Miracle-dz/UniFAR-FTUs huggingface dataset:
A Thermally-Based Dynamic Approach to the Load Management of Distribution Transformers

The future demand for electric vehicle (EV) charging power is expected to increase the peak loading of existing distribution transformers. In the absence of appropriate solutions, this may lead to the replacement of transformers with higher-rated types, incurring significant expense. Therefore, a novel approach is proposed to improve the management of transformer EV charging loads, avoiding or postponing the need for a transformer upgrade. It allows for the determination of whether loads that exceed transformer nameplate ratings are acceptable in terms of power level, time and duration. This is achieved with knowledge of the maximum charging duration, transformer thermal time constants, current loading levels, voltage regulation limits, oil pressure and temperature limits, and desired transformer lifetime requirements. The results of a case study are presented based on a future EV car park scenario for which an EV charging load is added to the existing base load on a 300 kVA transformer. The results show the proposed approach has advantages over the traditional approach of limiting loading to nameplate levels. These include reduced waiting time and increased levels of charging power, which are achieved without compromising the desired transformer lifetime target.
Optimal Spatial-Spectral Input For Real-Time Hyperspectral Image Classification

In this paper, we propose a novel approach for real-time hyperspectral image classification using optimal spatial-spectral input. The optimal input consisted of the main pixel with two of its spatial neighbors. We developed a lightweight deep neural network model consisting of only 5705 parameters and trained it with the well-known Pavia University dataset. Our model achieved a high overall accuracy of 96%. Furthermore, in terms of latency, our approach maximized the preprocessing speed compared to other spatial-spectral methods. The attained fast speed in preprocessing and inference is applicable for Real-time application.
Process Mining in Project Management for Smart Cities

Smart cities presuppose services, processes and infrastructures supported by highly integrated and networked technologies. The development of these products and technologies is influenced by project management approaches, which can be separated into traditional, agile and hybrid process models. Such process models include quality-gate models, Scrum, and the water-Scrum-fall-model. The most suitable process model must be chosen to ensure efficient and successful development. However, this is not an easy task, as a direct assignment of a project to a specific process model is not always possible. A potential solution for this issue lies in process mining. Process mining systematically analyses processes based on event logs. These can be used to analyse previous projects within an organisation. A model is created through the determined data of previous projects. This model selects an appropriate process model for project management based on a new project's planned data or context factors. An example is the required team size, which influences the selection of an appropriate model. This allows companies to select a process model based on their experiences and data.
Object detection and tracking aided SLAM in image sequences for dynamic environment

Object detection in a dynamic environment is crucial for accurate tracking and mapping in Simultaneous Localization and Mapping (SLAM). Dynamic feature points from objects cause unreliable performance in SLAM system. Previous researchers used varied techniques to solve this problem, such as, semantic segmentation, optical flow, and moving consistency check algorithm. In our proposal, Object Detection and Tracking SLAM (ODTS), a weighted grid-based attention model is defined for the tracking module, to track landmarks and objects feature points. Our system tracks landmarks, such as buildings in the background, and objects, such as vehicles, in the foreground. For evaluation, the trajectory of SLAM is tracked, and the accuracy is evaluated by obtaining the root mean square error (RMSE). Additionally, the number of background and foreground feature points were observed. ODTS significantly minimizes the tracking lost problem and produces more accurate maps and tracking of feature points.
Hypersurfaces with nonnegative Ricci curvature in hyperbolic space

Based on properties of n-subharmonic functions we show that a complete, noncompact, properly embedded hypersurface with nonnegative Ricci curvature in hyperbolic space has an asymptotic boundary at infinity of at most two points. Moreover, the presence of two points in the asymptotic boundary is a rigidity condition that forces the hypersurface to be an equidistant hypersurface about a geodesic line in hyperbolic space. This gives an affirmative answer to the question raised by Alexander and Currier in 1990.
Self-Supervised Confident Learning For Hyperspectral Image Change Detection

Hyperspectral images (HSIs) contain rich spectral information required by detection of multiple ground changes. HSI change detection (CD) via deep learning attracts lots of attention in recent years for its advantages in deep feature representation and nonlinear problem modeling. However, unexpected changes can be hardly labelled beforehand, which limits the application of supervised models for HSI CD. In this paper, we propose a novel self-supervised framework based on confident learning (SSCL) for HSI CD. It mainly comprises two branches, binary classification and label correction. For starters, two unsupervised methods are combined to select credible samples as the initial training set. It is used to train a feature extractor in the classification branch while being corrected by the CL and prototype selection in the correction branch. These two branches are updated alternatively as the former share the feature extractor with the latter and the latter provides credible pseudo labels for the former. Experiments on real HSI datasets have shown that our method outperforms many state-of-the-art methods.
Remote Monitoring of RF Amplifiers in HFC Networks: Voltage Drop Detection due to Power Blackouts

Hybrid Fiber–Coaxial (HFC) networks are widely used around the world to deliver voice, video, and data services. These networks are made up of both passive and active elements, most of which are managed remotely by the network operation centers. However, RF amplifiers do not have a mechanism to notify metrics or alarms that allow network operators to segment and locate the point of failure in an efficient and timely manner. This means that subscribers first detect the fault before the network operator, translating into an increase in complaints to the call center. This paper proposes an electronic system for remote monitoring of RF amplifiers in an HFC network that detects voltage drops due to power outages and notifies an alarm to the technical support staff. The results show that the designed device efficiently detects the voltage drop and sends the alarm notification through a text message, contributing to improve response times to subscribers when this type of incident occurs.
The thermal control system of the x-ray telescope eROSITA on Spektrum-Roentgen-Gamma

The X-ray telescope eROSITA is the main instrument besides the Russian ART-XC on the Spektrum-Rontgen-Gamma mission. Starting from 2014, an all-sky survey will be performed in the range between 0.3-10keV, followed by pointed observations. The main objective of thismission is the detection of 100 0000 galaxy clusters in order to constrain cosmological parameters, amongst others the density distribution and evolution of dark energy. Due to the minimum lifetime of seven years the thermal control system has to be completely passive without any consumables. With the ideal operational temperature of the CCD cameras being between 173K and 183K, this requires a very effective heat rejection system, consisting of a complex heat pipe system and a good thermal insulation. Simultaneously, a very sensitive temperature control via variable conductance heat pipes is implemented. For special outgassing requirements at the betinning of the mission these heat pipes are not working after launch but can be switched on any time. On the other hand the mirror moduules have to be tempered at room temperature and more than 200W of the electronics have to be dissipated without affecting the surrounding components or the satellite structure. The thermal control system has to be able to keep up the required temperature range and has to guarantee the optimum working conditions for all parts of the instrument. Calculations and verification tests validated the thermal concept.
Long Short-Term Memory Customer-Centric Power Outage Prediction Models for Weather-Related Power Outages

Severe weather phenomena have become more prevalent resulting in frequent and significant power disruptions and outages. Several weather-related power outage prediction models have been developed. However, most of the developed models focus on predicting outages at the utility’s equipment level, and not at the customer’s level. This paper introduces Long short-term memory (LSTM) power outages prediction models, with high prediction accuracy that have the potential to predict outages at a single customer’s location. The developed models can be deployed into smart energy agents to assist customers in preparing for outages
Updated and Adapted Curriculum and Pedagogy of Physics with the Fourth Industrial Revolution and Quantum Revolution: From Waves Principles to Quantum Mechanics Fundamentals

I, as a female educator in the field of physics, pragmatically act during a complex contemporary time and attempt to become part of new initiations and updates towards a new and more sustainable future. This paper is a demonstration of a focal point where some of my curricular and pedagogical approaches, experiences, practices in the fields of education and physics, and scholars’ theories are converged to create an updated and adapted curriculum—wave-quantum-curriculum— and pedagogy while considering two revolutions: the Fourth Industrial Revolution and the quantum revolution. I illustrate my efforts and approaches for engaging and encouraging adolescents, particularly females, for the aim of enhancing their participation and interest in learning physics, not as a school subject but as required scientific knowledge and skills for 21st-century achievements. This practical paper is concluded with the results of my studies, observations, actions, and practices from past to present toward learning the fundamentals of quantum physics by adolescents in an extracurricular and enrichment after-school science program in British Columbia, Canada. Moreover, as this study is in progress, further potential research can be emanated from this paper to learn about young students’ perspectives about the science program and the designed wave-quantum-curriculum. The extent to which the planned learning path has succeeded in supporting their contemporary mathematical science learning.
Survey on Artificial Intelligence- based Depression Detection using Clinical Interview Data

Depression is a current and significant mental illness that most people face in their daily lives. Depression affects not just the emotional state of the person but also the physical, psychological and mental state of the individual. In contrast to other diseases, depression cannot be detected by laboratory tests, remains undetected due to a lack of understanding and ignorance, and can worsen, leading to suicide. Currently, the diagnostic procedure for detecting depression adopts face-to-face interactions and self-reported questionnaires by physicians. Recognizing depression necessitates a psychiatric analysis of human behavior and social interactions. People with depression behave differently than normal people, as can be seen in the patients’ audio and video recordings. Computer vision and machine learning have been used to detect depression to fight the disease at an earlier stage. This article introduces a survey of research on the identification of depression using clinical interview data through Unimodal and Multimodal approaches.
Development of a Tool for Automated Verification of a Synchronous Generator Models Together with Turbine and Excitation System Using PMU Technology

Successful solution of problems aimed at improving the reliability of electric power systems operation and ensuring their stability depends on the correctness of electric power facilities models. An assessment of model response conformity to the actual transient in the power system can be given in the process of verifying this model using measurements of disturbance recorder or phasor measurement units. The development of the synchrophasor technology increases the observability of dynamic processes and opens up new prospects in the field of electric power systems and their elements models verification.The article is devoted to the development of a tool for automated verification of synchronous generator models together with a turbine, its control device and an excitation system. This article analyzes the existing approaches to verification and proposes a concept based on power system state replay using real synchrophasor data. The structure of the tool is described; the mathematical model of the synchronous generator is given. The article presents the results of testing the implemented tool on simulated data. Also, with the help of this tool, the turbogenerators verification installed at the power plants of the Russian Unified Energy System was carried out, through real synchrophasor data in transient.
Variance Analysis of Multi-Spectral Radar Echoes over Range for the Characterization and Classification of Objects

Radar sensors are increasingly utilized in a wide range of automotive, industrial, consumer, and other applications. The most usual tasks are to measure distances or to detect the presence of objects, and this requires often to distinguish between ‘wanted’ and ‘disturbing’ radar targets. In this contribution, a concept for multi-spectral analysis of radar echoes over range is presented. Scattering and reflection properties of radar targets are assessed by analyzing the variance of the signal energy obtained from different spectral sub-bands. An amplitude-invariant signal-to-noise ratio (SNR) parameter is suggested to be used as a quantitative feature. Prior to the analysis, an inverse filtering is performed in order to eliminate the transfer characteristic of the utilized radar system. The proposed concept has been evaluated by means of measurements with a broadband 68 to 92 GHz frequency-modulated continuous-wave (FMCW) radar system. It will be shown that the proposed method allows for a good differentiation between single, point-like radar targets and spatially distributed radar targets, and for an according classification of objects.
Methods and Tools of Video Monitoring for Remote Laboratory

The ability to monitor the progress of online experiments using a video monitoring system is one of the most important advantages of remote laboratories. In this case, special requirements are imposed on the capture area, quality and degree of image details. Therefore, the further development of methods and tools of capturing, broadcasting, receiving and displaying video images to improve the quality of educational services provided by a remote laboratory is an urgent task. In the course of the work, technologies for capturing and processing video images, implementing wireless protocols for receiving (commands, requests) and transmitting data (images), as well as architectural features, principles of software implementation and operation of video monitoring systems are investigated and realized. The developed system provides the ability to organize video monitoring for remote experiments observing and fixing. Thus, students receive the fullest information and data streaming provides the effect of physical presence at the site of the experiment. The hardware/software complex is developed on the basis of ESP-32 and ESP-32-CAM modules, as well as Wi-Fi, HTTP and WebSocket protocols. The ability to update the developed software is implemented using the OtaDrive cloud service. This makes it easier and faster to deploy and test new software. The main advantages of the proposed solution are ease of assembly, customization, availability of components, the possibility of easy modernization of both hardware and software parts of the complex, low power consumption. It has low cost, which ensures availability even for small educational laboratories. The practical value of the work lies in the fact that the created software/hardware complex can be used to organize effective video monitoring of experiments in remote laboratories, as well as for other video surveillance systems in various fields.
Analysis of social networks publications for stock market movement prediction: methodology and case study in a Spanish-speaking country

Predicting the stock market has been a problem that has caught the attention of the scientific community since any new information is quickly incorporated into the share price. Then, it has been sought to find new sources of information that can be useful for machine learning models and allow to predict with greater precision the movement of the action. One of these novel data sources has been the estimation of the mood of the population, approximating it by means of polarity analysis on Twitter. The scientific community has focused on the study of this relationship in the stock markets of the United States and China, but no work has been reported in Spanish-speaking markets. This paper presents a methodology for capturing, cleaning, and creating indexes derived from the polarity in Twitter messages, adapted to the Spanish language. For the calculation of indicators, more than 8 million tweets published in Colombia were analyzed during the period 8-2020 to 8-2021, their polarity was calculated using the set of words from the AFINN lexicon translated into Spanish. There were calculated 5 social indicators derived from the Twitter message’s polarity. The Logistic Regression, Support Vector Machines, and Artificial Neural Networks models were trained, where the latter had the best performance, with an accuracy of 58%. Then this article opens the discussion of the applicability of this type of technique in the Spanish-speaking markets.
A Technical Survey on Deep Learning and AI Solutions for Plant Quality and Health Indicators Monitoring in Agriculture

The crop production and quality face many challenges due to the loss caused by plant diseases and destruction due to pests. The traditional method of manual monitoring is not an early indicator when the span of the agricultural land is vast. Image processing is more accurate now with deep learning and computer vision techniques. The computer vision-based image classification can help identify diseases in plants at early stages from multi-angle images of the plant leaves and fruits. Such images can be auto captured by autonomous drones. The advantage of using drones as an edge device for image acquisition, filtering, and basic image preprocessing with further layers of Artificial Intelligence (AI) and deep learning to identify diseases in plants would solve the challenges faced by the human methods to a great extent. This paper surveys the state-of-the-art works done for the early detection of plant diseases using computer vision and deep learning techniques.
A Multi-Objective Optimization for Clustering Buildings into Smart Microgrid Communities

Smart microgrid communities are small groups of buildings and distributed energy resources (DERs) that collectively work to jointly enhance energy security. As a collective system, their energy performance relies on the energy consumption and generation characteristics of constituent buildings. Yet, in practice, the geographical proximity of buildings is often used as the primary clustering criteria for microgrids. In this study, we proposed a novel multi-objective formulation that expands this criterion to consider the heterogeneity and complimentary nature of building load profiles in the clustering process. Specifically, the proposed model clusters households by (i) minimizing the variation in net-energy across the building cluster, and (ii) minimizing the physical distance between buildings contained in the cluster. The study uniquely leverages the augmented ε-constraint method to efficiently populate and analyze Pareto optimal solutions within the resulting tradeoff space. To illustrate the application of the model, it is applied to cluster households within an imagined neighborhood in Ithaca, New York using the Pecan Street Inc. Dataport database. The results illustrate the ability of the proposed model to efficiently design building clusters that balance net energy and physical distance, allowing for increased utilization of DER resources.
Towards Quieter Air-Cooling Systems: Rotor Self-Noise Prediction for Axial Cooling Fans

Aeroacoustic noise is seen as a major threat to the survival of air cooling for electronics systems. The application of numerical noise prediction methods is highly constrained by computational costs. A reduced-order model (ROM) was proposed in this study to predict the rotor self-noise for axial cooling fans based on the blade element theory (BET), lifting line theory and Amiet's trailing-edge noise model. The accuracy of this ROM was validated against available benchmark case and experimental data. The ROM is able to accurately predict the fan characteristic and the broadband noise in the high frequency range (above 2 kHz), while the computational speed is more than 3 orders of magnitude than a high-fidelity simulation method, making it appropriate for low-noise fan design and optimization.
Quadrotor Inertial Navigation Aided by Vehicle Dynamic Model: A Nonlinear Observability Analysis

Previous studies have shown that quadrotor inertial navigation with aiding from a vehicle dynamic model (VDM) can be applied for short periods of flight without global navigation satellite system (GNSS) measurements. However, quadrotor inertial navigation flight experiments were performed without conducting the observability analysis which is necessary to guarantee state convergence. To investigate the claims of these studies, we conducted a nonlinear observability analysis on the recent quadrotor VDM. The analysis shows that the position of the quadrotor is unobservable, but the velocity is still observable. This analysis confirms the experimental results from previous studies that VDM-aided quadrotor inertial navigation is only applicable for short periods of GNSS-denied flight. However, a further analysis gives an interesting finding that the VDM parameters are observable provided only an inertial measurement unit (IMU) sensor. In theory, online estimation of the VDM parameters are still possible and may improve the performance of VDM-aided quadrotor inertial navigation.