Miracle-dz/UniFAR-FTUs huggingface dataset:
A Uyghur text detection method in natural scenes based on improved CenterNet

Aiming at the problems of difficult Uyghur feature extraction, easy missed detection of targets and low detection efficiency, a Uyghur text detection method based on CenterNet is proposed. Firstly, the lightweight EfficientNetV2-S is used as the backbone network to reduce the network parameters and improve the ability of the network to extract features. Then, the SE module in the backbone network EfficientNetV2-S is replaced by the ECA module to improve the network operation speed of the network. In addition, according to the characteristics of the diversity of Uyghur target size, the deconvolution module in CenterNet is removed, and the pyramid network of features is introduced to enhance the adaptability of the model to Uyghur with different target sizes and improve the generalization ability of the model. The experimental results on the Uyghur dataset show that the average precision of the improved CenterNet for Uyghur detection in natural scenes reaches 94.2 %, which is 5.9 % higher than that of the original CenterNet, and the detection effect on Uyghur is better.
A High-Power Dual-Transformer-Based Dual Active Bridge Converter Suitable for Electric Vehicles

The necessity of incorporating a dual active bridge (DAB) converter in electric vehicles (EVs) has been widely recognized in recent years. In this paper, a DAB converter based on the utilization of two transformers is presented. Also, the principles of operation, switching strategy, and transmission power characteristics of the proposed converter are discussed. Moreover, the RMS current of two transformers with different values of the inductances of the inductors that are in series with the transformers; is discussed. In addition, the loss distribution of the proposed converter across different powers is investigated. The proposed dual-transformer-based dual active bridge converter is compared with the presented converters in terms of transmission power of transformers and efficiency of the converters. Finally, the proposed converter with a low voltage side ($V _{\mathbf{L}}$ = 200 V), the switching frequency of power MOSFETs ($f _{\mathbf{s}}$ = 25 kHz), and an accurate model of the electric battery at a high voltage side ($V _{\mathbf{H}}$ = 400 V) are simulated at 2980 W to verify the way of charging and discharging the electrical battery with the proposed converter.
An Environmental and Behavioral-oriented Search-based Trajectory Planning Approach for Tractor-trailer System in Narrow Environment

Tractor-trailer systems (TTSs) have been adopted in the logistics and transportation domain owing to their vast cargo capacity. Trajectory planning plays an important role in autonomous TTS driving, where existing predominant planners are usually based on graph search and sampling. However, such prevailing planners face the difficulties of poor real-time performance, due to the unique underactuated characteristic and complex kinematic constraints of TTS. Based on TTS motion characteristics and its application environment, this paper proposes a hybrid A* - jump point search (HA-JPS) planner, in which JPS improves the real-time performance, while hybrid A* provides the guarantee of kinematic constraints. Several TTS configures and typical environments were constructed to test the planning efficiency, trajectory feasibility, and completeness of HA-JPS. Comparative simulation experiment results illustrate that HA-JPS can improve real-time performance by more than 50% in different scenarios without loss of completeness.
A Generation Method of Unknown Unsafe Scenarios for Autonomous Vehicles

How to narrow the region of unknown unsafe scenarios is the key to improving the performance of Safety of Intended Functionality for Autonomous Vehicles. However, the research of the generation and evaluation method for unknown unsafe scenarios is still insufficient. In this paper, a novel unknown unsafe scenario generation method and an evaluation method for the generation results are proposed. Firstly, we build a generation system consisting of an evolving system module with spatiotemporally continuous scenarios and a scenario processing module to identify and classify valuable fragments to construct a classified scenario library. Secondly, the generation results are evaluated from 5 dimensions including Validity, Complexity, Efficiency, Diversity and Discrimination. According to the experiment results, with appropriate Background Vehicle (BV) settings, the proposed method can generate explainable, complex, diverse, discriminative unknown unsafe scenarios efficiently. And the evolving system has better performance with DRL-based BVs compared to MOBIL and Nilsson BVs in all evaluation dimensions. Finally, the limitations of MOBIL and Nilsson BVs in the generation of unknown unsafe scenarios are analyzed.
Tailgating Behavior Detection On Rear Vehicles

Modern cars can monitor their drivers and alert them if they drive erratically. However, such systems do not address when other nearby drivers are unsafe. In this paper, we focused on this issue and propose detecting one type of unsafe driving, tailgating behavior of following vehicles. We first studied naturalistic driving data and found that tailgating is an emergent driving behavior, a newly observed driving variation that later transitions to a repeating pattern. This framing of the undesirable behavior as an emergent phenomenon motivates us to model this driving behavior of rear vehicles with Novelets, a recently developed time-series tool that detects emerging behavior. We verified the feasibility of the proposed method through simulation. Our preliminary results show that the proposed approach can identify tailgating driving behaviors of rear vehicles with 92% accuracy.
Decision Making for Driving Agent in Traffic Simulation via Adversarial Inverse Reinforcement Learning

Traffic simulation has the potential to facilitate the development and testing of autonomous vehicles, as a supplement to road testing. Since autonomous vehicles will coexist with human drivers in the transportation system for a period of time, it is important to have intelligent driving agents in traffic simulation to interact with them just like human drivers. Directly learning from human drivers' driving behavior is an attractive solution with potential. In this study, Adversarial Inverse Reinforcement Learning (AIRL) is applied to learn decision-making policies in complex and interactive traffic simulation environments with high traffic density. Bird's Eye View (BEV) is proposed as an observation model for driving agents, providing effective information for the agents' decision-making. Results show that compared with Behavioral Cloning (BC) and Proximal Policy Optimization (PPO), the driving agents generated by AIRL demonstrate higher levels of safety and robustness and they are capable of imitating the car-following and lane-changing characteristics from expert demonstrations. The results further confirm that different driving characteristics can be learned based on AIRL method.
Safe Reinforcement Learning for Lane-Changing with Comprehensive Analysis of Safety Detection

Reinforcement learning (RL) is an experience-driven and data-driven learning method that can well solve the lane-changing problems. However, because traditional RL methods rely on exploration of the environment, the agent is prone to unsafe behavior during exploration, leading to disastrous consequences. It prevents its application in systems that must ensure users' safety, such as autonomous driving. To address this issue, we propose a safe reinforcement learning (safe RL) method, define safety detection parameters, and comprehensively analyze the impact of the proposed parameters on the safety and driving efficiency during lane-changing. Based on the above parameters, a safety detection model is constructed, which can guide the agent to explore in the safe action space, enhance the agent's safety while exploration, and increase the learning convergence speed. In this paper, we use high-fidelity simulation software to simulate and validate the conventional RL method and the proposed safe RL method, respectively. According to the experiments, the safe RL approach suggested in our work dramatically lowers the collision rate during learning and speeds up learning convergence. Moreover, during evaluation, the collision rate of the proposed method can be reduced to 0%, while the average speed of the vehicle is reduced by only about 1 m/s. The results are available as videos at https://youtube.com/playlist? list=PLPFN_7Uwj6PioPAjP4ZBbJPyEAZELkfPD.
DPC Control for Grid-Tied Inverter Powered by PV System Under Change of Parameters and Load

Renewable energy exploitation is rapidly increasing, especially for global electricity production. Numerous enhancements are carried out to improve the exploitation of these renewable sources by using advanced power converters on one hand or adopting new control approaches on the other hand. In this regard, this paper deals with a PV system that is controlled by Direct Power Control (DPC) for the AC side on one hand, and a Fuzzy Logic Controller to extract the maximum power on the other hand. The performance of the presented system is validated through simulation tests using MATLAB.
Enhancing Hate Speech Detection for Social Media Moderation: A Comparative Analysis of Machine Learning Algorithms

In our ever-advancing technological age, a pressing concern emerges the challenge of preserving freedom of expression while combatting hate speech. Hate speech, in simple terms, involves hurtful messages directed at specific groups based on attributes like race, gender, and religion. Social media platforms have sought to curb hate speech through user reporting and automated content scrutiny, but their effectiveness is under scrutiny.To improve hate speech detection, we explore several machine learning algorithms, such as Naive Bayes, Random Forest, Decision Trees, and Gradient Boosting. By tweaking these algorithms and comparing their performance across metrics like AUC, CA, F1 score, Precision, and Recall, we aim to identify the best approach. Furthermore, our study reviews the current state of hate speech detection, presents the methodology used, and discusses implications for social media moderation.Our results show Random Forest as the top performer, but challenges remain. Future research may involve advanced classifiers, algorithm hybridization, and deep learning to enhance model performance in addressing the intricate landscape of online hate speech.
Learning a Residual Fusion Network for SAR Image Despeckling

Deep convolutional neural networks (CNN) have recently been proven to be valid for synthetic aperture radar (SAR) image despeckling. However, these CNN-based despeckling algorithms do not make the most of multi-level features, especially high-frequency features, therefore achieving relatively poor performance. We develop a novel residual fusion model for SAR image depeckling (SAR-RFN). We make the most of multi-level features of different convolutional layers. To be specific, we design residual fusion block (RFB) to extract rich local features through dilated convolutions. Local feature fusion is used to combine all features and learn more effective local features. Moreover, The amalgamation of local features from all RFBs into global features is undertaken through global feature fusion to achieve holistic learning. Extensive experimentation illustrates the supremacy of our SAR-RFN over current leading-edge approaches regarding both quantitative and qualitative measures.
Handwritten Text Recognition Using Deep Learning Techniques

“HANDWRITING RECOGNITION” has generated a lot of attention in the realms of pattern recognition and machine learning due to its applicability in a variety of disciplines. Both optical character recognition (OCR) and hand-written character recognition (HCR) have a defined operation sphere. In the near future, character recognition technology may be essential for exercising and digitising paper documents in order to create a paperless environment. This action categorises each handwritten word in order to convert handwritten notes into digital form. Input text or photos are directly identified by handwriting; many automated process systems also look at these materials. Numerous sorts of handwritten characters, such as integers, figures, cursive notation, symbols, and scripts in various languages have emerged as a result of the development of handwriting. In multitudinous situations where it's necessary to exercise large amounts of handwritten data, analogous as the identification of addresses and postcodes on envelopes, the interpretation of amounts on bank checks, document analysis, and hand verification, automatic recognition of handwritten text can be truly helpful. therefore, a computer is demanded in order for document processing to be as simple as possible.
Hi-Lane: Hierarchical Decision Support Framework for Traffic Signal Control with Dynamic Lanes

The optimal allocation of space-time resources is essential to enhance the performance of an intersection under variant traffic demand. While most research has concentrated on optimizing signal timings via rule-based or learning-based methodologies, there is a dearth of studies on how to adjust lane configuration to further optimize space resources in this context. This is particularly pertinent given the significantly variable flow patterns and turning ratios of urban traffic flow. To address this issue, Hi-Lane, a hierarchical framework is proposed for decision support on intersection traffic control. Hi-Lane takes advantage of dynamic lanes, which have been recently implemented to facilitate better usage of space resources. Based on Hierarchical Reinforcement Learning (HRL), Hi-Lane employs two reinforcement learning agents to tackle this complex optimization problem. Initially, the lane configuration agent will recommend the direction of dynamic lanes based on long-term traffic flow patterns. Then, the signal timing agent will optimize the hyperparameters of the downstream signal control method, which finally allocates green time to the traffic light. Hi-Lane's utility and superiority are demonstrated on an intersection with simulated traffic flows, where it outperforms the models which optimize space or time resources alone.
Fast Environment Generation Methods for Virtual Testing

The popularity of environment simulations for fast assessment of autonomous driving functions is growing faster than ever before. But the creation of virtual environments to simulate real driving scenarios remains a challenge. Modeling objects by hand is a rather slow and expensive process. An alternative approach is procedural generation. Unfortunately, most methods produce only fictional environments. In this paper, we present a novel approach to procedurally generate environments for automotive simulations, that are based on real environment recordings and adress physical sensor specific reflection mechanisms. We use OpenDRIVE to generate the layout and road network and augment it with open source data from OpenStreetMap for objects surrounding the road. In addition, we present a workflow for rapid reconstruction of building facades, which allows us to create realistic and immersive environments for simulations. Our approach is based on the Unreal Engine 4, making it compatible with other popular automotive simulation platforms such as CARLA. These generated environments can provide a cost-effective and flexible alternative to physical testing, allowing researchers and developers to simulate different scenarios in a controlled and reproducible environment. Our approach has the potential to significantly improve the efficiency and scalability of automotive simulations by generating realistic environments for simulation purposes. The GitHub repository of this plugin is publicly available on https://github.com/fzi-forschungszentrum-informatik/eg4u.
Identifying Vehicle Interaction at Urban Intersections: A Comparison of Proximity Resistance, Time-to-Collision, and Post-Encroachment-Time

Interactions between vehicles are extensive at intersections and significantly influence traffic efficiency and safety. Identifying where vehicle interaction occurs at intersections can provide insights for improving designs of and operations on intersections. Conflict indicators like Time-to-Collision (TTC) and Post-Encroachment-Time (PET) have been used to identify vehicle interaction. However, their sole focus on conflicts and potential collisions overlooks cooperative interaction such as waiting for others to pass first. This paper proposes using Proximity Resistance (PR) to identify vehicle interaction at urban intersections. PR measures two-dimensional spacing between vehicles, thus can identify interacting vehicles when their spacing is smaller than typically considered desirable by the drivers. With empirical analysis at an unsignalised urban intersection, this paper demonstrates the effectiveness of PR in identifying cooperative vehicle interaction beyond conflicts. Thereby, this study contributes to a more comprehensive understanding of vehicle interaction at urban intersections.
A Two-Stage Deep Learning Based Approach for Predicting Instantaneous Vehicle Speed Profiles on Road Networks

Prediction of vehicle speed profiles is vital to many transportation and vehicular applications. However, accurately predicting these profiles remains challenging due to the complex and uncertain nature of the factors influencing driver behavior. This research presents a novel data-driven model that leverages a deep two-stage long short-term memory (LSTM) architecture to effectively capture the relationship between vehicle speed and macroscopic road attributes. Our model integrates road features, obtainable from various online map services, and average speeds as inputs and generates naturalistic speed profiles for a given route. The ultimate goal of this study is to integrate the proposed model with a microscopic emission model and incorporate it downstream of a mesoscopic traffic model. This integration enables the generation of high-resolution spatiotemporal maps of traffic emissions. The proposed model is trained and evaluated on a large dataset, including various driving records. The results demonstrate its ability to accurately generate realistic driving patterns while reproducing fuel consumption and emissions levels similar to those of real-world profiles.
Spaceborne SAR Ship Data Expansion Based on GAN

In response to the lack of high-quality and sufficient synthetic aperture radar (SAR) data that can be paired with light ship images in practice, this paper proposes a method to convert optical ship images into SAR images based on GAN networks. In order to improve the coordination of generated images and stabilize the training process, this paper establishes a generator with a self-attention mechanism combined with spectral normalization operations. Secondly, the maximum spatial perturbation consistency loss is introduced to reduce the spatial changes between optical images and SAR images caused by changes in ship size and background noise. Finally, in order to speed up the convergence speed and stability of the model, a relative discriminator is introduced. Experiments conducted on the HRSID and LEVIR remote sensing ship data sets show that the FID (Fréchet Inception Distance) of the SAR ship images generated by this model is reduced to 57.25. At the same time, compared with the current optimal algorithm, the peak signal-to-noise ratio is increased by 5.89% on average. The structural similarity loss is increased by 18.75% on average, and the mean square error is reduced by 13.44% on average.
CCIR: An Architecture for Collecting and Storing Connnected Corridor Infrastructure and Mobility Data

Connected corridors are programs and initiatives that emerge under the umbrella of major paradigms such as SmartCities, Big Data, Artificial Intelligence or Internet of Things (IoT). Their main objective is to provide environments for testing, validating and demonstrating all kinds of technologies related to Cooperative, Connected and Autonomous Mobility, and Intelligent and Digital Infrastructures, within a real scenario. These corridors need to equip themselves with new intelligent and extremely powerful systems, based on technologies such as intelligent traffic systems. They aim to enhance and evolve these systems by leveraging the possibilities and solutions offered by this new wave of disruptive technologies allows. In this article, we describe the needs of a crucial component for connected corridors, such as the information repository, and we propose its possible technological implementation using the Data Lake paradigm, with a focus on data interoperability as a primary requirement. Finally, we validate the significant usefulness of the proposed architecture for the connected corridor information repository, called CCIR, through the concrete implementation of a specific collaborative corridor, such as the Bizkaia Connected Corridor - BCC is. To demonstrate it, we present different use cases that exploit the data generated and collected in the BCC environment relative to the infrastructure and mobility domain.
Isochrone Overlapping: A Novel Approach to Mobility and Infrastructure Planning

In this article we propose a novel approach for mobility analysis by isochrone overlapping to aid urban planners in the decision-making processes of walkable cities for all citizens. This approach focuses on the pedestrian mobility of older citizens but can accommodate different accessibility requirements to quantify the mobility of users. As will be seen, the proposed approach offers great insight for the X-minute city concept and chronourbanism as it unveils new data exploitation that enhances spatially aware analysis and optimization. The reduction in computation complexity of simulation has been quantified and tested. A streamlined workflow is proposed for the evaluation of indicators related to accessible walking and also the identification of the best locations for new services.
Power management control of a Photovoltaic Power System with Batteries and Fuel Cells in Stand Alone Applications

Renewable energy sources exhibit significant variability in their power output, making it necessary to incorporate a storage system. Batteries, and fuel cells, are the most used storage in photovoltaic systems. These technologies efficiently store surplus renewable energy for utilization during periods of low generation. However, batteries have a limitation in their lifetime, which is affected by the deterioration of their state of charge. Additionally, fuel cells' performance can be influenced by the hydrogen consumption rate. The supervision of a photovoltaic system with storage or multiple storage units involves multi-objective control. It not only allows for power regulation and optimization but also ensures a maximum state of charge for batteries and minimal hydrogen consumption for fuel cells. This paper considers and compares various scenarios of PV with storage to identify the optimal solution for specific applications, such as on-grid, off-grid, or electric vehicle setups. Simulation results are presented to support the findings.