Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Methodology for Analysing Interactions between an Autonomous Vehicle and other Users

The increasingly present automation of vehicles generates great concern both in the industrial and public sectors. Evidently, the autonomy of vehicles is accompanied by a large number of scientific questions: what are the real benefits of automating transport services? what are the new expected situations due to the presence of an autonomous vehicle? etc. In terms of road safety, great interest has been aroused in the interactions between autonomous vehicles and the rest of the road users (pedestrians, cyclists, motorists, etc.). Especially, there is interest in the way current road users act/react to the inclusion of this new element (the autonomous vehicle) in the road ecosystem. The definition and classification of these interactions undoubtedly influence the Operational Design Domain (ODD) as well as the defined behaviour that the autonomous vehicle must perform in any situation. In this article, the description of the methodology carried out for the identification, registration and subsequent analysis of these situations of interest is presented, which is composed of five parts: 1) Installation of fixed cameras and GPS system used for monitoring interactions, 2) detection of the autonomous vehicle (AV) and other road users, 3) estimation of AV and other users trajectories, 4) estimation of the distances between the AV and other users, and 5) interpretation of the data collected and its use to improve road safety.
Estimation of Direct Lightning Flashover Rate of Medium Voltage Distribution lines under Salt Pollution Condition

Lightning performance assessment is important to carry out the effective lightning countermeasures for medium voltage distribution lines. And thus, many studies of indirect and direct lightning performance have been carried out. In many studies, the reduction of the flashover voltage due to salt pollution of distribution insulators has not been considered. When the insulator is polluted by salt, flashover voltage of the insulators decreases, and it may cause the increase of the flashover rate due to lightning. In this paper, at first, we show the flashover characteristics of insulators considering the salt pollution. Next, we estimate the direct lightning flashover rate under salt pollution condition by Monte Carlo procedure. The calculation results show that the influence of salt pollution on the flashover rate may be relatively greater in the well-protected distribution lines.
Comparing Simulated and Real Conversations for QoE Assessments: Insights from ARKit-Based Facial Configuration Analyses

This manuscript investigates the suitability of video-based conversation simulations for studying human reactions to quality degradations, focusing on facial configurations as a proxy for QoE. We analyze data from two distinct studies: a video-simulated video-telephony scenario using the storytime dataset, where participants passively watched videos, and a second study involving real conversations between participants. In both studies, facial features were continuously recorded using Apple's iOS ARKit API. We identify a factor structure of facial features that significantly relates to participants' QoE ratings in the first study and validate its robustness by replicating it in the second, independent study. Our findings suggest statistically significant estimations of QoE ratings across both paradigms, demonstrating the suitability of passive conversation simulations for studying human reactions to quality degradation. We assess the value of the proposed approach at its present stage and conclude that it can be a valuable tool when used in conjunction with other methods, as its predictive capabilities are still not robust enough to rely solely on this analysis technique.
Audio Signal Processing in the 21st Century: The important outcomes of the past 25 years

Audio signal processing has passed many landmarks in its development as a research topic. Many are well known, such as the development of the phonograph in the second half of the 19th century and technology associated with digital telephony that burgeoned in the late 20th century and is still a hot topic in multiple guises. Interestingly, the development of audio technology has been fueled not only by advancements in the capabilities of technology but also by high consumer expectations and customer engagement. From surround sound movie theaters to the latest in-ear devices, people love sound and soon build new audio technology into their daily lives as an essential and expected feature.
Case study of an integrated expansion planning of coupled large urban electricity, gas and heat grids

In this case study, an integrated expansion planning of coupled electricity, gas and heat grids as well as coupling technologies of a large German city are presented. Based on a novel integrated planning method and a scenario on generation and load of electricity, gas and heat, the three grids and the coupling technologies are dimensioned for a future supply task. Afterwards, they are analysed regarding their suitability for renewable energy integration. This is conducted for four target images, representing a joint strategic expansion of all infrastructures towards either electrification, hydrogen use, further heat grids or holding to the status quo. To calculate the steady state and time series of the grids and the couplings, a previously published multi-energy flow calculation tool is used.
Performance of Bentonite and Zeolite Mixtures as Grounding Enhancement Materials

Grounding system is essential in order to provide protection and to reduce the risk of severe electrical shock to the personnel as well as power system equipment during both normal and fault operation. A properly secured grounding system will enable the fault current to be dispersed to earth at the shortest time possible. Therefore, it is important to have a good grounding system in both normal and fault conditions, and one way to achieve that is by adding NEMs to treat its surrounding soil, where Bentonite and Zeolite were considered in this study. These two NEMs were mixed with water and their performances were compared to a Reference grounding system where there is no NEM installed in the vicinity of the ground conductor. The performance of such grounding systems were measured by the percentage reduction of their earth resistances, which were measured daily for 150 days, i.e. from 19th December 2016 till 18th May 2017 where Fall-of-Potential technique was employed. Note that in this work, it has found that the best grounding system in the descending order were Bentonite 100%, Bentonite and Zeolite Mix A, Bentonite and Zeolite Mix B, Zeolite 100% and References grounding systems. However, being Mix A was having a slightly better performance compared to its counterpart Mix B, it would be interesting to further engage with various mixtures of Bentonite and Zeolite as grounding enhancement materials for future works. Note that Bentonite ratio was more in Mix A compared to Mix B.
Enhanced mechanical damping induced by non-linear magnetization dynamics

Mechanical resonance properties of a microcantilever made of a magnetic insulator have been investigated by laser-doppler vibrometry under DC magnetic fields and microwaves. At ferromagnetic resonance conditions, reduction of magnetization along the DC field was detected as a change in the mechanical resonance frequency. Above a microwave power threshold at which the change in the mechanical resonance frequency deviates from the linear dependence on the power, the mechanical damping was found to be enhanced steeply. The result suggests that the non-linear magnetization dynamics gives rise to additional dissipation in the mechanical motion.Mechanical resonance properties of a microcantilever made of a magnetic insulator have been investigated by laser-doppler vibrometry under DC magnetic fields and microwaves. At ferromagnetic resonance conditions, reduction of magnetization along the DC field was detected as a change in the mechanical resonance frequency. Above a microwave power threshold at which the change in the mechanical resonance frequency deviates from the linear dependence on the power, the mechanical damping was found to be enhanced steeply. The result suggests that the non-linear magnetization dynamics gives rise to additional dissipation in the mechanical motion.
Online Real-Time Rotating Unbalance Forecast Incorporating Model-Based with Machine Learning Techniques

Prognostics and Health Management (PHM) is a promising method of fault diagnosis for making maintenance decisions. For system fault development trends, different statistical or machine learning methods are being used. Unbalance is a fault that causes excessive vibrations in rotary systems, yet it cannot be totally eliminated. Thus, monitoring, and timely maintenance are needed, and this has been a research topic for years. This research forecasts rotating system unbalance faults using machine learning and system mathematical models. A machine-learning-based prognostic approach for unbalance faults in rotary systems is developed. Furthermore, operational datasets from a local petrochemical company on an overhung rotor system are utilized to validate the results. The proposed model is compared with other machine learning or statistical-based models for accuracy using the least root mean square error (RMSE) as the performance criterion. The proposed method has been proven feasible for industrial rotor unbalance prognostics.
Maximum Resolution Based Method for Balancing Capacitor Voltages in 7-Level Single Phase Flying-Capacitor Wavelet Modulated Inverters

This paper develops and tests a method for balancing capacitor voltages in 7-level flying-capacitor (FC) inverters, which are operated by the wavelet modulation (WM) technique. This multi-level inverter has two capacitors in each pole, whose voltages deviate due to changes in its loading level. In order to ensure that all switching elements experience identical voltage stresses and no circulating currents, the voltage across each capacitor has to be maintained very close to its reference value. The proposed method to balance the capacitor voltage is based on adjusting the scales of resolution segmented wavelet basis functions, which are used as switching signals to operate a 7-level FC inverter. The adjustments of the scales can vary the widths and locations of switching pulses produced by the WM technique. The proposed capacitor voltage method is structured using a proportional-integral controller to adjust the scales, thus balancing the capacitor voltages. The accuracy, effectiveness, and response speed of the proposed method to balance capacitor voltage are demonstrated through simulation and experimental test cases.
MPPI Control of a Self-Balancing Vehicle Employing Subordinated Control Loops

Recently published nonlinear model-based control approaches achieve impressive performances in complex real-world applications. However, due to model-plant mismatches and unforeseen disturbances, the model-based controller’s performance is limited in full-scale applications. In most applications, low-level control loops mitigate the model-plant mismatch and the sensitivity to disturbances. But what is the influence of these low-level control loops? In this paper, we present the model predictive path integral (MPPI) control of a self-balancing vehicle and investigate the influence of subordinate control loops on closed-loop performance. Therefore, simulation and full-scale experiments are performed and analyzed. Subordinate control loops empower the MPPI controller because they dampen the influence of disturbances, and thus improve the model’s accuracy. This is the basis for the successful application of model-based control approaches in real-world systems. All in all, a model is used to design a low-level controller, then its closed-loop behavior is determined, and this model is used within the superimposed MPPI control loop ’ modeling for control and vice versa. Videos of the self-balancing vehicle are available at: https://tinyurl.com/mvn8j7vf
Impact of Radiologist Experience on Medical Image Quality Perception

Low quality medical images can lead to inaccurate interpretation and diagnosis. Therefore, it is important to understand radiologists' perception of distortions in visual content. In this study, 12 radiologists with different degrees of experience scored MRI images of varying levels of quality. Statistical analyses were conducted to reveal the influence of the radiologists' experience on their perception of image quality. In scoring images of joints, brain and liver, the highly experienced radiologists gave significantly higher scores than less experienced radiologists. In scoring images of fetus and spine, there were no significant differences in scores between groups with different degrees of experience. No radiologists had expertise in breast images, and their experience in other anatomical areas did not significantly affect scoring of breast images. Overall, highly experienced radiologists gave higher scores for images with edge ghosting, plain ghosting or white noise than radiologists with less experience. The findings will provide a reference for determining or improving image quality standards in clinical practice.
Information Fusion-based Cybersecurity Threat Detection for Intelligent Transportation System

Intelligent Transportation Systems (ITS) are sophisticated systems that leverage various technologies to increase the safety, efficiency, and sustainability of transportation. By relying on wireless communication and data collected from diverse sensors, ITS is vulnerable to cybersecurity threats. With the increasing number of attacks on ITS worldwide, detecting and addressing cybersecurity threats has become critically important. This need will only intensify with the impending arrival of autonomous vehicles. One of the primary challenges is identifying critical ITS assets that require protection and understanding the vulnerabilities that cyber attackers can exploit. Additionally, creating a standard profile for ITS is challenging due to the dynamic traffic pattern, which exhibits changes in the movement of vehicles over time. To address these challenges, this paper proposes an information fusion-based cybersecurity threat detection method. Specffically, we employ the Kalman filter for noise reduction, Dempster-Shafer decision theory and Shannon’s entropy for assessing the probabilities of traffic conditions being normal, intruded, and uncertain. We utilised Simulation of Urban Mobility (SUMO) to simulate the Melbourne CBD map and historical traffic data from the Victorian transport authority. Our simulation results reveal that information fusion with three sensor data is more effective in detecting normal traffic conditions. On the other hand, for detecting anomalies, information fusion with two sensor data is more efficient.
An investigation into the temporal stability of CdTe-based photon counting detectors during spectral micro-CT acquisitions

Spectroscopic micro-computed tomography (CT) using photon counting detectors is a technology that promises to deliver material-specific images in pre-clinical research while suppressing noise related to dark current. Inherent to such applications is the need for a high spatial resolution, which can only be achieved with small focal spot sizes in the micrometre range. This limits the achievable x-ray fluxes and implies long acquisitions easily exceeding one hour, during which it is paramount to maintain a constant detector response. During the past years, the Medipix3RX detector has become a popular platform for research into low-flux and multi-energy CT, as it hosts a number of elaborate features that may serve as an inspiration for future systems when studied carefully. In this contribution, we illustrate our findings on how to reduce image artefacts in CT reconstructions using such a detector with a cadmium telluride sensor. We find that maintaining a constant temperature is a prerequisite to guarantee energy threshold stability. More importantly, we identify transient sensor leakage currents as a significant source to artefact formation. We demonstrate an improvement in image quality when allowing the chip to compensate for abnormally high leakge currents. The reduction of spectral resolution reported earlier for such settings is found to have no detrimental effect on material decomposition. Quite the contrary, the improved handling of transient leakage currents allows to even increase contrast-to-noise ratios in material decomposed images due to a reduced artefact level.
Interference Management in Visible Light Communications based on Reconfigurable Photodetectors for Industrial IoT

Visible light communications (VLC) have been proposed for providing connectivity in industrial scenarios where traditional radio-frequency systems are inefficient or they are even banned such as warehouses, mining industry or oil&gas plants. Specifically, providing industrial internet of things (IIoT) services requires to use high-power light emitting diodes as optical access points to satisfy the illumination requirements and simple interference management schemes that guarantee the connectivity of the IIoT devices. In this work, the concept of reconfigurable photodetector composed of multiple photodiodes following an angular diversity arrangement connected to a single signal processing chain is analyzed for ensuring satisfactory IIoT services through VLC in interference limited scenarios. Simulation results show that the proposed approach achieves a signal-to-interference ratio distribution that guarantees IIoT connectivity without the need for complex interference management schemes.
Updating Derived Relations: Detecting Irrelevant And Autonomously Computable Updates

Consider a database containing not only base relations but also stored derived relations (also called materialized or concrete views). When a base relation is updated, it may also be necessary to update some of the derived relations. This chapter gives sufficient and necessary conditions for detecting when an update of a base relation cannot affect a derived relation (an irrelevant update), and for detecting when a derived relation can be correctly updated using no data other than the derived relation itself and the given update operation (an autonomously computable update). The class of derived relations considered is restricted to those defined by PSJ-expressions, that is, any relational algebra expression constructed from an arbitrary number of project, select and join operations (but containing no self-joins). The class of update operations consists of insertions, deletions, and modifications, where the set of tuples to be deleted or modified is specified by a selection condition on attributes of the relation being updated.
Parameter Estimation and Time-frequency Distribution of Fractional Lower Order Time-frequency Auto-regressive Moving Average Model Algorithm Based on SS α Process

The performances of Time-Frequency Auto-Regressive Moving Average (TFARMA) model method degenerate under SS α distribution environment. Hence, Fractional Lower Order Time-Frequency Auto- Regressive Moving Average (FLO-TFARMA) model algorithm based on fractional lower order covariant is proposed, the parameters estimation of FLO-TFARMA model is introduced, time-frequency distribution based on FLO-TFARMA model is given, FLO-TFARMA model algorithm are compared with the existing TFARMA algorithm in detail. The simulation results show that FLO-TFARMA model method have better performance than TFARMA model method under SS α distribution environment, and the time-frequency spectrum of FLO-
Demand Response by Urban Multi-type Load Aggregators and Storage Systems in Power Grid

With the continuous growth of electricity load and renewable energy penetration, the demand for flexible regulation capacity of power grid is getting higher and higher. However, the cost and construction time of traditional energy storage is long, making it difficult to apply on a large scale. Therefore, the use of flexible load active regulation responding to the grid demand is getting more and more attention from scholars, which is called demand response (DR). In this paper, a model for load participation in DR dispatch in cities is investigated. Loads are integrated into electric vehicle load aggregator (EVLA) and building load aggregator (BLA) by type to participate in power grid optimal dispatch, thus reducing total operation cost and energy storage demand, and reducing wind and PV waste. The validity of the model is verified by cases based on real data adjustment.
A Noncooperative Stochastic Rolling Horizon Control Framework for V1G and V2B Scheduling in Energy Communities

In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs) thus acting as temporary storage systems by prosumers, which in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve their energy allocation. Prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method (ADALM). The convergence and effectiveness of the proposed approach are validated through numerical simulations under realistic scenarios.
Force Estimation using a High-Fidelity Strain Wave Gear Model

In this paper, we show that the asymmetrical, and changing, efficiency of a robot joint has significant effect on the accuracy of sensorless force estimation methods. Our work is based on a high-fidelity strain wave gear model that includes gear meshing friction forces. The meshing friction cause the efficiency of the gear to depend on the load and on whether the joint is in forward drive or backward drive. The changing efficiency is not captured by standard robot dynamic models which assume rigid joints nor by most other high-fidelity models. We use the presented gear model for sensorless force estimation using a generalized momentum observer that allows the estimation of externally applied forces on the links of the robot. The external force estimation is pivotal for the control applied during kinesthetic teaching of collaborative robots.
Automatic Detection of Myotonia using a Sensory Glove with Resistive Flex Sensors and Machine Learning Techniques

This paper deals with the automatic detection of Myotonia from a task based on the sudden opening of the hand. Data have been gathered from 44 subjects, divided into 17 controls and 27 myotonic patients, by measuring a 2-point articulation of each finger thanks to a calibrated sensory glove equipped with a Resistive Flex Sensor (RFS). RFS gloves are proven to be reliable in the analysis of motion for myotonic patients, which is a relevant task for the monitoring of the disease and subsequent treatment. With the focus on a healthy VS pathological comparison, customized features were extracted, and several classifications entailing motion data from single fingers, single articulations and aggregations were prepared. The pipeline employed a Correlation-based feature selector followed by a SVM classifier. Results prove that it’s possible to detect Myotonia, with aggregated data from four fingers and upper/lower articulations providing the most promising accuracies (91.1%).