arXiv subjects

Wen Wei

Publications and source records attributed to Wen Wei.

6 recordsLinked to original sources

arXiv2024

The Verifications of No Bright and Dark Fringes In a Standing-wave

After Wiener's experiment, it had been widely accepted that only electric-field was the fundamental-factor for optical interference and believed that there were alternating bright and dark fringes in a light standing-wave field which was formed by a pair of parallel light-beams traveling in mutually opposite directions. While being supplied to the bright fringes, the light-energy from the beams must pass across the dark fringes or across the electric-field nodes, so that the electric-field would not keep zero at the nodes. However, it was inconsistent with the characteristic of electric-field nodes at which the electric-field was zero anytime. Therefore, this naturally led to a presumption that there were no bright and dark interference-fringes in a standing-wave field, which was consistent with zero of the electromagnetic energy-flux-density in a standing-wave field. In this paper, it was fully verified from several aspects, including an ideal experiment, comparing a standing-wave with an LC circuit, a group of experiments and theoretical deductions of formulas. An inclination factor was introduced into the previous common formula of two-beams-interference to rectify it back to the original meaning of its initial definition by energy-flux-density rather than electric-field energy-density. Given the angle between the two-beams to be 180 degrees as a validation, it was verified by the corrected formula that the light-intensities of the bright interference-fringes in a standing-wave were equal to zero. i.e., no interference-fringes existed in a standing-wave field.Thus, instead of electric-field energy-density alone from Wiener's conclusions, interference-energy-flux density was the fundamental-factor to form interference-fringes. The magnetic-field vector acts the same role as the electric-field vector on light interacting with substance.

physics.optics↗

arXiv2024

Intention-based and Risk-Aware Trajectory Prediction for Autonomous Driving in Complex Traffic Scenarios

Accurately predicting the trajectory of surrounding vehicles is a critical challenge for autonomous vehicles. In complex traffic scenarios, there are two significant issues with the current autonomous driving system: the cognitive uncertainty of prediction and the lack of risk awareness, which limit the further development of autonomous driving. To address this challenge, we introduce a novel trajectory prediction model that incorporates insights and principles from driving behavior, ethical decision-making, and risk assessment. Based on joint prediction, our model consists of interaction, intention, and risk assessment modules. The dynamic variation of interaction between vehicles can be comprehensively captured at each timestamp in the interaction module. Based on interaction information, our model considers primary intentions for vehicles to enhance the diversity of trajectory generation. The optimization of predicted trajectories follows the advanced risk-aware decision-making principles. Experimental results are evaluated on the DeepAccident dataset; our approach shows its remarkable prediction performance on normal and accident scenarios and outperforms the state-of-the-art algorithms by at least 28.9\% and 26.5\%, respectively. The proposed model improves the proficiency and adaptability of trajectory prediction in complex traffic scenarios. The code for the proposed model is available at https://sites.google.com/view/ir-prediction.

cs.RO↗

arXiv2023

Ethical Decision-making for Autonomous Driving based on LSTM Trajectory Prediction Network

The development of autonomous vehicles has brought a great impact and changes to the transportation industry, offering numerous benefits in terms of safety and efficiency. However, one of the key challenges that autonomous driving faces is how to make ethical decisions in complex situations. To address this issue, in this article, a novel trajectory prediction method is proposed to achieve ethical decision-making for autonomous driving. Ethical considerations are integrated into the decision-making process of autonomous vehicles by quantifying the utility principle and incorporating them into mathematical formulas. Furthermore, trajectory prediction is optimized using LSTM network with an attention module, resulting in improved accuracy and reliability in trajectory planning and selection. Through extensive simulation experiments, we demonstrate the effectiveness of the proposed method in making ethical decisions and selecting optimal trajectories.

cs.RO↗

arXiv2021

General Analytical Method of Kibble-Zurek Mechanism for Exact Prediction of Topological Defects Production

In the field of non-equilibrium phase transitions, the Kibble-Zurek mechanism (KZM) is undoubtedly an important discovery, pointing out that some universal scaling rules are applied to a wide range of physical systems from quantum to the cosmos in complex non-equilibrium continuous phase transitions. However, except for some scaling relations in specific cases, the algebraic-based KZM can not provide further details on the topological defect generation laws. In this work, we propose an analytical-based KZM that can accurately predict topological defect generation for a given quenching condition. Compared with the conventional KZM, our theory is more accurate and more widely applicable, especially in non-linear quenching conditions and inhomogeneous structural systems, where it has more obvious advantages. Our work reveals a fundamental and intrinsic mechanism in non-equilibrium phase transitions, blazing a new trail for accurate description and manipulation of topological defect generation.

quant-ph↗

arXiv2020

Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization

Automatic animation line art colorization is a challenging computer vision problem, since the information of the line art is highly sparse and abstracted and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of Generative Adversarial Network (GAN) based image-to-image translation methods for single line art colorization have emerged. They can generate perceptually appealing results conditioned on line art images. However, these methods can not be adopted for the purpose of animation colorization because there is a lack of consideration of the in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of correlation matching feature transfer model (called CMFT) to align the colored reference feature in a learnable way and integrate the model into an U-Net based generator in a coarse-to-fine manner. This enables the generator to transfer the layer-wise synchronized features from the deep semantic code to the content progressively. Extension evaluation shows that CMFT model can effectively improve the in-between consistency and the quality of colored frames especially when the motion is intense and diverse.

cs.CV↗

arXiv2018

Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS). A reliable measure of the tissue myelin content is therefore essential for the understanding of the physiopathology of MS, tracking progression and assessing treatment efficacy. Positron emission tomography (PET) with $[^{11} \mbox{C}] \mbox{PIB}$ has been proposed as a promising biomarker for measuring myelin content changes in-vivo in MS. However, PET imaging is expensive and invasive due to the injection of a radioactive tracer. On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely available technique, but existing MRI sequences do not provide, to date, a reliable, specific, or direct marker of either demyelination or remyelination. In this work, we therefore propose Sketcher-Refiner Generative Adversarial Networks (GANs) with specifically designed adversarial loss functions to predict the PET-derived myelin content map from a combination of MRI modalities. The prediction problem is solved by a sketch-refinement process in which the sketcher generates the preliminary anatomical and physiological information and the refiner refines and generates images reflecting the tissue myelin content in the human brain. We evaluated the ability of our method to predict myelin content at both global and voxel-wise levels. The evaluation results show that the demyelination in lesion regions and myelin content in normal-appearing white matter (NAWM) can be well predicted by our method. The method has the potential to become a useful tool for clinical management of patients with MS.

cs.CV↗