Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Performance and Battery Consumption through Edge Computing

This paper presents the ways edge computing can influence both application performance and client-side battery consumption by testing a standard web application with varying configurations. Through these tests it was possible to determine that edge computing can have a measurable effect on mobile application battery consumption and may be able to increase the longevity of client device batteries if taken into consideration when designing an application. There are also some interesting results that indicate a relational database may outperform a cloud-native NoSQL database under certain conditions, but more testing and analysis will be required to determine the basis of the results and validate the findings in this area.
Maximum Entropy Inverse Reinforcement Learning Based on Frenet Frame Sampling for Human-Like Autonomous Driving

Ensuring social acceptability is a key factor in the successful operation of autonomous vehicles. To achieve this, it is important to extract driving habits from expert human drivers. This paper proposes a method for training reward functions using maximum entropy inverse reinforcement learning based on Frenet frame sampling. The proposed method is applied to learn reward functions for the replication of human driver's lane-changing and velocity-keeping behaviors in multi-lane scenarios from human driving dataset, outperforming traditional methods based on Cartesian frame sampling. Converting the reward value of each sample into a probability distribution in the sample space allows for the provision of a convex space with human driving characteristics to the lower-level trajectory planner. These enhancements will help autonomous vehicles to select trajectories that align more with human driving habits. Experimental results show that the learned reward function can characterize human drivers' tendencies in multi-lane scenarios across multiple optimization metrics and exhibit strong interpretability. The proposed method effectively extract human driving behavior and habits under test conditions.
A High Performing Approach to Short-Term PV Power Forecasting using Machine Learning Algorithms

The use of solar energy has been gaining popularity around the world as a clean and sustainable source of energy. However, the variability of solar power generation due to weather patterns poses challenges for integrating solar power into the grid and ensuring a reliable energy supply. To address these challenges, short-term solar photovoltaic (PV) power forecasting using machine learning (ML) algorithms has emerged as a promising solution. This study investigates the effectiveness of different ML algorithms for short-term PV power forecasting, provides an overview of factors affecting the performance with time-series based data, and proposes high performance approaches. Artificial Neural Networks (ANN), Long Short-term Memory (LSTM), and Support Vector Machine (SVM) algorithms have been examined on a one-year dataset of 88,494 with 20 features and 15-min resolution. Root means square error (RMSE) and mean square error (MSE) are considered as the accuracy measure and found that both ANNs and LSTM outperform SVM. A thorough comparison with recently published results shows the superiority of the proposed approaches. This success of ANNs and LSTM can be attributed to their ability to capture the complex and non-linear relationships present in the data. SVMs are more suited to handle linear relationships and may struggle with the complexities of short-term solar power forecasting. Although more research is needed to generalize this observation, these findings will serve as a valuable guide who wish to work in the area of PV power forecasting.
Digital Bank Security in Indonesia

The purpose of this work is to review and analyze the trust and security aspects of digital banking with the aim of obtaining valid and comprehensive information about the security views of users of digital banking services. Due to cyber security threats, users often use traditional methods instead of technology to think about security. Therefore, the purpose of this study is to investigate the intensity and trust of digital banking behavior completed by 434 respondents online using a Google form. The obtained data were then analyzed using CB-SEM (Structural Equation Modeling Based on Covariance) SmartPLS 4 application. The results showed that investor expectations, habits, security and social influence influence the adoption or use of digital banking services, while enabling conditions, hedonic motivation and work expectations do not influence the use of digital banking based on the feedback of existing users. Digital banking services. This study shows that the higher the investment of users, the more often they use digital banking services, habits, high security and social factors that encourage users to continue using digital banking services. However, the terms of service and performance expectations do not affect users’ use of digital banking services. Through this study, readers will gain new knowledge and awareness of the factors that influence the use of digital banking services and increase users’ confidence in the security of digital banking in Indonesia.
Enabling Rural IoV Communication through Decentralized Clustering and Federated Learning

The Internet of Vehicles (IoV), a network interconnecting vehicles through sensors and communication technology, holds promise for addressing traffic congestion and enhancing road safety through vehicle-to-vehicle and vehicle- to-infrastructure communication. While the potential benefits are evident, the integration of IoV in rural environments faces challenges due to inadequate infrastructure, rendering current centralized solutions impractical. This challenge is amplified by latency considerations, especially in applications critical for safety. In response, we present a decentralized clustering algorithm specifically designed for the implementation of IoV in rural areas, with enhancements from federated deep reinforcement learning. Our approach not only demonstrates low network latency, competitive packet delivery ratios, and cluster stability but also transcends the limitations of conventional solutions. We present a viable solution for IoV in rural settings, our research contributes to the realization of safer and more efficient transportation systems. The proposed approach not only addresses current challenges but also lays the groundwork for future applications in varied scenarios.
Adaptive Facial Landmark Detection Algorithm Based on Supervised Learning

Facial landmark detection is the basis of face analysis, which is of great significance. Aiming at the lack of accuracy and robustness of facial landmark detection in difficult scenes, we propose an adaptive facial landmark detection network Adaptively-3FabRec. In order to avoid the loss of key semantic information and reduce the accuracy, we introduce an adaptive spatial feature fusion module, which can adaptively fuse key features; in order to improve the accuracy of key points, Adaptive wing loss is introduced as a supplement to make the network focus on foreground pixels and difficult background pixels. Under the same experimental settings, the experimental results on the 300-W data set show that the NME, FR and AUC value of the Adaptively-3FabRec are 3.96,0.87 and 60.32, respectively, which are 9.3 %, 23.5 % and 1.8 % higher than the baseline 3FabRec.
Analysis of a Novel L-Type Outer-Rotor Consequent-Pole Machine

Recently, consequent-pole permanent magnet (CPM) machines have been widely utilized in various cost-sensitive applications due to their high permanent magnet (PM) utilization. However, CPM machines exhibit high torque ripple and asymmetrical back Electro Motive Force (EMF) voltage due to asymmetrical air gap flux density resulting from using PMs with the same polarity. So, the paper presents a new L-type modular outer-rotor consequent-pole permanent magnet (LMOCPM) machine with different PM and iron pole sequences to improve electromagnetic performance. To achieve the dual objective of reducing PM and rotor core volume, a new structure for the PM machine has been developed by combining the modular rotor with the CPM concept. On the other hand, the proposed structure has resulted in rotor loss reduction and high PM utilization in machines. Firstly, the three different PM arrangement topologies with various $\mathrm{N}, \mathrm{S}$, and iron poles are offered for the proposed LMOCPM machine. Then, the magnetic circuit model is used to investigate LMOCPM and modular surface-mounted PM (MSPM) machines, demonstrating the benefits of modular structures with unipolar PMs. The electromagnetic performance of the proposed machines is analyzed using Finite Element Method (FEM), including air gap flux density, flux linkage, cogging torque, and on-load torque. According to the results, the LMOCPM machines use 39% less PM material than MSPM, while the average torque remains nearly unchanged in LMOCPMI and LMOCPM2 machines compared to the MSPM machine. As a result, the PM utilization ratio is boosted by 61%. Furthermore, the torque ripples of the LMOCPMI and LMOCPM2 machines drop dramatically to 15.7% and 14.1%, respectively, while the MSPM machine has a torque ripple of 23.2%.
Cooperative Handover Performance Analysis in a Cell-Free Architecture

User-centric Coordinated Multipoint (CoMP) is a promising technology that can enhance handover performance in terms of network coverage and throughput. However, the increasing number of cooperative base stations (BSs) may lead to a higher handover rate, which could negatively impact user performance. This paper presents an analysis of the coverage probability and handover rate in CoMP mode and Non-CoMP mode, and provides easily manageable expressions. To balance the trade-off between handover cost and coverage capacity, we introduce average throughput as a performance indicator, and demonstrate that when the user speed is less than a certain limit, denoted as $v^{*}$, the cooperative mode outperforms the non-cooperative mode. Therefore, our proposed scheme has clear advantages in such scenarios. Monte Carlo simulation is used to verify the correctness and effectiveness of the analysis presented in this paper.
Generic Ontology and Framework for Critical Scenario Description and Generation. Applied to Evaluation and Validation of Automated Vehicle

The safety of new autonomous vehicles must be evaluated before introducing them into mixed traffic. But before this can be done, safety-critical scenarios must be identified and tested. Using expert knowledge and field data we identify ten safety critical scenarios to simulate and therefore evaluate the autonomous vehicle performance compared to conventional, human-driven vehicles performance. This selection was made balancing the criticality and frequency of a given situation. To describe these scenarios, we create an ontology that considers the difference between autonomous vehicles and human drivers and provides an exhaustive description of the vehicle surrounding. In this paper, we propose a generic framework and ontology usable for safety-critical scenarios description and generation.
The Impact of Truck Platooning on Traffic Flow Considering Bridge Safety Index

Truck platooning has the potential to revolutionize freight transportation by enhancing traffic flow and fuel efficiency. However, as the truck platoons roll out, they will be disruptive to the safety of existing highway bridges. This study focuses on addressing these concerns and ensuring the safe passage of platoons across bridges while examining their impact on traffic flow. To accomplish this challenge, the study proposes a novel method that utilizes the bridge safety index to search for the optimal configuration to operate truck platoons. The performance of the bridge safety and traffic flow with truck platoons is evaluated with microscopic traffic simulation. The evaluation indicates that reducing the space headway between vehicles and increasing the platoon size can improve bridge traffic flow rates. Additionally, the study highlights the influence of speed on traffic flow, revealing that increasing the speed generally improves traffic flow, except when it reaches 70 km/h. Considering the limitations of bridge safety, the optimal traffic flow can be increased by up 124% when compared with the roads without platooning and bridges. This emphasizes the substantial impact that truck platooning has on the dynamics of traffic flow.
Optimizing Vehicle-Passenger Matching for Online Ride-Hailing with Vehicular Crowd-Sensing

Vehicular crowd-sensing (VCS) has become a widely adopted approach in smart city applications, with online ride-hailing system as a promising carrier. However, the spatially unbalanced demand of online ride-hailing services makes it challenging to collect urban information comprehensively. This paper explores the potential of vehicle-passenger matching strategy in VCS based on online ride-hailing. We propose a mixed-integer programming model that considers two factors: the total sensing coverage of the fleet and drivers' income. With a limited monetary incentive budget, the model can improve the spatial-temporal coverage of VCS by matching suitable passengers or providing targeted cruising directions for idle vehicles. To validate the effectiveness of the matching strategy, we conduct simulation tests based on two demand scenarios with different passenger distributions. Compared to an income-maximizing greedy algorithm as benchmark, the proposed method improves the sensing coverage by up to 13.3%, at a minor cost to the drivers' income, by up to 1.6%. The mixed-integer programming approach is also compared to a coverage-maximizing greedy heuristic, with coverage improvement by up to 5.8%. A sensitivity analysis is conducted for different penetration rates of the sensors, which reveals a general trend of both objectives that can be used to inform decision making on sensor investment. The proposed methods and results offer insights into the sensing potential of ride-hailing fleets.
Simulation-Based Support for Semi-Automated Automotive Safety Analysis

Background: As part of the ISO 26262 Hazard Analysis and Risk Assessment, safety experts analyse the vehicle functions to identify potential hazards and assess the associated risk. At the time of HARA, the vehicle was not yet developed and could not be used for analysis purposes. Aim: To overcome this problem, simulations are a promising measure to support HARA execution in the early concept phase. Method: Thanks to the vehicle model available in the concept phase, simulation can be used to better describe the vehicle behaviour in a hazardous driving situation and to assess the associated risks more precisely. Results: We present a simulation environment that can simulate common failures and their effects in different HARA - relevant driving situations and use the obtained simulation data to determine the HARA parameters and the ASIL in a semi-automated way. Conclusion: The developed simulation environment can contribute to the automation of HARA and support the determination of safety measures and design criteria for the investigated function. The results have also shown that the use of simulation is limited and that HARA remains the responsibility of safety experts.
A method for suppressing SAR radio frequency interference based on deep learning in time-frequency domain

Synthetic-aperture radar is widely deployed due to its high resolution, such as deployed in battlefield monitoring, geographic mapping, disaster assessment and other fields. Radio frequency interference, as an active interference method, mainly emits electromagnetic waves to make the radar receiver inevitably receive interference signals while receiving echoes, forming a masking effect on the images, thereby reducing the quality of SAR images. Many filtering algorithms have been proposed to address the aforementioned issue, however, these algorithms generally lack generalization and the real-time performance is insufficient. Therefore, in order to solve the above problems, this paper proposes a deep learning filtering algorithm and constructs a simulation dataset in the time-frequency domain. The final experimental results show that the PSNR of the time-frequency map is improved to 35.4dB by our method, and the interference on the SAR image is effectively filtered out. What's more, this method has good generalization and can work well against various RFI styles.
Fault Diagnosis of Gear Based on Multichannel Feature Fusion and DropKey-Vision Transformer

To solve the problem that it is single-channel vibration signals not being able to fully express fault feature information and diagnostic networks not being able to fully capture its information resulting in low diagnostic accuracy, a new gear fault diagnosis method is proposed. First, subtraction average-based optimizer (SABO) as an optimization algorithm is introduced to optimize the parameters of variational mode decomposition (VMD) quickly and with high quality to conduct signal preprocessing. Next, the noisy signals in each channel can be quickly and effectively processed to obtain clean 1-D and prominent vibration characteristics signals from multichannel. Then, multichannel information is fused to obtain image datasets for diagnosis based on symmetric dot pattern (SDP) to realize clear signals transformed into images. A diagnostic model is proposed based on DropKey added for vision transformer (DVit) to enhance the diagnostic network’s ability to comprehensively capture multichannel feature information. Finally, the proposed method is validated through three datasets from gear fault diagnosis experiments with the average accuracy in fault diagnosis reaching more than 99.5% whether it is the degree or type of fault diagnosis. The average accuracy has increased by at least 0.5% compared with before improvement, and it has increased about 2%–7% compared with other methods. The results with visualization form verify the effectiveness and superiority of the proposed method.
Micro-Doppler Separation in ISAR Imaging based on Complex Local Characteristic-Scale Decomposition

In an inverse synthetic aperture radar (ISAR) system, the micro-Doppler (m-D) interference caused by the fast rotation or vibration of the target's components would degrade the legibility of the image reconstruction. To address this challenge, this paper proposes a novel algorithm that is based on complex local characteristic-scale decomposition (CLCD). The Doppler frequency characteristics of the main body and the fast-rotating parts were analyzed in our study. Subsequently, the radar echoes were decomposed into a set of stable mono-components using the CLCD algorithm. Through the imaging process, improved geometrical features of the main body were obtained. The effectiveness of the proposed method was demonstrated through comprehensive experiments.
Wind Turbine Mechanical Speed Regulation Reliability Of Artificial Intelligent PSO-FLC Control

this paper addresses modeling and control of a wind energy conversion system (WECS). The WECS based on Permanent Magnet Synchronous Generator, PMSG. Wind energy transformed to mechanical energy via blade and turbine to give speed and torque to the PMSG. This mechanical speed will be controlled firstly with a classical MPPT-PI then will be optimized by a PSO algorithm, after that a new intelligent controller MPPT-FLC will be applied to show the efficiency of that's kind of controller on our WECS mechanical speed control. The analysis and discussion of the simulation results aim to enhance the reliability and efficiency of each suggested approach. Keywords—Wind turbine, Speed control, Optimization, FLC, PSO, PI.
Interoperability Solution for Blockchain-Based Internet of Vehicles Driven by Multiple Oracles

The increasing blockchain-based Internet of Vehicles (BIoV) networks attract attention, and there is little cross-chain data interaction solution in this scenario yet. Due to the limited data amount and types, BIoV networks also require off-chain data to provide intelligent services and experiences. Nowadays, lots of surveys concentrate on cross-chain and on-off-chain interoperability for blockchains, while BIoV networks lack a secure and effective interoperability solution to satisfy both cross-chain and on-off-chain interoperability, where ensuring the authenticity of inter-chain semi-authentic data and off-chain inauthentic data is challenging. To solve these challenges, this paper proposes a BIoV interoperability solution driven by a multiple oracle network. Kinds of consensus algorithms implement the voting to ensure the authenticity of verified data against multiple malicious attacks. The voting is implemented by kinds of consensus algorithms to ensure the authenticity of verified data against multiple malicious attacks. Extensive experiments are conducted to prove the effectiveness of this solution in terms of transaction performance and resource occupancy.
Save Money, Get Charged: Facility-Tied Energy Management with Unknown and Unscheduled EV Charging

Energy Management Systems (EMS) for extensive facilities often experience significant unplanned loads from electric vehicle (EV) operators connected to Electric Vehicle Supply Equipment (EVSE). Facility EMS often statically curtails the installed EVSE units to mitigate costly peak infringement. This results in many vehicles being insufficiently charged or routinely exceeding peak limits. The developed EVSE EMS module is designed to reduce peak infringements while ensuring a fast and complete charge for EV operators. This tool leverages recent advances in machine learning (transformers, RegNet, and Deep Q Reinforcement Learning), coupled with classical algorithms such as Naive Bayes to learn, adapt, and anticipate impacts of unknown charging events. This module is implemented alongside the operating EMS at the USU Electric Vehicle Roadway (EVR) facility. It significantly reduces costs while ensuring efficient charging for unknown and unscheduled user EVs.
GPS-guided Vision-based Lane Detection for Autonomous Vehicles

Vision-based lane detection methods often rely on lane markings in image streams as the only source of information to determine lane boundaries. However, different road conditions such as strong shadows and lighting changes present significant challenges in extracting lane marking features, which may result in inaccurate or inconsistent lane boundary detections. This paper proposes GPS-Guided Lane Detection (GPS-LD) as a robust lane detection method that uses conventional image processing techniques for feature extraction and global route data as prior information to determine lane boundaries. The proposed method uses the route from the mission planner to determine the shape of the road in front of the vehicle. The road shape extracted from the route data is then used as a reference to cluster lane marking features and to verify the lane boundary detection results. The proposed method is evaluated on the KITTI dataset and the detection performance is comparable to state-of-the-art lane detection methods.
PID controller using new GWO strategy to drive dual star induction motor

This work deals with optimizing the parameters of the proportional-integral-derivative (PID) controller to improve the speed control performance of (DSIM) driven by field-oriented indirect control IFOC. The strategy used is a promising gray wolf optimization (GWO) algorithm based on artificial intelligence techniques. Compared to using the PID controller alone, the technique provided is more effective in improving the stability of the speed loop response. The simulation results under Matlab environment demonstrate that the designed PID-GWO speed controller achieves good dynamic behavior of DSIM, perfect speed tracking without overshoot, better performance and high robustness.