Search Results for author: Lei Lei

Found 40 papers, 11 papers with code

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts

no code implementations23 Mar 2024 Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, JinKun Lin, Yangguang Mei, Lingnan Xu

In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

no code implementations19 Nov 2023 Tong Liu, Lei Lei, Kan Zheng, Xuemin, Shen

It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay.

Autonomous Driving Decision Making

Learning Real-World Image De-Weathering with Imperfect Supervision

1 code implementation23 Oct 2023 Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, Chaoyu Feng, Xiaotao Wang, Lei Lei, WangMeng Zuo

Real-world image de-weathering aims at removing various undesirable weather-related artifacts.

Pseudo Label

Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes

1 code implementation3 Oct 2023 Zhilu Zhang, Haoyu Wang, Shuai Liu, Xiaotao Wang, Lei Lei, WangMeng Zuo

The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image.

HDR Reconstruction

Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement Learning Techniques

no code implementations25 Sep 2023 Emanuel Figetakis, Yahuza Bello, Ahmed Refaey, Lei Lei, Medhat Moussa

The results unequivocally demonstrate that the DQN agent trained using the {\epsilon}-greedy policy significantly outperforms the one trained with the Boltzmann policy.

Autonomous Vehicles OpenAI Gym +1

Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition

1 code implementation ICCV 2023 Xiaoyu Liu, Ming Liu, Junyi Li, Shuai Liu, Xiaotao Wang, Lei Lei, WangMeng Zuo

In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i. e., UNIC).

Image Cropping

Symbol-Level Precoding for MU-MIMO System with RIRC Receiver

no code implementations27 Jul 2023 Xiao Tong, Ang Li, Lei Lei, Fan Liu, Fuwang Dong

The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions.

Self-supervised Learning to Bring Dual Reversed Rolling Shutter Images Alive

2 code implementations ICCV 2023 Wei Shang, Dongwei Ren, Chaoyu Feng, Xiaotao Wang, Lei Lei, WangMeng Zuo

In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions.

Self-Supervised Learning

Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning

no code implementations28 Apr 2023 Jiaju Qi, Lei Lei, Kan Zheng, Simon X. Yang, Xuemin, Shen

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL).


SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

no code implementations11 Feb 2023 Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu

Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.

Decision Making Multivariate Time Series Forecasting +1

CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition

no code implementations ICCV 2023 Peiqi Jiao, Yuecong Min, Yanan Li, Xiaotao Wang, Lei Lei, Xilin Chen

The co-occurrence signals (e. g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR).

Sign Language Recognition Visual Grounding

Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography

no code implementations CVPR 2023 Yue Cao, Ming Liu, Shuai Liu, Xiaotao Wang, Lei Lei, WangMeng Zuo

Although deep neural networks have achieved astonishing performance in many vision tasks, existing learning-based methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling.

Image Denoising

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

1 code implementation28 Dec 2022 Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Bo Zheng, Lei Lei, Yun Hu

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.

Multivariate Time Series Forecasting Time Series

Efficient stereo matching on embedded GPUs with zero-means cross correlation

no code implementations1 Dec 2022 Qiong Chang, Aolong Zha, Weimin WANG, Xin Liu, Masaki Onishi, Lei Lei, Meng Joo Er, Tsutomu Maruyama

By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.

Stereo Matching

Multilayer Fisher extreme learning machine for classification

no code implementations Complex & Intelligent Systems 2022 Jie Lai, Xiaodan Wang, Qian Xiang, Jian Wang, Lei Lei

To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM).

Classification Denoising +1

AVDDPG: Federated reinforcement learning applied to autonomous platoon control

no code implementations5 Jul 2022 Christian Boin, Lei Lei, Simon X. Yang

Both Inter-FRL and Intra-FRL are applied to a custom AV platooning environment using both gradient and weight aggregation to observe the performance effects FRL can have on AV platoons relative to an AV platooning environment trained without FRL.

Federated Learning reinforcement-learning +1

Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming

no code implementations15 Jun 2022 Tong Liu, Lei Lei, Kan Zheng, Kuan Zhang

Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle.

reinforcement-learning Reinforcement Learning (RL)

Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning

no code implementations3 Jun 2022 Jiaju Qi, Lei Lei, Kan Zheng, Simon X. Yang

Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management.

energy management Management +2

A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud

1 code implementation31 May 2022 Siqiao Xue, Chao Qu, Xiaoming Shi, Cong Liao, Shiyi Zhu, Xiaoyu Tan, Lintao Ma, Shiyu Wang, Shijun Wang, Yun Hu, Lei Lei, Yangfei Zheng, Jianguo Li, James Zhang

Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.

Decision Making Management +3

Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information

no code implementations28 Mar 2022 Lei Lei, Tong Liu, Kan Zheng, Lajos Hanzo

In this context, the value of V2X communications for DRL-based platoon controllers is studied with an emphasis on the tradeoff between the gain of including exogenous information in the system state for reducing uncertainty and the performance erosion due to the curse-of-dimensionality.

reinforcement-learning Reinforcement Learning (RL)

Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling

no code implementations13 Oct 2021 Yaxiong Yuan, Lei Lei, Thang X. Vu, Zheng Chang, Symeon Chatzinotas, Sumei Sun

Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services.

Management Meta-Learning +1

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

no code implementations26 Aug 2021 Jiaju Qi, Qihao Zhou, Lei Lei, Kan Zheng

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL).

Edge-computing Federated Learning +2

An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids

no code implementations10 Feb 2021 Hossein Mohammadi Rouzbahani, Hadis Karimipour, Lei Lei

Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network.

Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization

no code implementations24 Jun 2020 Yaxiong Yuan, Lei Lei, Thang Xuan Vu, Symeon Chatzinotas, Sumei Sun, Bjorn Ottersten

The conventional RL/DRL, e. g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i. e., exponentially increasing action space and infeasible actions.

Combinatorial Optimization Q-Learning +1

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

no code implementations22 Jul 2019 Lei Lei, Yue Tan, Kan Zheng, Shiwen Liu, Kuan Zhang, Xuemin, Shen

Next, a comprehensive survey of the state-of-art research on DRL for AIoT is presented, where the existing works are classified and summarized under the umbrella of the proposed general DRL model.

Decision Making reinforcement-learning +1

Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing

no code implementations19 Jun 2019 Lei Lei, Huijuan Xu, Xiong Xiong, Kan Zheng, Wei Xiang, Xianbin Wang

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing.

Edge-computing reinforcement-learning +2

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

no code implementations17 Apr 2019 Lei Lei, Jiaju Qi, Kan Zheng

In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN).

Clustering Information Retrieval +2

An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications

no code implementations12 Feb 2019 Yue Tan, Kan Zheng, Lei Lei

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications.

General Classification Keyword Spotting

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