Di Fan

University of California, Irvine

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Xiaopeng Chen

Beijing Institute of Technology

Andrei Irimia

University of Southern California

Taoran Zhang

BOE Technology Group (China)

Yifan Wu

Shandong University

Yunhui Liu

Chinese University of Hong Kong

Nikhil N. Chaudhari

University of Southern California

Hyunwoo Kim

Samsung (South Korea)

Lauren J. O’Donnell

Harvard University

Fan Zhang

University of Electronic Science and Technology of China

Qiang Huang

Beijing Institute of Technology

Cooperative Institutions

Beijing Institute of Technology

University of Southern California

Southern California University for Professional Studies

Chinese Academy of Sciences

Huazhong University of Science and Technology

Fudan University

Beihang University

Beijing Advanced Sciences and Innovation Center

Ministry of Education of the People's Republic of China

Ministry of Agriculture and Rural Affairs

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Using Computer New Media Technology to Carry out Reform and Innovation Research on the Training of Sports and Beauty Dance Skills in Various Colleges and Universities

Di Fan

As a performing art, dance not only reflects the production and life of contemporary people, but also faces the task of how to advance with the times. Computer new media technology bears great responsibility for the reform and innovation of sports beauty dance skills training in colleges and universities. How to use computer new media technology to promote the orderly and efficient development of sports beauty dance skills training is worthy of consideration and discussion by sports beauty dance educators. This article takes the college sports and beauty dance skills training as the research object, combined with computer new media technology, and explores its reform and innovation development path. This article uses questionnaire survey and expert interview methods to explore the application status of computer new media technology in college sports and dance skills training, and analyzes the preference and effect of college students' use of new media platforms for dance skills training. This article analyzes the advantages of the application of computer new media technology in the training of sports and dance skills in colleges and universities, as well as the reform and innovation strategies.

Beauty

10.1109/fones-aiot54873.2021.00068

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Citations (2)

Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization

Energies (2018)

Dong Xu Luo Yu Zhiyu Lv Jiahuang Zhang Di Fan

The traditional leader-follower formation algorithm can realize the formation of multiply robotic fishes, but fails to consider the energy consumption during the formation. In this paper, the energy optimized leader-follower formation algorithm has been investigated to solve this problem. Considering that the acceleration of robotic fish is tightly linked to the motion state and energy consumption, we optimize the corresponding control parameters of the acceleration to reduce energy consumption during the formation via particle swarm algorithm. The whole process has been presented as follows: firstly we realize the formation on the base of the kinematic model with leader-follower formation algorithm; then the energy consumption on the base of dynamical model are derived; finally we seek the optimal control parameters based on the particle swarm optimization (PSO) algorithm. The dynamics simulation of the energy optimization scheme is conducted to verify the functionality of the proposed energy optimized leader-follower formation algorithm via MATLAB. The optimized results demonstrate that the proposed approach, reducing energy consumption during the formation, is superior to the traditional leader-follower formation algorithm and can reduce energy consumption during the formation. The novelty of the work is that we can reduce the energy consumption during the process of formation by considering the energy consumption, which is a gap in the current research field.

10.3390/en11082023

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Citations (4)

DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain

Lecture notes in computer science (2022)

Garima Aggarwal Chun-Yin Huang Di Fan Xiaoxiao Li Zehua Wang

Robustness

Smart contract

10.1007/978-3-031-18523-6_10

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Citations (0)

Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

Applied Sciences (2019)

Di Fan Hyunwoo Kim Junmo Kim Yunhui Liu Qiang Huang

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

Task Analysis

10.3390/app9122535

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Citations (3)

Mapping Cerebral Connectivity Changes after Mild Traumatic Brain Injury in Older Adults Using Diffusion Tensor Imaging and Riemannian Matching of Elastic Curves

2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) (2020)

Andrei Irimia Di Fan Nikhil N. Chaudhari Van Ngo Fan Zhang

Although diffusion tensor imaging (DTI) can identify white matter (WM) alterations due to mild cases of traumatic brain injury (mTBI), the task of within-subject longitudinal matching of DTI streamlines remains challenging in this condition. Here we combine (A) automatic, atlas-informed labeling of WM streamline clusters with (B) streamline prototyping and (C) Riemannian matching of elastic curves to quantitate within-subject WM changes, focusing on the arcuate fasciculus. The approach is demonstrated in a group of geriatric mTBI patients imaged acutely and ~6 months post-injury. Results highlight the utility of differential geometry approaches when quantifying brain connectivity alterations due to mTBI.

10.1109/isbi45749.2020.9098476

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Citations (6)

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

Insight - Non-Destructive Testing and Condition Monitoring (2018)

Shoufeng Jin Di Fan Reza Malekian Zhihe Duan LI Zhi-xiong

The m anufacturing line is a fundam ental element in short filam ent fibre production, in which the gearbox is the key mechanical part.Any faults in the gearbox w ill greatly affect the quality o f the short filam ent fibres.However, due to the harsh working environment, the gearbox is vulnerable to failure.Due to the complexity o f the m anufacturing line, effective and efficient feature extraction o f gear faults is still a challenge.To this end, a new fault diagnosis m ethod based on image recognition is proposed in this paper for gear fault detection in fibre m anufacturing lines.In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals.The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction.The grey-level co-occurrence m atrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence matrix.Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location.Experimental data acquired from a real-world m anufacturing line o f short filam ent fibres are used to evaluate the performance o f the proposed image-based gear fa ult detection method.The analysis results demonstrate th a t the newly proposed m ethod is capable o f accurate gear fault detection in fibre manufacturing lines.

Line (geometry)

10.1784/insi.2018.60.5.270

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Citations (15)

Application of 3D Human Motion Recognition in Dance System

Advances in intelligent systems and computing (2021)

Di Fan

Expression (computer science)

Motion Capture

Inheritance

10.1007/978-3-030-70042-3_9

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Multimodal Imaging of Cerebral Microhemorrhages and White Matter Degradation in Geriatric Patients with Mild Traumatic Brain Injury

Methods in molecular biology (2020)

Maria Calvillo Di Fan Andrei Irimia

Neurovascular bundle

10.1007/978-1-0716-0592-9_20

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Citations (1)

FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

arXiv (Cornell University) (2021)

Chaoyang He Alay Shah Zhenheng Tang Di Fan Adarshan Naiynar Sivashunmugam

Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.

Benchmarking

Benchmark (surveying)

Bridge (graph theory)

10.48550/arxiv.2111.11066

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Citations (33)

Eye Gaze Based 3D Triangulation for Robotic Bionic Eyes

Sensors (2020)

Di Fan Yanyang Liu Xiaopeng Chen Fei Meng Xilong Liu

Three-dimensional (3D) triangulation based on active binocular vision has increasing amounts of applications in computer vision and robotics. An active binocular vision system with non-fixed cameras needs to calibrate the stereo extrinsic parameters online to perform 3D triangulation. However, the accuracy of stereo extrinsic parameters and disparity have a significant impact on 3D triangulation precision. We propose a novel eye gaze based 3D triangulation method that does not use stereo extrinsic parameters directly in order to reduce the impact. Instead, we drive both cameras to gaze at a 3D spatial point P at the optical center through visual servoing. Subsequently, we can obtain the 3D coordinates of P through the intersection of the two optical axes of both cameras. We have performed experiments to compare with previous disparity based work, named the integrated two-pose calibration (ITPC) method, using our robotic bionic eyes. The experiments show that our method achieves comparable results with ITPC.

10.3390/s20185271

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Citations (9)