Then, based on the harmonic characteristics of effective harmonics and the connection with design parameters, a collaborative design can be realized, which aims to promote the torque performance of DPME motor. Finally, the electromagnetic performances are investigated by the Finite Element Analysis (FEA), and the results verify the effectiveness of the proposed design in the DPME motor.
Simultaneous Line Loss Minimization and CVR with Smart Inverter Control of BESS and PV System as STATCOM

This paper presents a novel concept of system energy demand reduction by simultaneous optimization of line loss reduction and Conservation Voltage Reduction (CVR). This is accomplished by a new smart inverter control of Battery Energy Storage Systems (BESS) and PV solar system as STATCOMs, both during night and day. Both the BESS and PV systems provide dynamic voltage control utilizing inverter capacities available after real power exchange, for reactive power control. The combination of BESS and PV system enhances the total available reactive power capacity for voltage control more than with BESS alone for achieving the above-stated objective. The effectiveness of the proposed control is demonstrated on a modified IEEE 14 bus system using optimal power studies performed with PSS/E software. With the rapid growth of PV systems and BESS in power systems, the proposed combined smart inverter control of BESS and PV systems can be effectively employed to reduce overall system energy demand.
An Improved Precise Power Control of Voltage Sensorless-MPC for PWM Rectifiers

In traditional model predictive control (MPC) methods, the power prediction of the state quantity is not only limited by the measurement of the voltage sensor, but also the open-loop control. In order to boost the overall prediction accuracy without AC voltage sensor, an improved sliding mode estimation-model predictive power precision control strategy without AC voltage sensor was proposed. In this paper, the voltage on the network side is dynamically estimated by using sliding mode sigmoid function, and the system stability is proved by the Lyapunov function. On this basis, the active power and reactive power at time k+2 are calculated through the designed rolling optimization prediction link with feedback correction, aiming to reduce power pulse vibration and accurate prediction. The simulation and experimental results show that the proposed control strategy effectively suppresses the influence of grid-side harmonics on the prediction accuracy and improves the overall of the dynamic and static performance of the system.
A Fixed-Frequency Reconfigurable Holographic Leaky-Wave Antenna for Dynamically-Controlled Radiation Patterns

This work proposes a reconfigurable holographic leaky-wave antenna that offers independent and dynamic control over (i) the radiation angle (including broadside) and (ii) the radiation rate at the fixed operation frequency of 5 GHz. This is achieved by leveraging the theory of binary holography and utilizing a tunable Huygens’ metasurface as a reconfigurable holographic surface. We present a route to the physical realization of the proposed reconfigurable leaky-wave antenna and numerically verify its versatile scanning properties via full-wave simulations.
Recrystallization of Cu–In–S thin films studied in situ by energy‐dispersive X‐ray diffraction

The recrystallization of Cu–In–S thin films has been monitored in real time by means of synchrotron-based energy-dispersive X-ray diffraction. To trigger recrystallization, nanocrystalline Cu–In–S layers with [Cu]/[In] 1. The bilayer films were heated to 773 K and the evolution of the microstructure was monitored in situ via diffraction spectra. In the first step of the analysis, the diffraction data were used to identify solid-state phase transitions as a function of temperature. In a further step, single-line profile analysis of the 112 CuInS2 reflection was used to study grain growth in this material system. The recrystallization was investigated under two sulfur pressure conditions and for different [Cu]/[In] ratios. The recrystallization is composed of three steps: consumption of the CuIn5S8 phase, grain growth, and a transition from the Cu–Au-type to the chalcopyrite-type structure of CuInS2. Increasing the sulfur pressure during heating systematically reduces the temperature at which grain growth sets in. Various paths to control the recrystallization of Cu–In–S thin films are proposed.
Analysis on Frequency-Sensitive Failure Mechanism of Multi-Layer Ceramic Capacitor

The multi-layer ceramic capacitor fails abnormally at a certain discharge cycle. This study explores the frequency-sensitive failure mechanism. The test circuit whose discharge cycle was adjustable was built and four different kinds of multi-layer ceramic capacitors were tested. The failure phenomenon and the failure samples were analyzed. The failure fault tree was established. And the piezoelectric coefficient of the ceramic dielectric was tested. The analysis results show that the frequency-sensitive failure of the multi-layer ceramic capacitor is not caused by the circuit resonance, but by the piezoelectric effect. Because of the piezoelectric effect, the multi-layer ceramic capacitor in the AC electric field keeps the vibration consistent with the discharge frequency in the whole discharge process. When the discharge frequency is the same as the natural resonance frequency of the capacitor, the resonance is excited and the internal mechanical stress becomes very large, which makes the dielectric crack and leads to breakdown failure.
Comparison of Principal Component Analysis Techniques for PMU Data Event Detection

Principal component analysis (PCA) is a dimensionality reduction technique often applied to process and detect events in large amounts of data collected by phasor measurement units (PMU) at transmission and distribution level. This article considers five different approaches to select an appropriate number of principal components, builds the statistical model of the PMU data online over a sliding window of 10 seconds and 1 minute, and evaluates the computation times and the accuracy of correct event detections with use of two statistical tests in a 1-hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities collected at different PMU substations.
Analyzing Risky Behavior in Traffic Accidents

Among all the transportation systems that people use, the public traffic-ways are most common and dangerous resulting in a significant number of fatalities per day worldwide. Statistics have shown that the mortality rates related to traffic accident are more among youth. Although various road safety strategies and rules are developed by the government and law-enforcement agencies to combat the situation, these methods mainly target design, operation, and usability of traffic-ways. Most of the recent data-driven analysis papers model the traffic patterns or predict accidents from the past data. In this paper, we consider a comprehensive, year long fatality analysis reporting system (FARS) data to analyze the role of various factors related to humans, weather and physical conditions (e.g., road surface, light condition etc.) involved in traffic accidents. We build an intelligent risk prediction model that can help decision-makers to ensure road safety. The proposed model estimates (i.) the accident risk over a future time frame, and (ii.) the risk associated with the drivers present on the traffic-way based on the driver’s behavior, history, environmental conditions and physical conditions related to traffic-way.
Towards organic polymers with very small intrinsic band gaps. I. Electronic structure of polyisothianaphthene and derivatives

We present valence effective Hamiltonian (VEH) band‐structure calculations on polyisothianaphthene and some of its simple derivatives. Our goal is to rationalize the lowering of the band gap by about 1 eV which is experimentally observed in polyisothianaphthene (Eg=∼1 eV) relative to the parent polymer, polythiophene (Eg=∼2 eV). We show that the smaller band gap found in polyisothianaphthene can be understood on the basis of a relationship which can be established between the band gap energy and the importance of the effective quinoid contributions to the electronic structure. Our theoretical results also indicate that simple dimethyl, dimethoxy, or dicyano substitutions are not expected to affect the size of the band gap in any significant way.
Motor data analysis of Parkinson’s disease patients

In this manuscript, a methodology for analysing motor signals from Parkinson’s disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson’s Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.