Haoran Wang

Jilin University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Mingyang Sun

Jilin University

Tianyu Zhang

East China University of Science and Technology

Kaiwen Song

Jilin University

Yanbin Shao

Jilin University

Xinyu Guo

Jilin University

Songfeng Xue

Jilin University

Wenzhong Tang

Research Center for Eco-Environmental Sciences

Hongwei Zhang

Jilin University

Xun Lin

Beihang University

Yixuan Li

Beijing University of Posts and Telecommunications

Cooperative Institutions

Jilin University

Fudan University

Chinese Academy of Sciences

Beihang University

Harbin Institute of Technology

State Key Laboratory on Integrated Optoelectronics

Jilin Medical University

University of Electronic Science and Technology of China

First Hospital of Jilin University

Beijing University of Posts and Telecommunications

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Education, Income, and Happiness: Evidence From China

Frontiers in Public Health (2022)

Dongliang Yang Ge Zheng Haoran Wang Mingna Li

Happiness is the continuous joy that people experience when they are satisfied with their lives long term, and is the ultimate goal pursued by all citizens. In this study, we investigate the relationship between education, income, and happiness in the migrant population in China. Using 1,31,186 individuals in the 2012 China Migrants Dynamic Survey (CMDS) as research samples, the estimated results of ordinal logistic regression show that education, including secondary education and higher education, has a significant and direct impact on individual happiness, and that the impact of education on happiness can also be mediated by income as an intermediary mechanism. In addition, factors such as gender, flow distance, flow time, employment status, type of housing, number of children, degree of preference for the city, and degree of discrimination by locals have obvious effects on happiness. This work provides important insights for countries seeking to implement an active education policy in order to increase economic income and thus achieve the development goal of universal happiness among their citizens.

Ordered logit

10.3389/fpubh.2022.855327

Cite

Citations (36)

CDS-Net: Cooperative dual-stream network for image manipulation detection

Pattern Recognition Letters (2023)

Haoran Wang Jiahao Deng Xun Lin Wenzhong Tang Shuai Wang

Robustness

Bitstream

Stream Processing

10.1016/j.patrec.2023.11.005

Cite

Citations (3)

A multilayer perceptron-based model applied to histopathology image classification of lung adenocarcinoma subtypes

Frontiers in Oncology (2023)

Mingyang Liu Liyuan Li Haoran Wang Xinyu Guo Yunpeng Liu

Lung cancer is one of the most common malignant tumors in humans. Adenocarcinoma of the lung is another of the most common types of lung cancer. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the fifinal diagnosis of many diseases. Thus, pathological diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far exceeds the number of pathologists, especially in the treatment of lung cancer in less-developed countries.This paper proposes a multilayer perceptron model for lung cancer histopathology image detection, which enables the automatic detection of the degree of lung adenocarcinoma infifiltration. For the large amount of local information present in lung cancer histopathology images, MLP IN MLP (MIM) uses a dual data stream input method to achieve a modeling approach that combines global and local information to improve the classifification performance of the model. In our experiments, we collected 780 lung cancer histopathological images and prepared a lung histopathology image dataset to verify the effectiveness of MIM.The MIM achieves a diagnostic accuracy of 95.31% and has a precision, sensitivity, specificity and F1-score of 95.31%, 93.09%, 93.10%, 96.43% and 93.10% respectively, outperforming the diagnostic results of the common network model. In addition, a number of series of extension experiments demonstrated the scalability and stability of the MIM.In summary, MIM has high classifification performance and substantial potential in lung cancer detection tasks.

Histopathology

Multilayer perceptron

10.3389/fonc.2023.1172234

Cite

Citations (6)

A Novel Nucleation Inducer for Ultrathin Au Anodes in High Efficiency and Flexible Organic Optoelectronic Devices

Advanced Optical Materials (2019)

Haoran Wang Zhenyu Wang Xiangchen Xu Wenzhuo Zhao Dan Wu

Abstract Methyl‐terminated alucone films are prepared as a nucleation inducer by molecular layer deposition (MLD), increasing the surface free energy (SFE) to help the nucleation of Au electrodes. By controlling the surface groups of MLD alucone, the SFE of the films reach 93.8 dyne cm −1 , which is significantly larger than the 75.94 dyne cm −1 of commonly used wetting layer ZnS. As a result, Au films deposited on this novel nucleation inducer show a layered growth at a thickness of 5 nm, and thus exhibit high photoelectric performance and a bending life of more than 1500 times. When this high‐performance electrode is applied in organic optoelectronic devices, current efficiency enhancement of 24.4% and 12.7% are achieved in organic light‐emitting devices (OLEDs) and flexible perovskite light‐emitting devices (PLEDs), respectively. Also, the flexible PLED maintains 96.4% of the initial luminance after 1000 bends.

Flexible display

Wetting layer

10.1002/adom.201901320

Cite

Citations (10)

Implicit parallelism for neural network acceleration

HAL (Le Centre pour la Communication Scientifique Directe) (2022)

Haoran Wang

Source

Cite

Citations (0)

Soft Particle Clogging in 2D Hoppers

Bulletin of the American Physical Society (2018)

Mia Morrell Haoran Wang Eric R. Weeks

Clogging

Particle (ecology)

Source

Cite

Citations (0)

Seeking Certainty In Uncertainty: Dual-Stage Unified Framework Solving Uncertainty in Dynamic Facial Expression Recognition

arXiv (Cornell University) (2024)

Haoran Wang Xinji Mai Zeng Tao Xuan Tong Junxiong Lin

The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by training on voluminous datasets. Yet, the DFER datasets encompass a substantial volume of noise data. Noise arises from low-quality captures that defy logical labeling, and instances that suffer from mislabeling due to annotation bias, engendering two principal types of uncertainty: the uncertainty regarding data usability and the uncertainty concerning label reliability. Addressing the two types of uncertainty, we have meticulously crafted a two-stage framework aiming at \textbf{S}eeking \textbf{C}ertain data \textbf{I}n extensive \textbf{U}ncertain data (SCIU). This initiative aims to purge the DFER datasets of these uncertainties, thereby ensuring that only clean, verified data is employed in training processes. To mitigate the issue of low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which assesses sample weights and prunes those deemed unusable due to their low weight. For samples with incorrect annotations, the Fine-Grained Correction (FGC) stage evaluates prediction stability to rectify mislabeled data. Moreover, SCIU is conceived as a universally compatible, plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methodologies. Rigorous experiments across prevalent DFER datasets and against numerous benchmark methods substantiates SCIU's capacity to markedly elevate performance metrics.

Certainty

Expression (computer science)

Facial expression recognition

10.48550/arxiv.2406.16473

Cite

Citations (0)

Exposing image splicing traces in scientific publications via uncertainty-guided refinement

Patterns (2024)

Xun Lin Wenzhong Tang Haoran Wang Yizhong Liu Yakun Ju

10.1016/j.patter.2024.101038

Cite

Citations (0)

Free Vibration Characteristics of Composite Hybrid Lattice Sandwich Panels Through Finite Element Method

Weimeng Xie Mengzhen Li Haoran Wang

The dynamic characteristics of composite hybrid lattice panels using the finite element method are studied. Firstly, six different hybrid schemes are designed based on different structural forms and various types of hybrid composites. The finite element modeling of hybrid composite lattice panels under various boundary conditions is performed. Mesh convergence analysis is carried out before parametric studies are given and discussed in detail. The effects of hybrid schemes, lamination schemes, thickness ratios, layup angles, and boundary conditions on the fundamental frequencies and modes of hybrid lattice sandwich panels are studied. The results show that higher natural frequencies can be obtained by placing high modulus CFRP in the lattice core, and different hybrid schemes have a more significant effect on high mode frequencies than low mode frequencies. The present data in this paper can also serve as a benchmark for future researches.

Lattice (music)

Natural frequency

10.1109/isssr58837.2023.00061

Cite

Citations (0)

Intelligent water perimeter security event recognition based on NAM-MAE and distributed optic fiber acoustic sensing system

Optics Express (2023)

Mingyang Sun Miao Yu Haoran Wang Kaiwen Song Xinyu Guo

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry can realize the distributed monitoring of multi-point disturbances along an optical fiber, thus making it suitable for water perimeter security applications. However, owing to the complex environment and the production of various noises by the system, continuous and effective recognition of disturbance signals becomes difficult. In this study, we propose a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on the novel mask mode of a Masked Autoencoders (MAE) and applies it to the intelligent event recognition in DAS. In this method, fewer but more accurate features are fed into the deep learning model for recognition by directly shielding the noise. Taking the fading noise generated by the system as an example, data on water perimeter security events collected in DAS underwater acoustic experiments are used. The NAM-MAE is compared with other models. The results indicate higher training accuracy and higher convergence speed of NAM-MAE than other models. Further, the final test accuracy reaches 96.6134%. It can be demonstrated that the proposed method has feasibility and superiority.

Distributed Acoustic Sensing

10.1364/oe.498554

Cite

Citations (5)