Xiaoli Li

Nanjing Tech University

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Zhenlong Du

Nanjing Tech University

Yefei Liu

Nanjing Tech University

Dong Sheng Chen

Nanjing Tech University

Hong Jiang

Nanjing Tech University

Rizhi Chen

Nanjing Tech University

Shuailing Zhou

Nanjing Tech University

Guomei Song

Nanjing Tech University

Yujia Yan

University of Rochester

Kangkang Shen

Hebei Medical University

Yang Han

The Ohio State University

Cooperative Institutions

Nanjing Tech University

Beijing University of Technology

Xi'an University of Technology

Chinese Academy of Sciences

State Key Laboratory of Materials-Oriented Chemical Engineering

The Synergetic Innovation Center for Advanced Materials

Shanghai Jiao Tong University

Huazhong University of Science and Technology

Sun Yat-sen University

Nanjing University

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Rainfall runoff prediction via a hybrid model of neighbourhood rough set with LSTM

International Journal of Embedded Systems (2020)

Xiaoli Li Guomei Song Shuailing Zhou Yujia Yan Zhenlong Du

Accurate rainfall runoff prediction is crucial for flood forecasting and water resources management, and it remains a challenging issue in hydrological information processing. The most challenging problem is that the hydrological information has a strong locality and nonlinearity, which leads to poor prediction accuracy. Neighbourhood rough sets has strong capability on data classification and reduction, which can reduce those redundant rainfall runoff data. Long short-term memory (LSTM) network is a special recurrent neural network (RNN) that is an excellent variant of RNN, it is good at handling the time series data. In the paper, a hybrid model of neighbourhood rough sets with LSTM is proposed, which is used for the rainfall runoff prediction. The experimental results show that the presented model could improve the training speed of LSTM and achieve much higher prediction accuracy than the conventional rainfall runoff prediction methods.

Neighbourhood (mathematics)

10.1504/ijes.2020.110654

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Citations (6)

A Mobile Robot Path Planning Method Based on Safe Pathfinding Guidance

Tong Bie Xiaoqing Zhu Xiaoli Li Xiaogang Ruan

The problem of path planning in the field of robot navigation is now a major research hot spot. On the premise of ensuring that the robot can successfully navigate to the target point, the safety of the path also needs to be considered. In order to find a safer path, a safe pathfinding path planning method is proposed, which introduces two safety parameters that affect the path selection: hazard coefficient and movement coefficient. After defining two security parameters, design an appropriate reward function and use A2C algorithm and PPO algorithm to guide the robot to conduct reinforcement learning. The experiment will be conducted on a two-dimensional grid map containing various obstacles. By conducting comparative experiments, it is verified that the safety pathfinding method proposed in this paper is feasible and reasonable, and can enable the robot to choose a safer road instead of a faster road when planning the path.

SAFER

Mobile Robot Navigation

Any-angle path planning

10.1109/ccdc52312.2021.9601697

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Citations (1)

Custom attribute image generation based on improved StyleGAN2

Guochao Gao Xiaoli Li Zhenlong Du

The continuous development of generative adversarial networks [1] has made it easier and easier to generate forged face images. Using generative adversarial networks can easily generate a large number of face images. However, the face images generated by generative adversarial networks are not of high quality and the face attributes cannot be controlled. In order to solve these problems, In this paper, we first use a style-based generative adversarial network [2] as a face image generator to generate high-quality face images, and then we create a convolutional neural network [3] that can predict the age and gender attributes of the input face image to control the age and gender of the generated face image. We experimentally demonstrated the reliability of the idea and finally achieved face image generation with customized attributes.

Generative adversarial network

10.1145/3587716.3587771

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Citations (1)

Intelligent pattern reconstruction based on primitive spatial relationship adaptive transformation

Fangbing Xia Xiaoli Li Zhenlong Du Lu Zhao

A novel method is proposed to construct relatively rich vector patterns from existing examples to address the problem of excessively simple and coarse details in automatically generated patterns. This method involves several key steps, including the extraction of vectorized primitives, the construction of primitive relationships, and the intelligent generation of patterns through optimization algorithms. Specifically, vectorized primitives are extracted from raster images, and directed graphs are used to establish relationships between primitives, taking into account the geometric relationships of the graph. Primitive relationships are calculated based on the extracted geometric relationships, and relevant constraints are used to transform the original pattern. The transformed pattern is then optimized to produce a more harmonious and aesthetically pleasing pattern variation. Experimental results show that the proposed algorithm can generate a diverse set of novel pattern variants, and the optimized variants demonstrate high levels of harmony and aesthetics. Users have the ability to influence the direction of pattern generation by adjusting the primitives, enabling them to compare and select the generated pattern variants that align with their implicit preferences. The proposed method provides an effective solution for pattern generation, catering to various requirements in practical applications and delivering a range of diverse pattern graphics for products.

10.1117/12.3021534

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High fidelity single image blind deblur via GAN

Wireless Networks (2021)

Xiaoli Li Gen Li Zhenlong Du

Deblurring

Kernel (algebra)

Discriminator

Sharpening

10.1007/s11276-020-02496-9

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Citations (3)

Bidirectional Quantum Teleportation with GHZ States and EPR Pairs via Entanglement Swapping

International Journal of Theoretical Physics (2020)

Zhenlong Du Xiaoli Li Xuejun Liu

Quantum teleportation

Teleportation

Superdense coding

10.1007/s10773-019-04355-6

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Citations (12)

Exposing Blur Kernel from Retouch Image

Zhenlong Du Xiaoli Li Yanwen Guo

The blurring in image comes either from the acquisition noise, or from image editing operation. The produced adverse noise during acquisition need to be eliminated, and the blurring generated by editing should be known in digital forensics, so the blur kernel recovery is significant in community of image processing and computer graphics. In the log-fourier domain, the images before and after blurring bears the isometry, therein an approach of gaussian blur kernel recovery based on Riemannian geodesic is proposed, which evaluates the blurring-invariant quantity between the original image and blurred one, and recovers the blur kernel from blur image. The presented method is testified in images convolved by gaussian blur, median blur, box blur and multiple gaussian blur, and the kernel could be robustly recovered from gaussian blur image. Moreover, the discussed method is capable of authenticating the retouch image generated from blurring.

Gaussian blur

Kernel (algebra)

Gaussian filter

Gaussian Noise

10.1109/cadgraphics.2013.70

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Citations (2)

Comparison of the Calibrated Objective Functions for Low Flow Simulation in a Semi-Arid Catchment

Water (2022)

Xue Yang Chengxi Yu Xiaoli Li Jungang Luo Jiancang Xie

Low flow simulation by hydrological models is a common solution in water research and application. However, knowledge about the influence of the objective functions is limited in relatively arid regions. This study aims to increase insight into the difference between the calibrated objective functions by evaluating eight objectives in three different classes (single objectives: KGE(log(Q)) and KGE(1/Q); multi objectives: KGE(Q)+KGE(log(Q)), KGE(Q)+KGE(1/Q), KGE(Qsort)+KGE(log(Qsort)) and KGE(Qsort)+KGE(1/Qsort); Split objectives: split KGE(Q) and split (KGE(Q)+KGE(1/Q))) in Bahe, a semi-arid basin in China. The calibrated model is Xin An Jiang, and the evaluation is repeated under varied climates. The results show a clear difference between objective functions for low flows, and the mean of KGE and logarithmic transformed-based KGE in time series (KGE(Q)+KGE(log(Q))) presents the best compromise between the estimation for low flows and general simulation. In addition, the applications of the inverse transformed-based KGE (KGE(1/Q)) and the Flow Duration Curve-based series (Qsort) in objectives are not suggested.

10.3390/w14172591

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Citations (4)

Deterministic joint remote state preparation of four-qubit cluster type with tripartite involvement

Quantum Information Processing (2019)

Zhenlong Du Xiaoli Li

ENCODE

One-way quantum computer

10.1007/s11128-019-2535-8

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Citations (15)

Study on Rainfall Runoff Prediction of Basin Based on Digital Twin

J. B. Chen Xiaoli Li Weiyan Liu Zhenlong Du

10.1145/3587716.3587811

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Citations (0)