Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Energy Efficiency of Two-Layer Control Approach for Connected Vehicles

In this paper, a two-layer control approach for maximizing fuel economy by considering actual traffic conditions has been developed. Compared with previous studies, this work proposes a novel strategy to optimize both the vehicle speed and transmission gear position. Based on the road map information, engine and vehicle parameters, the upper layer optimizes the vehicle speed and gear position simultaneously using dynamic programming (DP) algorithm, which can be planned before departure. The lower layer is used to follow the pre-computed optimal trajectories while respecting speed limit and safe distance constraint with the preceding vehicle. Model predictive control (MPC) is used to track the optimal speed while ensuring safe distance constraint. For gear shifting, an online framework assuring the tracking of the optimal gear position is developed. To evaluate the effectiveness of the proposed approach, comprehensive numerical tests are conducted. Simulation results show that the proposed approach leads to a significant increase in fuel economy under traffic. The average computational time of the online optimization is 0.025 s, indicating its real-time capability.
Combining Optical and Electronic simulation models for the assessment of Visible Light Positioning Systems

Research activities to establish precise and reliable Indoor Positioning Systems (IPS) have significantly gained momentum in the recent years. Visible Light Positioning (VLP), an approach to establish a positioning system by utilizing the spectrum of the visible light that is emitted from one or multiple light sources and received at a photosensitive device, has shown to be a promising candidate for the fulfillment of this task. Still, despite the huge progress in improving the basic technology, some discrepancies between the achievable accuracies reported in simulation based studies and real world implementations become aware. This is because in the simulations the effects of the electronic components necessary for a real world implementation are often simplified or even not considered at all. In this study, we propose an approach of an electronic receiver design methodology that is combining ray-tracing for the simulation of the optical channel with simulations of electronic circuits in Simulink/Simscape. By interfacing these two domains, a system level assessment of applications in the field of Visible Light Positioning can be realized. In addition, we demonstrate in an exemplary implementation how the electronic design can be optimized utilizing the method of fingerprinting.
A Novel Self-Correction Method for Linear Displacement Measurement Based on 2-D Synthesis Mechanism

With increased range, the accuracy of longer-range linear displacement measurement is difficult to guarantee. According to the analysis, it is inevitable that there will be a certain margin between the direction of the calibration grating and the direction of the reading head, which will impact the longer-range linear displacement measurement. In order to eliminate this measurement error, a self-correction method based on 2-D synthesis is herein proposed. First, we describe the principle of absolute linear displacement measurement based on image recognition. Then, the error model is established, which caused by the included angle between the calibration grating and the reading head. Third, a method is proposed for obtaining the vertical offset of the calibration grating by using the vertical image sensor. Finally, we reach an error self-correction method based on 2-D synthesis. In order to test the feasibility of the proposed method, a linear displacement measuring device with a range of 200 mm was developed. After experiment, the maximum absolute error was reduced from 4.34 to $2.03~\mu \text{m}$ with the proposed error correction in the long range of 200 mm. The proposed algorithm does not depend on the assembly and installation position, and can realize self-correction of errors. So the method could lay the groundwork for improving larger range linear displacement measurement technology.
Concept of a Teleoperation System for Inland Shipping Vessels

For some years now, the keyword Maritime 4.0 has also been used to describe the digital transformation in the shipping industry. Given the current challenges in inland shipping, such as the shortage of nautical personnel and the freight structure effect, there is a need for innovative and future-oriented concepts. Modern means of broadband communication and the expansion of the mobile network are opening new ways of managing and networking ships. The joint project “Remote controlled, coordinated driving in inland shipping - FernBin” is part of the “Research strategy for automated driving in inland shipping” and targets the implementation of a teleoperation station on land to address future challenges. This paper addresses the technical concept of such a system and gives insights into the first implementation steps.
An Intelligent Text Processing Method for Civil Aviation Radiotelephony Communication Based on Generative Adversarial Network

Civil aviation radiotelephony communication is the main way of voice communication between controllers and pilots. Verifying radiotelephony communication’s read-back, extracting intent information, and making control decisions through artificial intelligence is significant. Training the deep models necessitates a lot of text data. The radiotelephony communication data is not easy to obtain and often requires extensive manual correction, the use of machines to automatically generate standardized text data has become a pressing issue that must be addressed. Aiming at the problems of error accumulation and exposure bias in traditional supervised learning text generation methods, this paper proposes an intelligent text processing method for civil aviation radiotelephony communication based on Generative Adversarial Networks (GAN), which can achieve high-quality command generation and read-back generation. This paper's key contributions are in two areas. Firstly, this paper proposes a text generation method for civil aviation radiotelephone communications by introducing a Long Short-Term Memory (LSTM) network-based sequence-to-sequence (Seq2Seq) model into a GAN model as its generator. It enhances the ability to capture text features and can generate samples that are as close to the actual text as possible to confuse the discriminator's judgment. Secondly, this paper presents a text categorization approach for civil aviation radiotelephony communication based on Convolutional Neural Network (CNN) and cross-entropy function. The discriminator of GAN built with the CNN model strives to distinguish between real text and generated text without being fooled by the generator. At the same time, the concept of policy gradient in reinforcement learning is introduced during adversarial training. The generator and the discriminator achieve a dynamic balance in the alternating adversarial training. According to experimental findings, the designed model outperforms other comparable models and can automatically generate texts that conform to civil aviation radiotelephone communication specifications without supervision. It can provide new ideas for the intelligent feedback system of civil aviation radiotelephony communication, speed up the digitization process of air traffic control, and lay a solid foundation for reducing crew operations.
Brain Tumor Type Recognition Algorithm Fused with Double Residual Structure and Attention Mechanism

Brain tumor type recognition plays an important role in brain tumor diagnosis, In order to solve the problems of low accuracy and poor real-time performance of existing recognition algorithms, this paper combines double residual structure and attention mechanism to accurately and real-time brain tumor type recognition based on ResNet34. The first step is to reduce the number of parameters while enhancing the algorithm’s ability to extract multi-scale features by replacing the original convolution in the algorithm with a multi-scale convolution to avoid the loss of too large or too small features; Secondly, the loss of features due to deep layers is mitigated by changing the original structure to a double residual structure to prevent degradation of the algorithm. Finally, in order to enhance the weight of important features, an attention mechanism module is embedded in the sidechain residual structure to avoid the impact of redundant features on recognition accuracy. The extracted features are then passed to a classifier for accurate identification of the type of brain tumour. The improved algorithm was validated under the kaggle public dataset, and its accuracy reached 97.4% and the number of parameters was 7. 59M, which is 2% more accurate than the original model and 33% of the number of parameters, and outperformed some existing classical and mainstream algorithms. The experimental results show that the algorithm can accurately and quickly identify the type of brain tumour, which can help doctors in the subsequent treatment.
New results on GP Com

We present high resolution optical and UV spectra of the 46 min orbital period, helium binary, GP Com. Our data contains simultaneous photometric correction which confirms the flaring behaviour observed in previous optical and UV data. In this system all lines show a triple peaked structure where the outer two peaks are associated with the accretion disc around the compact object. The main aim of this paper is to constrain the origin of the central peak, also called "central spike". We find that the central spike contributes to the flare spectra indicating that its origin is probably the compact object. We also detect that the central spike moves with orbital phase following an S-wave pattern. The radial velocity semiamplitude of the S-wave is ∼10 km s - 1 indicating that its origin is near the centre of mass of the system, which in this case lies very close to the white dwarf. Our resolution is higher than that of previous data which allows us to resolve structure in the central peak ofthe line. The central spike in three of the He I lines shows another peak blueshifted with respect to the main peak. We propose that one of the peaks is a neutral helium forbidden transition excited in a high electron density region. This forbidden transition is associated with the permitted one (the stronger peak in two of the lines). The presence of a high electron density region again favours the white dwarf as their origin. We compute Doppler maps for the emission lines which show three emission regions: an accretion disc, a bright spot and an unidentified low velocity emission region associated with the central spike. We obtain modulation Doppler tomograms for some of the emission lines that map the anisotropic emission from the bright spot region. The HST UV spectra also show a strong flare component and confirm the under abundance of silicon in GP Com.
Modification of YOLO with GhostNet to Reduce Parameters and Computing Resources for Detecting Acute Lymphoblastic Leukemia

The Detection of acute lymphoblastic leukemia (ALL) subtypes on multicellular microscopic images is significant for early diagnosis to support the treatment process. Recently, the object detection with a deep learning approach shows good accuracy and fast computation time in the medical field. Therefore, we propose using the You Only Look Once (YOLO) method, namely the Yolov4 and Yolov5 models, to detect the L1, L2, and L3 subtypes. However, both models still have high GFLOPS values and high number of parameters. This paper proposes a modification of Yolov4 and Yolov5 by replacing the standard backbone convolution module with the GhostNet convolution module. The GhostNet module can reduce the GFLOPS value and the number of parameters. Overall. , the Yolo backbone modification model has comparable results with the original Yolo model with a slight difference with a value of 1.4 % in the Yolov4 backbone modification model and 2.4 % in the Yolov5 backbone modification model. The number of parameters and GFLOPS values of the two models modified was reduced by 35% and 40%, respectively.
Optimal GPU Frequency Selection using Multi-Objective Approaches for HPC Systems

Power consumption poses a significant challenge in current and emerging GPU-enabled high-performance computing (HPC) systems. In modern GPUs, controls like dynamic voltage frequency scaling (DVFS), among others, exist to regulate power consumption. Due to varying computational intensities and the availability of a wide range of frequency settings, selecting the optimal frequency configuration for a given GPU workload is non-trivial. Applying a power control with the single objective of reducing power may cause performance degradation, leading to more energy consumption. In this study, we characterize and identify GPU utilization metrics that influence both the power and execution time of a given workload. Analytical models for power and execution time are then proposed using the charac-terized feature set. Multi-objective functions (i.e., energy-delay product (EDP) and ED2p) are used to select an optimal GPU DVFS configuration for a workload such that power consumption is reduced with no or negligible degradation in performance. The evaluation was conducted using SPEC ACCEL benchmarks on NVIDIA GV100 GPU. The proposed power and performance analytical models demonstrated prediction accuracies of up to 99.2% and 98.8%, respectively. On average, the benchmarks showed 28.6% and 25.2% energy savings using EDP and ED2p approaches, respectively, without performance degradation. Fur-thermore, the proposed models require metric collection at only the maximum frequency rather than all supported DVFS configurations.
The effect of Compressor-Decompressor Networks with different image sizes on Mask Detection using Convolutional Neural Networks - VGG-16

Since a pandemic such as COVID-19 entered our lives, the importance of using face masks against such epidemics was experienced once again. Although the use of vaccines increasing the immunity against the COVID-19 virus prevents it from being as effective as in the early times, the use of face masks and their detection in crowded areas may still be important because another pandemic can occur any time. In this research, a preliminary work about a CNN-based compression-decompression model has been studied. In this two-staged model, first, the input image is compressed and its size is reduced to $8\times 8$ from $32\times 32,\ 64\times 64$, or $128\times 128$. In the second stage, this $8\times 8$ image is decompressed and reconstructed to its original size of $32\times 32,\ 64\times 64$, or $128\times 128$ depending on the input image size. Since the results are promising, such a system can be used to compress the image when it is acquired and then transfer it to another machine with better specifications to decompress and use more complex models for better classification accuracies. 90.9%, 91.7%, and 92.1% accuracies were obtained with the VGG-16 model by using $32\times 32,\ 64\times 64$, and $128\times 128$ images respectively. If a balance between network size and accuracy is considered, $64\times 64$ images are the optimal solution, but if the goal is better performance, $128\times 128$ images can be used to have higher accuracy by using higher-cost machines.
Brain MRI based diagnosis of autoimmune diseases using deep learning

The diagnosis of an autoimmune disease usually requires a careful examination of the patient’s health history and the evaluation of any possible occupation and environment related exposures. Frequently, autoimmune disorders have early symptoms such as joint and muscle pain, fatigue, weight loss or fever. These symptoms however are non-specific and imaging technology tools can be extremely valuable for precise diagnosis. In this paper, we deal with autoimmune diseases that result in brain damage and more specifically, multiple sclerosis. Classification of brain MRI images is performed leveraging a Convolutional Neural Network, showing excellent results.
A Novel Long Short Term Memory Network Based Train Dynamic Identification for Virtual Coupling

In recent years, virtual coupling has been proposed as a novel railway transportation method to achieve zero capacity waste with full reliability, flexibility and accessibility. To maintain platoons stable in virtual coupling, the speed of the preceding train must be predicted precisely by combing planned control outputs and train dynamic models. Unfortunately, hindered by complicated wheel-rail interaction mechanisms, accurate train dynamic is difficult to be modeled with conventional methods. This paper proposes a long short term memory network (LSTM) based train dynamic identification method. By considering non-ideal identification situations of value errors and lack of train operation data, two specific LSTM models are designed. The models are tested using the real train operation data extracted from the on-board equipment of Chengdu Metro Line NO. 8, China. Five evaluation indicators are selected to measure the accuracy of the models. The experimental results of different non-ideal conditions show improvements of the root mean square errors of train speed predictions at least 27% when using the proposed LSTM-based identification method.
Dynamic Repositioning in Dock-less Bike-sharing System: A Multi-agent Reinforcement Learning Approach

The dock-less bike-sharing system (DBSS) is a novel mode that allows users to rent/return available sharing bikes without fixed docking station requirements. To increase the usage of idle bikes and fulfill the spatial-temporal varying user demand, the operator needs to reposition available bikes periodically. This paper proposed a multi-agent reinforcement learning framework (MARL) for generating dynamic bike repositioning plans for DBSS to minimize user dissatisfaction. The framework introduced the double deep q network (DDQN) algorithm with the shadow environment trick to tackle the non-stationary learning problem and decomposed training process into sequential independent single-agent training. The bike-sharing data in Yangpu and Hongkou Districts, Shanghai, China, was implemented for model validation. The validation results demonstrated that the proposed approach can achieve robust performance in various condition with different truck and bike fleet size and truck capacity. It also outperformed some traditional stage-optimization model in reducing user dissatisfaction.
Structured illumination fluorescence microscopy using Talbot self-imaging effect for high-throughput visualization

We report a novel extension to structured illumination (SI) microscopy that utilizes the Talbot self-imaging effect to generate a SI pattern on a sample with field-of-view (FOV) and resolution unconstrained by the numerical aperture (NA) of the microscope objective. This enables high-resolution SI pattern generation (up to the free-space propagation limit) across large FOVs, which in turn allows SI microscopy to be efficiently adapted for high-throughput imaging. We demonstrated this concept using a simple prototype microscope to image a large FOV (450 x 350 um) with a diffraction-limited fluorescence resolution limited by the microscope NA to 2 um. A custom-fabricated structured element, designed with periodic spatial features beyond the diffraction limit of the microscope imaging objective, was positioned to within an integer multiple of half the Talbot distance away from the sample. This generated a large FOV, sub-diffraction SI pattern at the sample plane via the Talbot self-imaging effect. Conventional SI super-resolution principles were then used to increase imaging resolution to 500 nm across the complete FOV, roughly a factor of 4 improvement over the microscope NA diffraction limit. This approach should also be applicable for high-throughput sub-diffraction resolution multimodal imaging.
Learning-Based MPC Controller for Drift Control of Autonomous Vehicles

Drift is an agile maneuvering technique that can transiently change the vehicle heading angle. By exploiting the appealing skill, we can broaden the control envelop of autonomous vehicles and keep vehicles controllable even in extreme situations. However, vehicle models at drift states are highly complex, and the parameters are hard to identify. To solve this problem, in this paper we use a relatively simple vehicle model combined with a neural network that can compensate for model errors to capture a more accurate vehicle dynamics. Moreover, the model is then linearized at the drift equilibrium points to construct a fast drift controller based on Model Predictive Control(MPC). Besides, a drift control system is introduced to make the vehicle in drift states and track a complex path simultaneously. The effectiveness of this control scheme is verified by simulations on the Matlab-Carsim platform and experiments on a 1/10 scale RC car.
Thermal Noise and the Branching Threshold in Brittle Fracture

Many studies have confirmed that cracks in brittle materials branch when the crack speed exceeds a certain threshold velocity, but the value of that threshold is not understood. Almost all theoretical calculations overestimate the threshold by factors of two or more. We show that thermal noise can reduce the threshold by a substantial amount, and we propose that this effect can account for the discrepancy.
A Heterogeneous Cellular Automata Model for Crowd Evacuation in Emergency Situations

The modeling of crowd evacuation plays a crucial role in serving the safety management under emergency conditions. However, pedestrian heterogeneity is often ignored in most studies, resulting in the deviation between simulations and real situations. Therefore, this paper develop a heterogeneous cellular automata (CA) model by considering pedestrian heterogeneity from the aspect of physiology and psychology, and their dynamic changes are incorporated into the update rules for movement direction, speed, and conflict resolution mechanism. By conducting numerical simulations, the effects of pedestrian heterogeneity on evacuation process, evacuation efficiency, and conflict and competition are analyzed in detail. Moreover, this paper provides a framework for the application of heterogeneous characteristics in evacuation models, which can further be used to guide relevant research in the fields of intelligent transportation and safety science.
Multi-agent reinforcement learning for electric vehicles joint routing and scheduling strategies

Ahstract-Transforming to a low-carbon future requires massive efforts from both transport and power systems. Electric vehicles (EVs) can reduce CO2 emission in road transport through eco-routing while providing carbon intensity service for power systems via vehicle-to-grid (V2G) scheduling. This paper studies the coordinated effect of routing and scheduling problems of EVs via a novel model-free multi-agent reinforcement learning (MARL) method. In this context, EVs do not reply on any knowledge of the simulated environment and are capable of handling the system with various uncertainties and dynamics during the learning process, which can lead to timely decision making and better privacy protection. Extensive case studies based on a virtual 7-node 10-edge transportation network are developed to demonstrate the effectiveness of the proposed MARL method on reducing carbon emissions in the transportation system and providing carbon intensity service in the power system.