Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Swarm Optimization Applied to the Design of an External Rotor PMSM for the Cooling System of an Electric Vehicle

This work proposes the design optimization of three permanent magnet synchronous machines (PMSMs) with external rotors and different slot-pole ratios through particle swarm optimization (PSO) algorithm. After optimization, the three machines are compared with the goal of selecting the one that best suits a specified application: a cooling system applied to the propulsion system of an electric vehicle. In all cases, the energy efficiency of the machine is set as the sole optimization objective, while performance and design constraints are incorporated into the algorithm's cost function to bring the initial design to a truly optimized condition. The equations used for the machine design are presented, as well as the methodology followed for the calculation of losses and estimation of back electromotive force. A sensitivity analysis of the optimization objective concerning the optimization variables is conducted. In the end, the results are discussed, and the machine with 12 slots and 14 poles is selected as the most suitable for the final application. It achieved an efficiency increase of nearly 4% compared to the initial design through optimization and demonstrated the highest values of efficiency and average torque among the analyzed machines.
Sinc-EEGNet for Improving Performance While Reducing Calibration of a Motor Imagery-Based BCI

The potential of motor imagery-based brain-computer interfaces (BCIs) is hindered by long calibration times. Therefore, this study investigates a classification model that minimises BCI calibration time while maximising its accuracy by exploiting transfer learning. To this end, a modified version of the Sinc-EEGNet architecture is proposed. Analyses were carried out with data from multiple subjects. Notably, when the model was trained with data from subjects other than the test subject, Sine-EEGNet-32 achieved a mean classification accuracy of 78 $\pm$ 10 %. This outperformed the reference EEGNet-4 architecture by 10 %. Instead, when considering also data from the test subject for a fine tuning, Sinc-EEGNet-32 achieved a mean accuracy of 80 $\pm10\ \%$ by exploiting only 10 % of test subject's data and 83 $+10\ \%$ by exploiting 40 % of test subject's data. These correspond to a system calibration of less than 2.0 min and of approximately 8.0 min, respectively. Overall, there was an increasing trend in performance for Sinc-EEGNet-32 as higher percentages of data were exploited for fine-tuning. In contrast, EEGNet-4 only achieved an accuracy of 72 $\pm$ 13 % even with fine tuning.
Digital Practices: Introducing Social Dimension in Digital Twins

Digital Twin is one of the key components of society's digital transformation and a promising paradigm to support the development of cyber-physical systems. Currently, researchers are investigating methodologies that exploit Digital Twins as general-purpose abstractions for complex modelling and simulation. This paper explores the idea of explicitly representing the physical and social context in cyber-physical systems through dynamic digital entities. To this aim, we introduce the concept of Digital Practice as a shared (social) digital concept, highlighting how reality is not merely described by the state of physical objects (digital twins) but also by connections among them. We illustrate this concept by exploiting an assistance scenario for the elderly.
A Data-Driven Edge Computing Solution for Real-Time Monitoring of Industrial Load Conditions

With the help of advanced information technology, the smart grid will transition to a digitized paradigm. Advanced data analytics for energy system management and optimization can serve as a key technology pillar for improving energy efficiency and reducing carbon emissions. In this paper, a data-driven condition monitoring solution running on an edge computing device is designed and implemented. Firstly, the collected heterogeneous data are transformed into features, then a clustering learning is used to learn the operating states, and finally an anomaly score metric is used to assess the operating behaviors that deviate from the normal condition. The whole process is done in a single board computer to verify the method, and the data comes from a distributed energy project in southern China. Therefore, the solution proposed in this paper can be used for real-time monitoring of industrial loads and alleviate the dependence on communication networks and aggregation management platforms.
A Method for Measuring Network Channel Delay for Virtual Power Plant Aggregation Control

Virtual power plant aggregation and control technology is an emerging method for energy dispatch and management. It is of great significance in improving the utilization of renewable energy and the reliability of power systems. In this paper, a measurement method has been proposed for idle and connected state delay based on network channels for virtual power plant aggregation and control. Firstly, a actual communication network architecture in virtual power plants has been simulated. A network model is then constructed in the OMNeT++ simulation environment to assess the delay in communication networks. Secondly, active and passive delay measurement techniques using distributed probes has been combined to analyze time synchronization technology and the transition between idle and connected states. Finally, simulation experiments verify the effectiveness and stability of the proposed measurement method in virtual power plant aggregation and control.
Energy measurement in buildings with local generation of energy

The complexity of residential electrical systems in terms of energy generation and use is every day increasing. In this context, the measurement of energy production and consumption acquires an important role. The objective of this work is to implement an energy measurement system appropriate for domestic applications in accordance with the Italian Technical Standard CEI 0-21. This system must also allow for complete integration with the other devices of the existing commercial home automation system allowing for the creation of energy saving scenarios.
Monitor screen anomaly detection and identification

In the field of video surveillance, due to signal interference, harsh environmental conditions, equipment aging and other reasons, all kinds of noise are mixed in the monitored pictures. If the abnormal picture cannot be effectively denoised, the accuracy of the subsequent identification will be reduced. For this problem, this paper proposed a improved MobileNetV2 network structure, using ECA module to reconstruct the original block, this paper proposed four different.
Minimizing ad hoc Technical Safety Assessments: Use of AHP for Prioritization of Passive Fire Protection Alternatives

Technical safety assessment is essential for ensuring the asset integrity of oil & gas production & process facilities to prevent accidents and mitigate unwanted consequences. Passive fire protection (PFP) provides technical safety assurance and reduces asset damage by explosion risk and fire spread. Applying the PFP also gives adequate time to escape and for evacuation, thereby reducing casualties and fatalities. The PFP-related decisions have been ad hoc, leading to suboptimal fire protection. This paper presents a systematic multi-criteria decision analysis approach for selecting the most viable PFP option. It demonstrates how to prioritize selecting PFP materials for an offshore topside production and process facility using the analytic hierarchy process (AHP). The PFP decision hierarchy has been developed using a real-time case study. The possible alternatives are: ‘PFP made of Cellular glass/AES fiber,’ ‘Lambda EasyFLEX/Favuseal wrap-on,’ or ‘not having PFP. ’ The decision model considers the following criteria: cost implication, platform weight, space constraints, and risk level to justify the type of PFP and avoid overkill. The findings and the methodology suggested that it is helpful for PFP practitioners to minimize ad hoc prioritization practices in the PFP-related decision analysis.
Optical Measurement Method of Electromechanical Effect Based on Fabry-Perot Interference Mechanism

The detection of electromechanical effect plays a vital role in the accurate calibration of wearable devices, microrobots, and nano-electromechanical systems. A high-precision microstrain detection system based on fiber Fabry-Perot (F-P) interference for obtaining precise electromechanical coupling coefficient was established in this article. In light of the structural characteristics of the F-P interferometer, piezoelectric (PZT) was selected as core material embedded in the sensor for forming an optical fiber F-P cavity. The deformation of the sample was detected from the wave shift in the interference spectrum by the system under step voltages. As demonstrated by the results, the fitting degree is obtained between the deformation of the sample and electric field strengths as high as that the coefficient of determination (R2) is 99.97%. The measured resolution could reach to the level of 10 pm in the meantime.
An Ontology-Based Mixed Reality Application to Support Car Reconfiguration for Drivers with Disabilities

This contribution introduces a Mixed Reality application designed and developed on Microsoft HoloLens to simulate the car configurations and driving aids necessary for drivers with physical disabilities. The application is developed relying on an ontology-based Decision Support System, which identifies proper car configurations and driving aids necessary for each driver based on their health condition, disabilities, and limitations. Each driver is able to choose a configuration from a list of all the possible configurations picked for that particular driver and see the graphical representation of the associated driving aids augmented on top of the actual car in real time. The proposed system improves the visualization of the modifications the car needs to undertake before actually modifying the car through the adaptation process.
Development of High Renewable Penetration Test Cases for Dynamic Network Simulations using a Synthetic Model of South-East Australia

Power system studies rely on trusted benchmark models to test new theories and technologies. While many such models exist for small networks, realistic benchmark models for full-scale networks tend to be less available due to data confidentiality. Synthetic models offer a potential solution to this issue by allowing the public release of network models through data obfuscation. One such model, of Australia’s National Electricity Market transmission network, has recently been made publicly available, however, it lacks details of inverter-based generation that are required for dynamic modeling. In this paper, the synthetic model is extended to include parameter values for inverter-based generators that are required for electromechanical dynamic modelling and 25 operating conditions with varying penetrations of inverter-based generation are identified based on future network expansion plans. The steady-state and dynamic performances of the derived model and operating points are analyzed.
Simulation of a New Developed PEA Cell for Flat Specimens and Full-Size Cables

In this work, a new PEA cell configuration recently proposed by Zahra et al has been implemented in a PEA cell simulation software previously developed by the authors. In addition, in the present paper, an in-depth analysis of the acoustic wave behavior within the PEA cell has been carried out. In the new PEA cell, the metallic ground electrode is replaced by a polymeric acoustic coupler, while the absorber made of the same material of the piezoelectric sensor is replaced by a metallic block. Thanks to these changes, the sensitivity is increased by a factor of four compared to the traditional PEA cell. This constitutes a very interesting advantage provided the new PEA cell configuration, especially for space charge measurements in full-size cables. Based on the above, the aim of the present work is to evaluate if the developed PEA cell simulation software is able to simulate the new PEA cell configuration and, in turn, evaluate if the simulation results confirm the advantages of the new PEA cell. For a complete study, a comparison of the acoustic waves behavior and of the PEA cell output signals for both traditional and new PEA cells, has been made.
High Static Gain Boost-Ćuk DC-DC Converter

This study presents a DC-DC converter with a high static conversion ratio, resulting from the combination of boost and Cuk topologies. This new topology achieves a high static gain by transferring the voltage from the intermediate capacitor to a switched capacitor connected in series with the output capacitor. Additionally, it is possible to obtain a voltage step-up/step-down output. Therefore, the studied converter can provide two output connections with a wide voltage range. Initially, static analysis is performed, including operation stages and the static gain curve. Finally, a prototype is built with a switching frequency of 100 kHz, a power of 1 kW, and a theoretical efficiency of 95.1 %. Modulation is carried out with an FPGA to validate the theoretical analysis.
Autonomous Wind-Turbine Blade Tracking Using A Dual-Camera System

Inspection is an increasingly critical process in the Operation and Maintenance (O&M) of an offshore wind turbine. The quality and fidelity of the data acquired during inspections has profound downstream consequences on decisions such as how to operate and when to repair the wind turbine. The acquisition of high-quality data has become a focal point of wind turbine O&M and forms the thrust of this work. We have developed an image acquisition system capable of pointing a camera with enough precision and stability to reliably acquire an entire blade surface over the course of an inspection. Doing this offshore without the possibility for remote control requires that we develop a targeting system tolerant to the communication lag associated with offshore vessels. Lack of cellular coverage and the cost of direct satellite communication requires the need of an intelligent targeting system capable of data acquisition without direct human control over long missions for many turbines.
Optimization of a Bidirectional Boost Converter for Nanogrid Applications

The electrical distribution system is undergoing a transformation from centralized to distributed generation, particularly in rural areas with limited grid access. This change is supported by advancements in key technologies, including distributed resources, renewable generators, electricity storage systems, and power electronic converters which enable the efficient and reliable integration of all energy systems within the nanogrid. Given the importance of the DC/DC converter in a nanogrid and the increasing requirements, optimizing its design is essential for improving the nanogrid's overall performance. This paper proposes an optimization method for a bidirectional boost converter that optimizes cost and efficiency as a function of switching frequency, current ripple, and other parameters. The optimization is performed using a genetic algorithm widely used in power electronics design optimization problems. Analytical models for cost and power losses are used to solve the optimization. Several solutions to the optimization problem are presented, dependent on the weight of the objective function, which can be customized to suit the user's preferences.
Current Injection Based Quasi-Static Time Series Load Flow Algorithm for Distribution System with Multiple Distributed Energy Resources

In this paper, a current injection-based QSTS load flow approach has been developed to simulate and analyze the interactions between Distributed Energy Resources (DERs) and voltage-regulating devices over a given time. The novelty is that the paper proposes a comprehensive modeling approach that takes into account the intricacies of both voltage-regulating devices and DERs. By integrating these models into the QSTS simulation, the researchers can perform detailed analyses and simulations to evaluate the effects of DERs on control devices and assess the overall impact on the grid’s stability and operation. The process involves modeling the voltage-regulating devices, such as the load tap changers and voltage regulators, in a way that accurately represents their physical characteristics and control strategies. This enables the simulation to mimic the real-world behavior of these devices under various conditions. The time series analysis has been successfully validated using a state-of-the-art QSTS solver, demonstrating that the developed approach is both efficient and accurate.
An Imbalanced Data Classification Algorithm Based on Mixed Sampling

In recent years, the classification of imbalanced data sets has always been a research difficulty in the field of machine learning. Traditional classification algorithms have the problem of low recognition rate of minority samples when dealing with imbalanced data sets. To solve this problem, this paper proposes an improved random forest algorithm based on mixed sampling for imbalanced data sets. First of all, at the data preprocessing level, the improved synthetic minority oversampling technology and the Tomek-links algorithm are combined to balance the data set. Secondly, at the algorithm level, improve the traditional voting mechanism of random forests, assign different weights to each decision tree, so that decision trees with good classification effects have larger voting weights, and improve the overall classification ability of random forest algorithms for imbalanced data. Finally, the algorithm of this paper is verified on the UCI public data set. The experimental results show that the algorithm effectively improves the recognition accuracy of minority samples and improves the comprehensive classification ability of the classifier.
The Theory of Probabilistic Hierarchical Supervised Learning for Classification

The supervised learning methods for classification are being extensively used in many application areas including health, education, industry, agriculture, and defense. Therefore, any contribution in this area has potential of application over several aspects of modern life. The theory of probabilistic hierarchical supervised learning for classification has evolved through the years with several publications to its credit. The theory introduces the problem decomposition strategy for training of classification models. The training set is decomposed into smaller hierarchical subproblems, and the model is trained for each subproblem separately. Therefore, multiple trained models are put together in a hierarchical order. The hierarchical model then can be used to classify the samples in the test set. In this paper, further enhancements are introduced to the theory like incorporation of additional mathematical operators to the models, design of hierarchical multimodal fitness function and modified data normalization scheme. In addition, basic principles of theory are further modified and redescribed with greater detail here. These modifications have enabled application of the theory to more datasets. The results are competitive with the well-known methods.
Joint User Pairing and Resource Allocation for Buffer-aided NOMA Networks

Due to the advantages of improving network connectivity and spectral efficiency, non-orthogonal multiple access (NOMA) has been regarded as a promising technology for future networks. Meanwhile, buffer-aided communication technology, can greatly enhance network throughput via adaptive traffic control and transmission scheduling, and has attracted many research attentions. In this paper, we study the achieved long-term network throughput of a buffer-aided NOMA network, wherein the data buffer stability is taken into account, and the data admission control, power allocation and user pairing policies are jointly optimized. To solve the formulated network throughput maximization problem, we use the Lyapunov optimization framework to decouple the long-term time-averaged throughput maximization problem, into multiple single-slot resource allocation and user pairing optimization problem. On the basis, we propose an online scheme which includes data control, power allocation and user pairing. Specifically, the close-formed power allocation scheme is derived with given number of NOMA users by the decomposition method. Furthermore, we design a user pairing scheme to construct the user set for NOMA transmission in the same communication channels. Finally, extensive simulation results validate that the proposed adaptive resource scheduling algorithm greatly improves network throughput while strictly maintaining buffer stability.
Delayed Matching Considering User Patience in Ride-Sourcing System

Ride-sourcing system reshaped the way people travel. Online matching between the idle drivers and passengers is an important problem in a ride-sourcing system. This paper constructs a new multi-stage learning framework, which is used to determine the optimal delay time in ride-sourcing system, and achieve a better matching result while considering the user's patience. We conducted experiments to evaluate the proposed framework and verified its effectiveness, highlighting the importance of patience. This paper presents a novel application of reinforcement learning, showcasing examples of how to apply reinforcement learning in scenarios with large action spaces. In the example of this paper, it achieved good results and can be extended to other application scenarios.
The Mediating Effect of Entrepreneurial Attitude on the Relationship Between Entrepreneurial Motivation and Entrepreneurial Intention

The purpose of this study is to investigate the relationship between entrepreneurial motivation, entrepreneurial attitude and entrepreneurial intention among students from science and technology college who have taken innovation and entrepreneurship courses and to determine whether or not entrepreneurial attitude influences the relationship between entrepreneurial motivation and entrepreneurial intention. Research findings: Entrepreneurial motivation influences entrepreneurial attitude and entrepreneurial intention, entrepreneurial attitude has a significant effect on entrepreneurial intention and entrepreneurial attitude has a complete mediating effect on the relationship between entrepreneurial motivation and entrepreneurial intention. In other words, in order for entrepreneurial education to enhance students' entrepreneurial intention, it is far more effective to enhance students' entrepreneurial attitudes compared to enhancing their entrepreneurial motivation. It is recommended that entrepreneurial education in schools can be achieved by acquiring entrepreneurship knowledge, providing students with a support system, participating in entrepreneurship internship activities, hearing stories of entrepreneurial failures or seeking professional advice, writing entrepreneurship plans, participating in innovative entrepreneurship activities or competitions and establishing good interpersonal relationships, etc. to enhance students' entrepreneurial attitude.