Huiqin Chen

University of Chicago

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Co-Authors

Lei Chen

Hangzhou Dianzi University

Guanjun Zhang

Xi'an Jiaotong University

David Logan

Monash University

Xiexing Feng

Hunan University

Shangce Gao

University of Toyama

Sheng Li

Nanjing University of Chinese Medicine

Qiaoyi Wang

Institute of Tibetan Plateau Research

Emanuel Aldea

Université Paris-Saclay

Sylvie Le Hégarat‐Mascle

Laboratoire des systèmes et applications des technologies de l'information et de l'énergie

Libo Cao

Hunan University

Cooperative Institutions

The University of Texas MD Anderson Cancer Center

Hangzhou Dianzi University

Chinese Academy of Sciences

Hunan University

Zhejiang University

University of Chinese Academy of Sciences

Southeast University

University of Toronto

City Of Hope National Medical Center

Peking University

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Research Field

Research of DOA Estimation Based on Modified MUSIC Algorithms

Liwei Huang Yulin Chen Huiqin Chen

This paper studys several kinds of MUSIC algorithms in DOA estimation, expounds the principle of Root-MUSIC algorithm, beamforming MUSIC algorithm, spatial smoothing MUSIC algorithm, conductes simulation for uniform linear array with Matlab, get the characteristics of three kinds of algorithms, Root-MUSIC algorithm is suitable for high SNR, the performance of beamforming MUSIC depends on the selection of matrix, spatial smoothing MUSIC gets the DOA estimation of coherent signals with the cost of reducing the antenna aperture, provides a reference for practical use.

Multiple signal classification

10.2991/amcce-17.2017.189

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Camera Localization Based on Belief Clustering

Huiqin Chen Emanuel Aldea Sylvie Le Hégarat‐Mascle

This work deals with epipole estimation related to egocentric camera localization in surveillance and security applications. Matching visual features in the images provides some evidences for various solutions, so that epipole localization can be addressed as a fusion task with a large number of sources including outlier ones. In order to deal with source imprecision and uncertainty, we rely on the belief function theory and a 2D framework suited for our application. In this framework, we address the challenges introduced by a large number of sources with a strategy based on clustering and intra-cluster fusion. The proposed method exhibits more robustness in terms of accuracy and precision when compared on real data with the standard algorithms which provide single solution. Since we provide a Basic Belief Assignment as a result, our strategy is particularly adapted for the prospective combination with additional sources of information.

Robustness

Sensor Fusion

10.23919/fusion45008.2020.9190358

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Frontal Crash Protection Performance of Integrated Child Safety Seat

SAE technical papers on CD-ROM/SAE technical paper series (2013)

Libo Cao Ruifeng Zhang Huiqin Chen Xianyang Zhao

Vehicle safety

10.4271/2013-01-1160

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Supplementary Figure-6 from Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting

Jeffrey S. Morris Rajyalakshmi Luthra Yusha Liu Dzifa Y. Duose Wonyul Lee

<p>Nanostring CMS Classifier Performance: Performance of Nanostring 100 gene CMS classifier applied to FFPE samples (6A and 6C) and FF samples (6B and 63D). The top two figures plot 4-class accuracy vs. classification confidence, with the dots marking individual samples either correctly (1.0) or incorrectly (0.0), with color indicat-ing correct CMS. The line contains a generalized additive model (GAM) fit to these data with 95% pointwise confidence bands, and demonstrates that samples classified with greater confidence were more likely to be cor-rectly classified. Figure 6C and 6D plot 4-class accuracy vs. RNA quality, defined as 0nt (FFPE) or RIN (FF). Note that there is little if any association of CMS accuracy with RNA quality, suggesting that the perfor-mance of classifier is robust to RNA quality in this study.</p>

Pointwise

Subtyping

10.1158/1078-0432.22478931.v1

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Personalized neural state segmentation: validating the GSBS algorithm for individual-level fMRI data

Robyn Wilford Huiqin Chen Erika Wharton-Shukster Amy S. Finn Katherine Duncan

Humans segment experience into a nested series of discrete events, separated by neural state transitions that can be identified in fMRI data collected during passive movie viewing. Current neural state segmentation techniques manage the noisiness of fMRI data by modelling groups of participants at once. However, the perception of event boundaries is itself idiosyncratic. As such, we developed a novel denoising pipeline to separate meaningful signal from noise and validated the Greedy State Boundary Search (GSBS) algorithm for use in individual participants. We applied the GSBS to publicly available (1) young adult (YA) and (2) developmental fMRI datasets. After extensive denoising, we confirmed that personalized YA neural state transitions exhibited a canonical temporal cortical hierarchy and were related to normative behavioural boundaries across time in key regions such as the posterior parietal cortex. Further, we used machine learning to show that the strongest neural transitions could be used to predict the timing of normative boundary judgements. Results from the developmental dataset also demonstrated important boundary conditions for estimating personalized neural state transitions. Nonetheless, some brain-behaviour relations were still apparent in individually modelled developmental data. These validations pave the way for applying personalized fMRI modelling to the study of event segmentation; what meaningful insights could we be missing when we average away what makes each of us unique?

10.31219/osf.io/4b3f2

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Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status

Sustainability (2023)

Huiqin Chen Hao Liu Hailong Chen Jing Huang

Precise driving status recognition is a prerequisite for human–vehicle collaborative driving systems towards sustainable road safety. In this study, a simulated driving platform was built to capture multimodal information simultaneously, including vision-modal data representing driver behaviour and sensor-modal data representing vehicle motion. Multisource data are used to quantify the risk of distracted driving status from four levels, safe driving, slight risk, moderate risk, and severe risk, rather than detecting action categories. A multimodal fusion method called vision-sensor fusion transformer (V-SFT) was proposed to incorporate the vision-modal of driver behaviour and sensor-modal data of vehicle motion. Feature concatenation was employed to aggregate representations of different modalities. Then, successive internal interactions were performed to consider the spatiotemporal dependency. Finally, the representations were clipped and mapped into four risk level label spaces. The proposed approach was evaluated under different modality inputs on the collected datasets and compared with some baseline methods. The results showed that V-SFT achieved the best performance with an recognition accuracy of 92.0%. It also indicates that fusing multimodal information effectively improves driving status understanding, and V-SFT extensibility is conducive to integrating more modal data.

Sensor Fusion

Modality (human–computer interaction)

Activity Recognition

Concatenation (mathematics)

Modalities

Discriminative model

10.3390/su15129661

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Citations (3)

Rightly Regarding Computer Figure Education

Yin Shan Acadimic Journal (2000)

Huiqin Chen

Computer figure teading should abide impersonal law of art education. Traditional principle of aesthetic judgment, creating new meaning and composition of a picture stilly is the base of computer art design. Computer can help artist complete repeated and complex work but without thinking.

Computer art

Base (topology)

Source

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Economic Adaptive Cruise Control for Electric Vehicles Based on Multi-step Tree Backtracking Algorithm in Q-Learning Framework

2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2023)

Honglei Chen Chunjie Zhai Jianyun Qiu Junzhe Zhao Bo Wang

Car driving safety, comfort, energy economy and battery life extension are important factors for electric vehicles. To optimize these factors, this paper proposes an economic adaptive cruise control (Eco-ACC) strategy for electric vehicles based on multi-step tree backtracking algorithm in the Q-Learning framework. First, the vehicle's longitudinal dynamics and energy storage models are established, and the control objectives are presented. Then, the control and state variables, reward and value functions are defined, and the Eco-ACC control strategy based on the multi-step tree backtracking algorithm is designed. Finally, simulations are performed, and compared with the benchmark algorithm, the proposed method achieves lower energy consumption and longer battery life.

Backtracking

Tree (set theory)

Algorithm design

10.1109/cvci59596.2023.10397264

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Hybrid Gravitational Search Algorithm with Random-key Encoding Scheme Combined with Simulated Annealing

Huiqin Chen Sheng Li Zheng Tang

Summary This paper is devoted to the presentation of a novel hybrid method by combining gravitational search algorithm (GSA) with simulated annealing (SA) method. In GSA, the representation of the problem on hand is based on the random-key encoding scheme. While GSA is employed as a global search algorithm, a multi-type local improvement scheme is incorporated into it, performing as a local search operator. Furthermore, SA is utilized to manipulate the iteration progress algorithmically. The resultant proposed hybrid random-key gravitational search algorithm (Hr-GSA) is tested on the famous traveling salesman problem. The experimental results show that Hr-GSA is more robust and efficient than other seven traditional population based algorithms, such as genetic algorithm, particle swarm optimization, artificial immune system, and so on.

Gravitational search algorithm

Hill climbing

Random search

Source

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Citations (31)

Driver mental load identification model Adapting to Urban Road Traffic Scenarios

Transportation Safety and Environment (2023)

Jing Huang Wei Wei Xiaoyan Peng Lin Hu Huiqin Chen

Abstract Objective At present, most research on driver mental load identification is based on a single driving scene. However, the driver mental load model established in a road traffic scene is difficult to adapt to the changes of the surrounding road environment during the actual driving process. We proposed a driver mental load identification model which adapts to urban road traffic scenarios. Methods The model includes a driving scene discrimination sub-model and driver load identification sub-model, in which the driving scene discrimination sub-model can quickly and accurately determine the road traffic scene. The driver load identification sub-model selects the best feature subset and the best model algorithm in the scene based on the judgement of the driving scene classification sub-model. Results The results show that the driving scene discrimination sub-model using five vehicle features as feature subsets has the best performance. The driver load identification sub-model based on the best feature subset reduces the feature noise, and the recognition effect is better than the feature set using a single source signal and all data. The best recognition algorithm in different scenarios tends to be consistent, and the support vector machine (SVM) algorithm is better than the K-nearest neighbors (KNN) algorithm. Conclusion The proposed driver mental load identification model can discriminate the driving scene quickly and accurately, and then identify the driver mental load. In this way, our model can be more suitable for actual driving and improve the effect of driver mental load identification.

Identification

Feature (linguistics)

10.1093/tse/tdac076

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