Zhixuan Zhao

China Southern Power Grid (China)

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Yan Liu

Shanghai Electric (China)

Wenxiang Li

Shandong Academy of Forestry

Haisheng Hong

China Southern Power Grid (China)

Changcheng Yan

Tianjin University of Technology

Mingju Gong

Tianjin University of Technology

Sheng Li

University of Malaya

Xiaohong Ding

Pennsylvania State University

Weidong Xie

Northeastern University

Lehua Qi

Northwestern Polytechnical University

Jiabao Wang

Anhui Polytechnic University

Cooperative Institutions

Chinese Academy of Sciences

Tianjin University of Technology

University of Chinese Academy of Sciences

Fujian Institute of Research on the Structure of Matter

Northwestern Polytechnical University

China Southern Power Grid (China)

Fuzhou University

Beijing Institute of Technology

Wuyi University

PLA Army Engineering University

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Scalable Preparation of Superdurable, Self-Healing, and Biocompatible Superhydrophobic Poly(ethylene terephthalate) Fabrics

ACS Applied Materials & Interfaces (2024)

Xuan Zhou Wudi Zhang Shuqiang Peng Zhixuan Zhao Wenxing Chen

The chemical inertness of poly(ethylene terephthalate) (PET) fabrics poses challenges in achieving superhydrophobic coatings with durable adhesion on their surfaces. Conventional surface modification methods such as alkali etching and plasma etching typically compromise the favorable mechanical properties of PET. In this study, polydopamine (PDA) was utilized to functionalize the PET fabric nondestructively by creating robust and reactive hydroxyl and amine groups on its surface, which were subsequently used as a binder of superhydrophobic modifiers such as fluorine-free octadecyltrichlorosilane (OTS). By utilizing PDA to provide reactive groups, OTS undergoes self-assembly through hydrolysis on the surface of the PET fabric without introducing any inorganic nanoparticles while simultaneously forming low surface energy, strong covalent bonds, and rough surfaces. This robust material system provides a novel strategy to design and prepare superhydrophobic PET fabrics that can withstand extreme conditions and maintain superb water repellency even after 1000 times of abrasion and 100 washing cycles. Additionally, the room-temperature self-assembly properties of OTS provide the modified PET fabrics with efficient and repeatable room-temperature self-healing capability. This entire process through an environmentally friendly two-step immersion method enables large-scale production of superhydrophobic PET fabrics with wide applications in sports shoes and clothing.

Biocompatible material

Poly ethylene

10.1021/acsami.4c17473

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A Surface Defect Detection Method Based on Positive Samples

Lecture notes in computer science (2018)

Zhixuan Zhao Bo Li Rong Dong Peng Zhao

Autoencoder

Convolution (computer science)

Sample (material)

10.1007/978-3-319-97310-4_54

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Citations (113)

Pedestrian dangerous action recognition in infrared image based on Resnet18 network

Yujuan Wang Dong Xuan Zhixuan Zhao W. Shan

The effective identification of pedestrian dangerous actions at night was a core task of unmanned driving and intelligent assistant driving system. Limited by the network depth and learning ability of traditional convolutional neural network, the performance of the algorithm and its improvement were still unsatisfactory. Considering the imaging characteristics of the camera at night, this paper proposed an infrared pedestrian dangerous action recognition algorithm based on residual network to recognize pedestrian actions at night. Resnet18 network framework was adopted according to the characteristics of infrared images and the scale of problems. In order to adapt to the network input format, the infrared image in the database were preprocessed. The experimental results in the actual infrared pedestrian dangerous action dataset indicated that the mean precision of the proposed method for six types of dangerous actions was improved to 98.3%, and the average recall rate was improved to 98.1%, which was better than the traditional recognition method.

Identification

10.1117/12.2667410

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Wind power forecasting based on SCINet, reversible instance normalization, and knowledge distillation

Journal of Renewable and Sustainable Energy (2023)

Mingju Gong Wenxiang Li Changcheng Yan Yan Liu Sheng Li

Wind energy plays a crucial role as a clean energy source in the electricity system. The unpredictability of wind power makes it more challenging to put into use in comparison to thermal power generation. Accurate wind power prediction algorithms are of great importance for allocation and deployment of wind power. In this paper, a novel time-series forecasting model, SCINet, is used for short-term wind power forecasting and achieves high forecasting accuracy. Furthermore, the addition of reversible instance normalization (RevIN) to SCINet effectively alleviates the shift problem that arises in time series forecasting tasks. This enhancement further improves the model's forecasting ability. Finally, this paper uses knowledge distillation to get a small model that could speed up the computing and save memory resources. The source code is available at https://github.com/raspnew/WPF.git.

Wind Power Forecasting

Normalization

10.1063/5.0166061

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Short-term wind power forecasting model based on temporal convolutional network and Informer

Energy (2023)

Mingju Gong Changcheng Yan Wei Xu Zhixuan Zhao Wenxiang Li

Wind Power Forecasting

Predictive power

Predictive modelling

Probabilistic Forecasting

10.1016/j.energy.2023.129171

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Citations (44)

Research on vegetable pricing and replenishment strategy based on time series model and particle swarm optimization

Academic Journal of Computing & Information Science (2023)

Mengxiao Li Wei Li Zhixuan Zhao

The pricing and replenishment strategy of vegetables is very important for supermarkets, and accurate prediction and appropriate pricing replenishment strategies are of great significance for saving costs, improving supply chain management, and reducing the loss rate of vegetables. In order to accurately predict the pricing strategy and replenishment of vegetables every day in the coming week, based on the time series prediction model and particle swarm optimization algorithm, combined with the advantages of MATLAB in processing cleaning data, the pricing and replenishment strategy model that maximizes the revenue of supermarkets was constructed. Finally, based on the data, it is concluded that the maximum income of the supermarket in the coming week is 17108 yuan, and the maximum income per day is 2108.20 yuan.

Particle (ecology)

10.25236/ajcis.2023.061320

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Direct Fabrication of Micron-Thickness PVA-CNT Patterned Films by Integrating Micro-Pen Writing of PVA Films and Drop-on-Demand Printing of CNT Micropatterns

Nanomaterials (2021)

Jun Luo Zhixuan Zhao Lehua Qi Hongcheng Lian Yufang Zhao

The direct fabrication of micron-thickness patterned electronics consisting of patterned PVA films and CNT micropatterns still faces considerable challenges. Here, we demonstrated the integrated fabrication of PVA films of micron-thickness and CNT-based patterns by utilising micro-pen writing and drop-on-demand printing in sequence. Patterned PVA films of 1–5 μm in thickness were written first using proper micro-pen writing parameters, including the writing gap, the substrate moving velocity, and the working pressure. Then, CNT droplets were printed on PVA films that were cured at 55–65 °C for 3–15 min, resulting in neat CNT patterns. In addition, an inertia-pseudopartial wetting spreading model was established to release the dynamics of the droplet spreading process over thin viscoelastic films. Uniform and dense CNT lines with a porosity of 2.2% were printed on PVA substrates that were preprocessed at 55 °C for 9 min using a staggered overwriting method with the proper number of layers. Finally, we demonstrated the feasibility of this hybrid printing method by printing a patterned PVA-CNT film and a micro-ribbon. This study provides a valid method for directly fabricating micron-thickness PVA-CNT electronics. The proposed method can also provide guidance on the direct writing of other high-molecular polymer materials and printing inks of other nanosuspensions.

Printed Electronics

PDMS stamp

Inkjet Printing

Ribbon

10.3390/nano11092335

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Deep Neural Network-based lap time forecasting of Formula 1 Racing

Applied and Computational Engineering (2024)

Zhixuan Zhao

Making comparisons and analyzing players in the sporting world is extremely valuable. The media, coaching staff, and players all rely on this data to assess performance, develop strategies, and make critical decisions. Therefore, neural networks can be employed to create a practical system that uses previous years data to predict future performance. This paper uses a Deep Neural Network (DNN) to predict the fastest lap time in qualifying for Formula 1 (F1) races. The network categorizes data to learn each drivers performance at each circuit and provides separate predictions. By doing so, it considers the unique characteristics of each driver and track, enabling more accurate predictions. The paper demonstrates that neural networks tend to have better performance and adaptability in such complex environments compared to traditional mathematical methods like linear regression. Neural networks can learn from the data and detect patterns that are difficult to capture with traditional methods. As a result, they can achieve a relatively precise prediction, providing valuable insights and decision-making support for coaches, drivers, and fans.

10.54254/2755-2721/47/20241191

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Highly stable scalable production of porous graphene-polydopamine nanocomposites for drug molecule sensing

Talanta (2024)

Xiaohong Ding Ruiqiang Chen Jie Xu Jiapeng Hu Zhixuan Zhao

10.1016/j.talanta.2024.126990

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Implementation of Probabilistic Operational Scenario Forecasting for Risk Analysis of Power Distribution Systems

Haisheng Hong Xinyuan Ge Zhixuan Zhao Xueyou Huang Yan Deng

Probabilistic Forecasting

10.1109/ssitcon62437.2024.10796644

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