检索结果-内蒙古大学图书馆

7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Fang, Lijin Huang, Yongjie Zhao, Qiankun Wan, Yingcai Northeastern University Faculty of Robot Science and Engineering Shen Yang China

ISBN: (纸本)9781665478571

Obtaining accurate 3D bounding boxes from monocular images is a challenging task. In autonomous driving scenarios, mutual occlusion between objects is a common situation, and that is one of the main factors affecting the accuracy of 3D object detection. Aiming at solving the occlusion problem, this paper proposes a monocular 3D object detection method that integrates occlusion differential processing. We propose an occlusion processing branch to process the filtered occlusion feature maps from global and optimize the 3D prediction results with predicted occlusion compensation. We validate our method on the KITTI 3D object detection benchmark, and experiments show that our prediction accuracy for occluded objects is significantly improved. © 2022 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Sampled-data synchronization for heterogeneous delays inertial neural networks with generally uncertain semi-Markovian jumping and its application

引用

Neural Computing and Applications 2024年第33期36卷 20963-20982页

作者： Wang, Junyi He, Wenyuan Xu, Hongli Cai, Haibin Chen, Xiangyong Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Automation and Electrical Engineering Linyi University Linyi276000 China

This article is concerned with sampled-data synchronization problem of heterogeneous delays inertial neural networks (INNs) with generally uncertain semi-Markovian (GUSM) jumping. Different from traditional Markovian inertial neural networks (MINNs), the INNs with GUSM are investigated in this paper by fully considering the sojourn time and the lacking transition rates, which is more general and applicable for practical system. The new extended two-sided looped-functional (ETSLF) approach is adopted in this paper, and some improved less conservative criteria are derived to achieve the synchronization of the drive and response INNs. The controller gain matrices are acquired based on synchronization criteria. Finally, the viability of the method is presented through three examples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

A deep learning neural network-based visualization prediction model for carboplatin chemoresistance in ovarian cancer patients 16

A deep learning neural network-based visualization predictio...

引用

16th International Conference on Digital Image Processing, ICDIP 2024

作者： Xu, Yinghui Zhang, Haiyang Ling, Xiuwei Du, Yanze Yu, Zibo Tao, Junjun Chi, Jianning Wu, Qijun Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Department of Clinical Epidemiology Shengjing hospital of China Medical University Shenyang110122 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In this paper, we propose and construct a new model for predicting cisplatin resistance in ovarian cancer using a deep learning neural network based on multi-modal data fusion. This multimodal data consists of ovarian ultrasound images(US) and color Doppler flow images(CDF). Firstly, collect clinical multimodal data, train U-Net network, and segment regions of interest in the image. Subsequently, the trained U-Net model is used to segment the image and obtain regions of interest, namely the regions of interest in ultrasound images and blood flow images. Finally, the ultrasound images of the regions of interest are segmented and input into a diagnostic network for feature extraction. During the feature extraction process, the transformer module is used to interact and fuse the two modal data, and the features of these two modal data are comprehensively utilized to achieve visual prediction of cisplatin resistance in ovarian cancer. In the experiment, we compared this model with other network models and demonstrated that our proposed model effectively predicted drug resistance in ovarian cancer patients, maintaining approximately 81% accuracy. © 2024 SPIE.

关键词： Ultrasonic imaging

来源：评论

学校读者我要写书评

暂无评论

AGC: Automated Machine Learning for Garbage Classification on Mobile Devices

AGC: Automated Machine Learning for Garbage Classification o...

引用

2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022

作者： Liu, Changhao Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang110000 China

ISBN: (纸本)9781510655867

To protect the environment, governments introduced many policies about garbage recycling and garbage classification. However, the different garbage classification policies between different areas bring many inconveniences for people who come from other areas. In the meantime, the development of garbage classification application requires developers to have lots of experience about object classification. In this work, we employed Automated Machine Learning for the garbage classification application, which is called AGC method. The AGC method uses Google AutoML service to train the garbage classification model with Baidu AI studio garbage dataset. During train the model, we set the data augmentation and normalization to increase the precision of the model. To compare model effects, the model trained by AutoML was compared with MobileNet. At the end, the model was deployed on an iPhone to test the model. Our method achieves 0.92 in precision, which is much higher than MobileNet, and the application correctly recognized the real-word garbage image with 0.87 confidence. © 2022 SPIE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points

arXiv

引用

arXiv 2025年

作者： Li, Zhiheng Cui, Yubo Huang, Ningyuan Pang, Chenglin Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang China

Recently, 4D millimetre-wave radar exhibits more stable perception ability than LiDAR and camera under adverse conditions (e.g. rain and fog). However, low-quality radar points hinder its application, especially the odometry task that requires a dense and accurate matching. To fully explore the potential of 4D radar, we introduce a learning-based odometry framework, enabling robust ego-motion estimation from finite and uncertain geometry information. First, for sparse radar points, we propose a local completion to supplement missing structures and provide denser guideline for aligning two frames. Then, a context-aware association with a hierarchical structure flexibly matches points of different scales aided by feature similarity, and improves local matching consistency through correlation balancing. Finally, we present a window-based optimizer that uses historical priors to establish a coupling state estimation and correct errors of inter-frame matching. The superiority of our algorithm is confirmed on View-of-Delft dataset, achieving around a 50% performance improvement over previous approaches and delivering accuracy on par with LiDAR odometry. Our code will be available. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical radar

来源：评论

学校读者我要写书评

暂无评论

PC-Yolo: Enhanced YOLOv5-Based Defect Detection System with Improved Partial Convolution for Ham Sausage Inspection 4

PC-Yolo: Enhanced YOLOv5-Based Defect Detection System with ...

引用

4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Cai, Hexue Shangguan, Haonan Wang, Lulin Faculty of Robot Science and Engineering Northeastern University Liaoning Shenyang110167 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110167 China School of Metallurgy Northeastern University Liaoning Shenyang110819 China

ISBN: (纸本)9781510682528

Advances in automation for the packaging industry necessitate robust quality control measures, particularly in food appearance inspection. This paper introduces PC-Yolo, an advanced defect detection system based on an enhanced version of the YOLOv5 algorithm, tailored for the inspection of ham sausage products. PC-Yolo incorporates Partial Convolution (PConv) to improve the defect detection process, offering substantial gains in speed and accuracy over traditional methods. A custom dataset comprising high-resolution images of ham sausages, with and without defects, was used to train and validate the model. The proposed system efficiently handles variations in defect size through optimal prior box dimensionality and employs a combination of Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) for effective feature fusion. The resultant PC-Yolo model demonstrates superior real-time detection capabilities, with robust performance in complex scenarios, thereby addressing the industry's zero-defect challenge. © 2024 SPIE.

关键词： Inspection

来源：评论

学校读者我要写书评

暂无评论

Emotional EEG Prediction Based on Spatial-Temporal Graph Convolutional Neural Network 17

Emotional EEG Prediction Based on Spatial-Temporal Graph Con...

引用

17th International Convention on Rehabilitation engineering and Assistive Technology, i-CREATe 2024

作者： Bi, Jinying Wang, Fei Hu, Fangzhou Li, Hao Qu, Gangguo Han, Shuai Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110169 China Shengjing Hospital China Medical University Department of Neurosurgery Shenyang110004 China

ISBN: (纸本)9798350355154

Background and Objective: Emotions are an integral part of our daily lives and have a significant impact on brain neural activities. However, most traditional studies on electroencephalogram (EEG) emotions have focused on identification and classification, without effectively predicting future EEG based on existing emotional EEG signals. This paper introduces a spatial-temporal graph convolutional neural network (STGCNN) to integrate temporal and spatial features of EEG, capturing more emotional information. Methods: After calculating the brain functional network operating Pearson correlation, it is utilized as the input information of the model together with the frequency domain differential entropy (DE). Information prediction in the spatiotemporal domain is mainly achieved through two cross structures of two temporal convolutions and one spatial convolution. Meanwhile, an ablation experiment is designed to illustrate the practicality of the model. Results and Conclusion: The performance of the model is evaluated on the SEED dataset for mixed and single emotions across three sessions and three frequency bands by metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Weighted Mean Absolute Percentage Error (WMAPE). All performance metrics confirm the predictability of EEG-based emotions, demonstrating the effectiveness of STGCNN for the prediction of EEG. Significance: Accurately emotional EEG prediction is crucial for brain neural rehabilitation training and forecasting. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Skeleton-Based Action Recognition with Adaptive Connections and Motion Modeling

Skeleton-Based Action Recognition with Adaptive Connections ...

引用

International Conference on Industrial Artificial Intelligence (IAI)

作者： Qizhao Geng Chao Zheng Ziao Li Junyi Wang Faculty of Robot Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering and Foshan Graduate School of Innovation Northeastern University China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

In the field of skeleton-based action recognition, ST-GCN has gained popularity because of its strong capability to model graph data and its effective use of prior knowledge regarding human joint connections. However, it faces certain challenges and limitations, such as difficulties in transferring information between non-adjacent joints and dealing with subtle variations in action speed and acceleration. To address these issues, we present a Graph Convolution Network based Adaptive connections and Motion modeling (AM-GCN). Firstly, we remove the constraints of skeleton graphs by using learnable adjacency matrices to represent dynamic connections between joints, resulting in information exchange between non-adjacent joints. Additionally, we propose a joint motion modeling technique that combines position, velocity, and acceleration information to provide richer prior knowledge about human movements, further enhancing the model's performance. Extensive experiments conducted on the NTU-RGB+D dataset show that our method performs well in skeleton-based human behavior analysis tasks, demonstrating its potential and value in practical applications.

关键词： Adaptation models Adaptive systems Convolution Dynamics Skeleton Data models Behavioral sciences Artificial intelligence Information exchange Faces

来源：评论

学校读者我要写书评

暂无评论

Research on the Adaptive Control Method of Quadrotor Based on Neural Network 2

Research on the Adaptive Control Method of Quadrotor Based o...

引用

2nd International Conference on Smart City Challenges and Outcomes for Urban Transformation, SCOUT 2023

作者： Ji, Xiangzhongyu Zhang, Haiyan Zhang, Boyang Ru, Jingyu The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong999077 Hong Kong Northeastern University Faculty of Robot Science and Engineering Shenyang110167 China

ISBN: (纸本)9798350301373

At present, the more widely used and mature neural network is Back Propagation (BP) neural network, which is a typical feed-forward network in artificial neural network, especially suitable for dealing with system nonlinearity, uncertainty and the identification function of approximating system or structural characteristics. Neural network-based control methods are widely used in various intelligent control systems and have a very broad development prospect. The application of neural networks in aerospace vehicles can effectively control their stability, anti-interference and convergence speed. The system performance is better adapted to different scenarios and the control accuracy is continuously improved. Therefore, this paper proposes a neural network-based adaptive control method for quadcopter, and the design of neural network-based adaptive control system is carried out based on the elaboration of neural network related theories. After simulation experiments, it is confirmed that the design in this paper has good tracking performance and small tracking error. And the application effect is good. © 2023 IEEE.

关键词： Backpropagation

来源：评论

学校读者我要写书评

暂无评论

Online Identification Framework for AUV Based on PD-like Event-Triggered Mechanism and Data Filtering Window

Online Identification Framework for AUV Based on PD-like Eve...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Shutao Wang Yaomin Li Junyi Wang Genying Wang Chao Zheng Huixia Cui Faculty of Robot Science and Engineering Northeastern University Shenyang China Faculty of Robot Science and Engineering and Foshan Graduate School of Innovation Northeastern University China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

The development of the Autonomous Underwater Vehicle (AUV) has garnered significant attention. Establishing an accurate mathematical model is playing a crucial role in AUV control. In this paper, the least squares support vector machine (LS-SVM) algorithm is applied to identify the vertical model of AUV for long-term operation. Additionally, a PD-like (proportional and derivative-like) event-triggered condition is proposed to determine the appropriate time of model updates. In addition, an online identification framework for AUV models is established by adopting a filtering window structure. The stochastic excitation experiments are conducted on simulated data to demonstrate that the proposed framework is highly adaptable and capable of maintaining a balance between model accuracy and computation time during long-term AUV operation.

关键词： Support vector machines Adaptation models Autonomous underwater vehicles Accuracy Automation Filtering Computational modeling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：