arXiv subjects

Yuan Lin

Publications and source records attributed to Yuan Lin.

At least 55 records · Page 3Linked to original sources

arXiv2023

Dynamic Modulation of Electromagnetically Induced Transparency Metamaterials through Mode Coupling and Stretchable Design

The active control of electromagnetically induced transparency (EIT) metamaterials (MM) has the potential to revolutionize communication networks without relying on quantum technology. However, current reconfigurable systems offer limited flexibility and have high fabrication costs and difficulties. In this study, we examine a classical EIT metamaterial and discover a novel modulation mechanism that leverages mode coupling to dynamically adjust the bandwidth and group delay of the EIT MM. This mechanism is verified through analyses of the electric field and surface charge density distributions. Additionally, a robust coupled Lorentz oscillator model is used to explain the coupling mechanism, with results that are in good agreement with simulations and experiments. To capitalize on this mechanism, we propose a block-definition approach where the MM is divided into stretchable sections, allowing for dynamic modulation of the bandwidth and group delay by stretching the EIT MM. Furthermore, the fabrication process is highly compatible with traditional flexible printed circuit board techniques. Our block-definition EIT MM offers unprecedented tunability and flexibility, requiring no complex components or specialized materials, making it a promising candidate for tunable slow-wave devices and other reconfigurable microwave applications.

physics.app-ph↗

arXiv2023

Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control

In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the adaptive cruise control (ACC) model for energy-saving driving is established. Then, a control-oriented energy management strategy (EMS) model considering the clutch engagement and disengagement is constructed. Finally, the co-optimization structure by integrating ACC model and EMS model is created and is converted to the mixed integer nonlinear programming (MINLP). The results show that this modeling method can be applied to EMS based on the model predictive control (MPC) framework and verify that co-optimization can achieve a 5.1$\%$ reduction in fuel consumption compared to sequential optimization with the guarantee of ACC performance.

eess.SY↗

arXiv2023

Shear thinning of non-Brownian suspensions and its variation at different ambient conditions

Immiscible contaminants are commonly involved in naturally occurring suspensions. The resulting variations of their flow behavior has rarely been evaluated. Here, we investigate the variation of the viscosity of the oil-based two-phase suspension over a period of two years, which is exposed to the ambient air at the production stage. We find that the air's absolute humidity, which strongly varies with the seasons, causes exchanges of water droplets with the suspension, substantially altering its shear-thinning behavior. Only in winter, when the humidity is low, is the latter close to that of ideal two-phase suspensions. Our measurements suggest that, when the surface roughness of the suspended solid particles is sufficiently low, immersed droplets remain in a free state, effectively increasing repulsion between particles, weakening shear thinning. In contrast, when the roughness is sufficiently high, immersed droplets become trapped on the particle surfaces, inducing an attractive particle interaction via water bridging, enhancing shear thinning.

physics.flu-dyn↗

arXiv2023

Half of an image is enough for quality assessment

Deep networks have demonstrated promising results in the field of Image Quality Assessment (IQA). However, there has been limited research on understanding how deep models in IQA work. This study introduces a novel positional masked transformer for IQA and provides insights into the contribution of different regions of an image towards its overall quality. Results indicate that half of an image may play a trivial role in determining image quality, while the other half is critical. This observation is extended to several other CNN-based IQA models, revealing that half of the image regions can significantly impact the overall image quality. To further enhance our understanding, three semantic measures (saliency, frequency, and objectness) were derived and found to have high correlation with the importance of image regions in IQA.

cs.CV↗

arXiv2022

A Neural-Symbolic Approach to Natural Language Understanding

Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks. However, their performances can drastically deteriorate when logical reasoning is needed. This is because NLU in principle depends on not only analogical reasoning, which deep neural networks are good at, but also logical reasoning. According to the dual-process theory, analogical reasoning and logical reasoning are respectively carried out by System 1 and System 2 in the human brain. Inspired by the theory, we present a novel framework for NLU called Neural-Symbolic Processor (NSP), which performs analogical reasoning based on neural processing and logical reasoning based on both neural and symbolic processing. As a case study, we conduct experiments on two NLU tasks, question answering (QA) and natural language inference (NLI), when numerical reasoning (a type of logical reasoning) is necessary. The experimental results show that our method significantly outperforms state-of-the-art methods in both tasks.

cs.CL↗

arXiv2022

A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts. However, when the rotation of the texts increases, it becomes harder to recognizing these texts. The performance of the OCR systems decreases. Thus predicting the rotations of the texts and correcting the images are important. Previous work mainly uses traditional Computer Vision methods like Hough Transform and Deep Learning methods like Convolutional Neural Network. However, all of these methods are prone to background noises commonly existing in general images with texts. To tackle this problem, in this work, we introduce a new masked bounding-box selection method, that incorporating the bounding box information into the system. By training a ResNet predictor to focus on the bounding box as the region of interest (ROI), the predictor learns to overlook the background noises. Evaluations on the text rotation prediction tasks show that our method improves the performance by a large margin.

cs.CV↗

arXiv2022

Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning

Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV energy management are co-optimized such that the DRL policy directly outputs the power split between the engine and the electric motor. The testing results show that DRL can be successfully used for co-optimization, leading to collision-free on-ramp merging. When compared with sequential approaches wherein the upper-level on-ramp merging control and the lower-level PHEV energy management are performed independently and in sequence, we found that co-optimization results in economic but jerky on-ramp merging while sequential approaches may result in collisions due to neglecting powertrain power limit constraints in designing the upper-level on-ramp merging controller.

eess.SY↗

arXiv2021

RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection

Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic Text-Spine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performance in both speed and accuracy, without complex label assignments or repeated feature aggregations. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset.

cs.CV↗

arXiv2020

Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control

This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order system is used as the Control-Oriented Model (COM) to approximate the acceleration command dynamics of a vehicle. Based on the equations of the control system and the multi-objective cost function, we train a DRL policy using Deep Deterministic Policy Gradient (DDPG) and solve the MPC problem via Interior-Point Optimization (IPO). Simulation results for the episode costs show that, when there are no modeling errors and the testing inputs are within the training data range, the DRL solution is equivalent to MPC with a sufficiently long prediction horizon. Particularly, the DRL episode cost is only 5.8% higher than the benchmark solution provided by optimizing the entire episode via IPO. The DRL control performance degrades when the testing inputs are outside the training data range, indicating inadequate generalization. When there are modeling errors due to control delays, disturbances, and/or testing with a High-Fidelity Model (HFM) of the vehicle, the DRL-trained policy performs better with large modeling errors while having similar performance as MPC when the modeling errors are small.

eess.SY↗

arXiv2020

Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and longitudinal control for high-speed on-ramp merging. The DRL environment state includes the states of five vehicles: the merging vehicle, along with two preceding and two following vehicles when the merging vehicle is or is projected on the main road. The control action is the acceleration of the merging vehicle. Deep Deterministic Policy Gradient (DDPG) is the DRL algorithm for training to output continuous control actions. We investigated the relationship between collision avoidance for safety and jerk minimization for passenger comfort in the multi-objective reward function by obtaining the Pareto front. We found that, with a small jerk penalty in the multi-objective reward function, the vehicle jerk could be reduced by 73% compared with no jerk penalty while the collision rate was maintained at zero. Regardless of the jerk penalty, the merging vehicle exhibited decision-making strategies such as merging ahead or behind a main-road vehicle.

eess.SY↗

arXiv2019

Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.

cs.RO↗

arXiv2019

An Action Recognition network for specific target based on rMC and RPN

The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.

cs.CV↗

arXiv2019

Non-Volatile Superconductivity in an Insulating Copper Oxide Induced via Ionic Liquid Gating

Manipulating the superconducting states of high-T_c cuprate superconductors in an efficient and reliable way is of great importance for their applications in next-generation electronics. Traditional methods are mostly based on a trial-and-error method that is difficult to implement and time consuming. Here, employing ionic liquid gating, a selective control of volatile and non-volatile superconductivity is achieved in pristine insulating Pr_2CuO_{4\pmδ} film, based on two distinct mechanisms: 1) with positive electric fields, the film can be reversibly switched between non-superconducting and superconducting states, attributed to the carrier doping effect. 2) The film becomes more resistive by applying negative bias voltage up to -4 V, but strikingly, a non-volatile superconductivity is achieved once the gate voltage is removed. Such a persistent superconducting state represents a novel phenomenon in copper oxides, resulting from the doping healing of oxygen vacancies in copper-oxygen planes as unraveled by high-resolution scanning transmission electron microscope and in-situ x-ray diffraction experiments. The effective manipulation and mastering of volatile/non-volatile superconductivity in the same parent cuprate opens the door to more functionalities for superconducting electronics, as well as supplies flexible samples for investigating the nature of quantum phase transitions in high-T_c superconductors.

cond-mat.supr-con↗

arXiv2019

Integrating Inter-vehicular Communication, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss

Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enables vehicle following with desired inter-vehicular distances. Cooperative Adaptive Cruise Control (CACC) is upgraded ACC that utilizes additional inter-vehicular wireless communication to share vehicle states such as acceleration to enable shorter gap following. Both ACC and CACC rely on range sensors such as radar to obtain the actual inter-vehicular distance for gap-keeping control. The range sensor may lose detection of the target, the preceding vehicle, on curvy roads or steep hills due to limited angle of view. Unfavourable weather conditions, target selection failure, or hardware issue may also result in target detection loss. During target detection loss, the vehicle following system usually falls back to Cruise Control (CC) wherein the follower vehicle maintains a constant speed. In this work, we propose an alternative way to obtain the inter-vehicular distance during target detection loss to continue vehicle following. The proposed algorithm integrates inter-vehicular communication, accurate vehicle localization, and a digital map with lane center information to approximate the inter-vehicular distance. In-lab robot following experiments demonstrated that the proposed algorithm provided desirable inter-vehicular distance approximation. Although the algorithm is intended for vehicle following application, it can also be used for other scenarios that demand vehicles' relative distance approximation. The work also showcases our in-lab development effort of robotic emulation of traffic for connected and automated vehicles.

cs.RO↗

arXiv2018

Tunable superconductivity in parent cuprate Pr$_{2}$CuO$_{4\pmδ}$ thin films

In this article, we studied the role of oxygen in Pr$_{2}$CuO$_{4\pmδ}$ thin films fabricated by polymer assisted deposition method. The magnetoresistance and Hall resistivity of Pr$_{2}$CuO$_{4\pmδ}$ samples were systematically investigated. It is found that with decreasing the oxygen content, the low-temperature Hall coefficient ($R_H$) and magnetoresistance change from negative to positive, similar to those with the increase of Ce-doped concentration in R$_{2-x}$Ce$_{x}$CuO$_{4}$ (R= La, Nd, Pr, Sm, Eu). In addition, $T_c$ versus $R_H$ for both Pr$_{1-x}$LaCe$_{x}$CuO$_{4}$ and Pr$_{2}$CuO$_{4\pmδ}$ samples can coincide with each other. We conclude that the doped electrons induced by the oxygen removal are responsible for the superconductivity of $T^\prime$-phase parent compounds.

cond-mat.supr-con↗

arXiv2018

Non-Backtracking Centrality Based Random Walk on Networks

Random walks are a fundamental tool for analyzing realistic complex networked systems and implementing randomized algorithms to solve diverse problems such as searching and sampling. For many real applications, their actual effect and convenience depend on the properties (e.g. stationary distribution and hitting time) of random walks, with biased random walks often outperforming traditional unbiased random walks (TURW). In this paper, we present a new class of biased random walks, non-backtracking centrality based random walks (NBCRW) on a network, where the walker prefers to jump to neighbors with high non-backtracking centrality that has some advantages over eigenvector centrality. We study some properties of the non-backtracking matrix of a network, on the basis of which we propose a theoretical framework for fast computation of the transition probabilities, stationary distribution, and hitting times for NBCRW on the network. Within the paradigm, we study NBCRW on some model and real networks and compare the results with those corresponding to TURW and maximal entropy random walks (MERW), with the latter being biased random walks based on eigenvector centrality. We show that the behaviors of stationary distribution and hitting times for NBCRW widely differ from those associated with TURW and MERW, especially for heterogeneous networks.

cs.SI↗

arXiv2017

Normal State Gap in Parent Cuprate Pr2CuO4+/-δ

We present a tunneling study on single crystalline parent cuprate thin films, i.e. a series of Pr2CuO4+/-δ(PCO) with tunable superconducting transition temperature. The zero-bias anomaly of differential conductance, well reported in the normal state of R2-xCexCuO4 (R = Pr, Nd, La) and named as normal state gap (NSG), is observed in the Ce-free samples. This NSG behaves quite robust against the magnetic field up to 16 T, but fades away with increasing the temperature. Most importantly, we find that the magnitude of the NSG becomes larger with increasing point-contact junction resistance on the superconducting films, which is further enhanced in the non-superconducting samples of more oxygen disorders. The origination of NSG can be understood in the framework of Altshuler-Aronov-Lee (AAL) theory, where the disorder-induced electron-electron interactions suppress the density of states and thereby result in a soft Coulomb gap.

cond-mat.supr-con↗

arXiv2017

Assessing Percolation Threshold Based on High-Order Non-Backtracking Matrices

Percolation threshold of a network is the critical value such that when nodes or edges are randomly selected with probability below the value, the network is fragmented but when the probability is above the value, a giant component connecting large portion of the network would emerge. Assessing the percolation threshold of networks has wide applications in network reliability, information spread, epidemic control, etc. The theoretical approach so far to assess the percolation threshold is mainly based on spectral radius of adjacency matrix or non-backtracking matrix, which is limited to dense graphs or locally treelike graphs, and is less effective for sparse networks with non-negligible amount of triangles and loops. In this paper, we study high-order non-backtracking matrices and their application to assessing percolation threshold. We first define high-order non-backtracking matrices and study the properties of their spectral radii. Then we focus on 2nd-order non-backtracking matrix and demonstrate analytically that the reciprocal of its spectral radius gives a tighter lower bound than those of adjacency and standard non-backtracking matrices. We further build a smaller size matrix with the same largest eigenvalue as the 2nd-order non-backtracking matrix to improve computation efficiency. Finally, we use both synthetic networks and 42 real networks to illustrate that the use of 2nd-order non-backtracking matrix does give better lower bound for assessing percolation threshold than adjacency and standard non-backtracking matrices.

cs.SI↗