Search Results for author: HaiYan Wang

Found 25 papers, 4 papers with code

A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification

1 code implementation12 Jun 2024 Wei Huang, Ning Wang, Panpan Feng, HaiYan Wang, Zongmin Wang, Bing Zhou

MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism.

ECG Classification

Predictive Analysis for Optimizing Port Operations

no code implementations25 Jan 2024 Aniruddha Rajendra Rao, HaiYan Wang, Chetan Gupta

This research addresses a significant gap in port analysis models for vessel Stay and Delay times, offering a valuable contribution to the field of maritime logistics.

Decision Making Diversity +1

Agent based modelling for continuously varying supply chains

no code implementations24 Dec 2023 Wan Wang, HaiYan Wang, Adam J. Sobey

Academic/practical: However, learning in continuously varying environments remains a challenge in the reinforcement learning literature. Methodology: This paper therefore seeks to address whether agents can control varying supply chain problems, transferring learning between environments that require different strategies and avoiding catastrophic forgetting of tasks that have not been seen in a while.

reinforcement-learning Reinforcement Learning (RL)

A novel feature selection method based on quantum support vector machine

no code implementations29 Nov 2023 HaiYan Wang

We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features.

feature selection Quantum Machine Learning

Several fitness functions and entanglement gates in quantum kernel generation

no code implementations22 Aug 2023 HaiYan Wang

Furthermore, we demonstrate that the separability indexes of data can be leveraged to estimate the number of non-local gates required for the quantum support vector machine's feature maps.

Quantum Machine Learning

A Functional approach for Two Way Dimension Reduction in Time Series

no code implementations1 Jan 2023 Aniruddha Rajendra Rao, HaiYan Wang, Chetan Gupta

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision.

Decoder Dimensionality Reduction +3

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation

no code implementations4 Nov 2022 Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, HaiYan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wangg, Dong Liang, Tianye Niu, Xiaohua Zhuc, Zhanli Hua

In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.

Segmentation STS +1

Sequential Point Clouds: A Survey

no code implementations20 Apr 2022 HaiYan Wang, YingLi Tian

Point cloud has drawn more and more research attention as well as real-world applications.

Autonomous Driving object-detection +2

Optimal service resource management strategy for IoT-based health information system considering value co-creation of users

no code implementations5 Apr 2022 Ji Fang, Vincent CS Lee, HaiYan Wang

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

Management

Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting

no code implementations3 Apr 2022 Ji Fang, Vincent CS Lee, Hao Ji, HaiYan Wang

This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.

Management reinforcement-learning +2

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

1 code implementation29 Mar 2022 Ziyue Feng, Liang Yang, Longlong Jing, HaiYan Wang, YingLi Tian, Bing Li

Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.

Depth Prediction Disentanglement +4

K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents

no code implementations18 Jan 2022 Hamed Khorasgani, HaiYan Wang, Hsiu-Khuern Tang, Chetan Gupta

Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of agents.

Management reinforcement-learning +1

An Offline Deep Reinforcement Learning for Maintenance Decision-Making

no code implementations28 Sep 2021 Hamed Khorasgani, HaiYan Wang, Chetan Gupta, Ahmed Farahat

Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years.

Decision Making reinforcement-learning +1

Deep Reinforcement Learning with Adjustments

no code implementations28 Sep 2021 Hamed Khorasgani, HaiYan Wang, Chetan Gupta, Susumu Serita

Our method can learn complex policies to achieve long-term goals and at the same time it can be easily adjusted to address short-term requirements without retraining.

Q-Learning reinforcement-learning +1

FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

1 code implementation CVPR 2021 HaiYan Wang, Jiahao Pang, Muhammad A. Lodhi, YingLi Tian, Dong Tian

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc.

Autonomous Driving Robot Navigation +1

Interactions of Linguistic and Domain Overhypotheses in Category Learning

no code implementations28 Dec 2020 Luann C. Jung, HaiYan Wang

For humans learning to categorize and distinguish parts of the world, the set of assumptions (overhypotheses) they hold about potential category structures is directly related to their learning process.

Computational Engineering, Finance, and Science

A Non-linear Function-on-Function Model for Regression with Time Series Data

no code implementations24 Nov 2020 Qiyao Wang, HaiYan Wang, Chetan Gupta, Aniruddha Rajendra Rao, Hamed Khorasgani

Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations.

regression Time Series +1

Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching

no code implementations9 Nov 2020 Hamed Khorasgani, HaiYan Wang, Chetan Gupta

In this paper, we review the main challenges in using deep RL to address the dynamic dispatching problem in the mining industry.

reinforcement-learning Reinforcement Learning (RL)

Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports

no code implementations4 Sep 2020 Nao Yamamoto, HaiYan Wang

In June 2020, Arizona, U. S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May.

Populations and Evolution Physics and Society

GPR-based Subsurface Object Detection and Reconstruction Using Random Motion and DepthNet

no code implementations20 Aug 2020 Jinglun Feng, Liang Yang, HaiYan Wang, Yifeng Song, Jizhong Xiao

This system is composed of three modules: 1) visual inertial fusion (VIF) module to generate the pose information of GPR device, 2) deep neural network module (i. e., DepthNet) which detects B-scan of GPR image, extracts hyperbola features to remove the noise in B-scan data and predicts dielectric to determine the depth of the objects, 3) 3D GPR migration module which synchronizes the pose information with GPR scan data processed by DepthNet to reconstruct and visualize the 3D underground targets.

Depth Estimation Depth Prediction +3

Using Networks and Partial Differential Equations to Predict Bitcoin Price

no code implementations8 Jan 2020 Yufang Wang, HaiYan Wang

The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.

FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection

no code implementations11 Dec 2017 Zexun Zhou, Zhongshi He, Ziyu Chen, Yuanyuan Jia, HaiYan Wang, Jinglong Du, Dingding Chen

The proposed network is consist of multiple context modeling and prediction modules, which are in order to detect small, blur, occluded and diverse pose faces.

Decoder Face Detection +1

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