检索结果-内蒙古大学图书馆

第40届中国控制会议

作者： Yuan Xie Kan Xie Shengli Xie School of Automation Guangdong University of Technology and Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong Key Laboratory of IoT Information Processing and Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education and 111 Center for Intelligent Batch Manufacturing Based on IoT Technology

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then,using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards,the unmixng matrix is updated using Newton’s method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline ***, it has better superiority in the strong reverberant environment.

关键词： Blind source separation（BSS） Convolutive mixtures Heart sound Independent vector analysis

来源：评论

学校读者我要写书评

暂无评论

Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

引用

IEEE Transactions on intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Solving clustering problem by hybrid ant colony algorithm

引用

Journal of information and Computational Science 2009年第4期6卷 1839-1846页

作者： Gao, Shang Zhang, Zaiyue Zhang, Xiaoru Cao, Cungen School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Information Processing Chinese Acad. of Sci. Beijing 100080 China

An optimization model of clustering problem is given in this paper. The advantages and shortages of K-Means algorithm, simulated annealing algorithm and basic ant colony algorithm are analyzed. The ant colony algorithm is then extended to use K-means clustering to seed the initial solution and the information pheromone is adjusted according to them. The hybrid ant colony algorithm is proved effective than others through examples. © 2009 Binary information Press.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

A generalization theory based on independent and task-identically distributed assumption

arXiv

引用

arXiv 2019年

作者： Zheng, Guanhua Sang, Jitao Li, Houqiang Yu, Jian Xu, Changsheng University of Science and Technology of China School of Computer and Information Technology Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Hefei230026 China National Lab of Pattern Recognition Institute of Automation CAS Beijing100190 China University of Chinese Academy of Sciences

—Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumption, makes these theories fail to interpret many generalization phenomena or guide practical learning tasks. In this paper, we propose a new Independent and Task-Identically Distributed (ITID) assumption, to consider the task properties into the data generating process. The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance. Based on the new bound, we introduce a practical invariance enhancement algorithm from the perspective of modifying data distributions. Finally, we verify the algorithm and theorems in the context of image classification task on both toy and real-world datasets. The experimental results demonstrate the reasonableness of the ITID assumption and the effectiveness of new generalization theory in improving practical generalization performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Nonnegative Edge Consensus of Networked Linear Systems

Nonnegative Edge Consensus of Networked Linear Systems

引用

第35届中国控制会议

作者： Xiaoling Wang Housheng Su Xiaofan Wang Guanrong Chen Department of Automation Shanghai Jiaotong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of ChinaHuazhong University of Science and Technology Department of Electronic Engineering City University of Hong Kong

ISBN: (纸本)9781509009107

In this paper,nonnegative edge consensus of systems steered by general linear dynamics on directed network is *** the new approach,one first endows dynamics to each directed edge and then designs a distributed edge consensus protocol guiding all edges to reach a common *** the help of line graph theory,it is proved that strongly connected network can ensure reaching nonnegative edge consensus,if the initial conditions of all edges are *** simulations are provided to verify the theoretical results.

关键词： Networked system Nonnegative edge consensus Distributed algorithm Line graph

来源：评论

学校读者我要写书评

暂无评论

Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology Images 48

Multi-Object Localization and Irrelevant-Semantic Separation...

引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Tang, Ya Ye, Xiongjun Li, Xuanya Chen, Zhineng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan411105 China Chinese Academy of Medical Sciences Peking Union Medical College National Cancer Center Cancer Hospital Beijing100021 China Baidu Inc. Beijing100085 China Fudan University School of Computer Science Shanghai200438 China

ISBN: (纸本)9781728163277

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effective nuclei segmentation method for histopathology images based on a novel neural network for multi-object localization and irrelevant-semantic separation (MI-Net), which includes a multi-object localization module (MOLM), a deep boundary awareness module (DBAM), and an irrelevant semantic separation module (ISSM). Specifically, the MOLM is used to calibrate features to capture more comprehensive information about the location of nuclei. It alleviates the problem of cell adhesion in histopathology images. The DBAM is intended to extract boundary information and solve the blurred boundary problem effectively. The ISSM is used to separate foreground features from background features and effectively address the problem of complex backgrounds in histopathology images. It also solves the low contrast problem between the target objects and the background. We evaluate MI-Net on the famous MoNuSeg dataset and compare it with ten state-of-the-art methods. MI-Net surpasses the best-performed method by margins from 1.12% to 2.29% over standard evaluation metrics, showing its effectiveness on nuclei segmentation. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An improved group theory-based optimization algorithm for discounted 0-1 knapsack problem

引用

Advances in Computational Intelligence 2021年第5期1卷 1-11页

作者： Wang, Ran Zhang, Zichao Ng, Wing W. Y. Wu, Wenhui College of Mathematics and Statistics Shenzhen University Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China

Discounted 0-1 knapsack problem (D0-1KP) has been proved to be NP-hard, thus a lot of researches focus on designing non-deterministic algorithms to solve it. Group theory-based optimization algorithm (GTOA), as a recently proposed evolutionary algorithm (EA), can provide satisfactory results to D0-1KP. GTOA introduces important theories of algebra, i.e., group theory, to describe combinatorial optimization problems, and applies the classic operations in group theory to design operators for EA. In order to generate a better solution according to a set of existing solutions during each evolutionary iteration, an important operator called random linear combination operator (RLCO) is designed. However, the practical meaning of applying the operations in group theory is hard to explain, and the proposed RLCO is lack of interpretability, causing difficulties in analyzing and improving the algorithm. In this paper, to improve the interpretability and further enhance the performance, we propose a new operator named random xor operator (RXO), and interpret it from the view point of bitwise operation. By replacing RLCO with RXO, a new GTOA algorithm is realized for D0-1KP. Experimental results demonstrate that it can provide very competitive performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Lightweight Small Target Detection Algorithm Based on YOLOv5 2

Lightweight Small Target Detection Algorithm Based on YOLOv5

引用

2nd International Conference on Applied Statistics, Computational Mathematics, and Software Engineering, ASCMSE 2023

作者： Song, Nan Wang, Ying Xin, Zhiyi Xin, Huili Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTGYOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further lightweighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhenced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy. © 2023 SPIE.

关键词： Signal detection

来源：评论

学校读者我要写书评

暂无评论

Small object detection algorithm for UAV images based on YOLOv5

Small object detection algorithm for UAV images based on YOL...

引用

2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Song, Nan Wang, Ying Liu, Tianxu Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475004 China Software Engineering Intelligent Information Processing Innovation Base-Subject Innovation Base of Henan Higher Universities Henan University Kaifeng475004 China Henan Provincial Communications and Transportation Dispatch and Command Center Zhengzhou450000 China

ISBN: (纸本)9781510681514

In the field of deep learning-based object detection algorithms, the YOLO algorithm has attracted a lot of attention due to its advantages in speed and accuracy. However, there are problems such as low detection speed and poor detection accuracy in the application of UAV images. For this reason, an improved small object detection algorithm MTF-YOLOv5 based on the YOLOv5 algorithm is proposed. Firstly, MobileNet V3 is used to replace the main network to reduce the amount of network parameters and improve the running speed. Secondly, a small object detection layer is added to the original network structure to enhance the detection ability for small objects. Finally, the FocalModulation is introduced to replace the SPP module to improve accuracy. The improved algorithm is tested on the processed TinyPerson dataset. Experimental results show that the mean average precision (mAP) on the YOLOv5s algorithm improved by 4.1%, and the FPS increased from 86 to 136, improving the speed by 51%. While maintaining detection speed, the updated algorithm successfully raises detection accuracy. © 2024 SPIE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Standing on the Shoulders of Giants: Reprogramming Visual-Language Model for General Deepfake Detection

arXiv

引用

arXiv 2024年

作者： Lin, Kaiqing Lin, Yuzhen Li, Weixiang Yao, Taiping Li, Bin Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security SZU-AFS Joint Innovation Center for AI Technology Shenzhen University Shenzhen518060 China Tencent Youtu Lab China

The proliferation of deepfake faces poses huge potential negative impacts on our daily lives. Despite substantial advancements in deepfake detection over these years, the generalizability of existing methods against forgeries from unseen datasets or created by emerging generative models remains constrained. In this paper, inspired by the zero-shot advantages of Vision-Language Models (VLMs), we propose a novel approach that repurposes a well-trained VLM for general deepfake detection. Motivated by the model reprogramming paradigm that manipulates the model prediction via input perturbations, our method can reprogram a pre-trained VLM model (e.g., CLIP) solely based on manipulating its input without tuning the inner parameters. First, learnable visual perturbations are used to refine feature extraction for deepfake detection. Then, we exploit information of face embedding to create sample-level adaptative text prompts, improving the performance. Extensive experiments on several popular benchmark datasets demonstrate that (1) the cross-dataset and cross-manipulation performances of deepfake detection can be significantly and consistently improved (e.g., over 88% AUC in cross-dataset setting from FF++ to WildDeepfake);(2) the superior performances are achieved with fewer trainable parameters, making it a promising approach for real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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

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

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：