检索结果-内蒙古大学图书馆

International Conference on Measuring technology and Mechatronics Automation, ICMTMA

作者： Wentao Feng Kekang Song National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Sichuan Chengdu China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

The whale optimization algorithm (WOA) is an effective algorithm for solving complex optimization problem. Its unique search mechanism results in better exploitation than exploration capability which means that it's easy to get trapped in the local optima. A self-adaptive modified whale optimization algorithm named of SAMWOA is proposed. A self-adaptive quasi-opposition method and novel population updating mechanism is integrated into the whale optimization algorithm to improve the solution accuracy. Finally, the proposed algorithm is utilized in optimizing the radiation pattern of a 16-element linear antenna array. The experimental results show that SAMWOA algorithm can reduce the sidelobe level obviously.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection

arXiv

引用

arXiv 2022年

作者： Liu, Zhengyi Tan, Yacheng He, Qian Xiao, Yun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China

Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model, SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity ***://***/liuzywen/SwinNet © 2022, CC0.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Value Identification in Multistakeholder Recommender Systems for Humanities and Historical Research: The Case of the Digital Archive ***

arXiv

引用

arXiv 2024年

作者： Atzenhofer-Baumgartner, Florian Geiger, Bernhard C. Vogeler, Georg Kowald, Dominik Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria Department of Digital Humanities University of Graz Graz Austria Know Center Research GmbH Graz Austria Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria

Recommender systems remain underutilized in humanities and historical research, despite their potential to enhance the discovery of cultural records. This paper offers an initial value identification of the multiple stakeholders that might be impacted by recommendations in ***, a digital archive for historical legal documents. Specifically, we discuss the diverse values and objectives of its stakeholders, such as editors, aggregators, platform owners, researchers, publishers, and funding agencies. These in-depth insights into the potentially conflicting values of stakeholder groups allow designing and adapting recommender systems to enhance their usefulness for humanities and historical research. Additionally, our findings will support deeper engagement with additional stakeholders to refine value models and evaluation metrics for recommender systems in the given domains. Our conclusions are embedded in and applicable to other digital archives and a broader cultural heritage context. © 2024, CC BY-SA.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Unknown Type Streaming Feature Selection via Maximal Information Coefficient

Unknown Type Streaming Feature Selection via Maximal Informa...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Peng Zhou Yunyun Zhang Yuanting Yan Shu Zhao Key Laboratory of Intelligent Computing and Signal Processing (the Ministry of Education of China) School of Computer Science and Technology Anhui University Hefei China

Feature selection aims to select an optimal minimal feature subset from the original datasets and has become an indispensable preprocessing component before data mining and machine learning, especially in the era of big data. Most feature selection methods implicitly assume that we can know the feature type (categorical, numerical, or mixed) before learning, then design corresponding measurements to calculate the correlation between features. However, in practical applications, features may be generated dynamically and arrive one by one over time, which we call streaming features. Most existing streaming feature selection methods assume that all dynamically generated features are the same type or assume we can know the feature type for each new arriving feature on the fly, but this is unreasonable and unrealistic. Therefore, this paper firstly studies a practical issue of Unknown Type Streaming Feature Selection and proposes a new method to handle it, named UT-SFS. Extensive experimental results indicate the effectiveness of our new method. UT-SFS is nonparametric and does not need to know the feature type before learning, which aligns with practical application needs.

关键词： Measurement Microwave integrated circuits Correlation Conferences Machine learning Big Data Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Application of complex deep neural networks in complex data processing and comparative analysis of activation functions

Application of complex deep neural networks in complex data ...

引用

IET International Radar Conference (IRC 2023)

作者： Junxiang Yang Ying Zhou Hui Ma Kun Lv Hongwei Liu Lin Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China Beijing Institute of Remote-sensing Equipment Science and Technology on Millimeter-wave Laboratory Beijing People's Republic of China

ISBN: (数字)9781837240982

The application of deep learning to fast radar data processing has important significance. The effectiveness of complex deep neural networks (CDNNs) has recently been demonstrated, and complex networks are important for processing complex data because they have the ability to handle phases. And the complex-valued activation function introduces a nonlinear transformation to the network, which has a great potential to improve the network performance. In this paper, we test the feasibility of CDNNs and the effect of different activation functions in the framework of complex networks under the goal of retaining more phase information. In particular, we propose a new complex-valued activation RℂReLU based on RℂeLu and test its performance in a classification and auto-encoder context. Finally, we perform radar imaging reconstruction by different activation functions in complex networks, and the results show that RℂReLU is more suitable for processing radar data in CDNNs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Speech language classification algorithm based on multiple-instance learning with negative sample cleaning 9

Speech language classification algorithm based on multiple-i...

引用

9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Jianyi Xu, Wei Yao, Jiaqi Yan, Jikun Huang, Xin PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance. © 2020 IEEE.

关键词： Speech

来源：评论

学校读者我要写书评

暂无评论

Multi-TransPathoNet: A transfer learning-based neural network for pathology image classification with multi-Task learning 3

Multi-TransPathoNet: A transfer learning-based neural networ...

引用

3rd International Conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Deng, Xin Li, Min Ren, Xiaojun Zheng, Xiuyan Liang, Fei He, Liang Zhu, Min School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information China Xinjiang Clinical Research Center for Precision Medicine of Digestive System Tumor China Department of Pathology Karamay Central Hospital China Xinjiang Key Laboratory of Signal Detection and Processing China Dept. of Electron. Eng. and Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781510668522

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-Task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-Task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-Task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Aiding AUV Localization Using a Dual-Sided Synthetic Aperture Sonar

Aiding AUV Localization Using a Dual-Sided Synthetic Apertur...

引用

2021 CIE International Conference on Radar, Radar 2021

作者： Chen, Shiping Chi, Cheng Zhang, Pengfei Wang, Peng Li, Yu Liu, Jiyuan Huang, Haining Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Beijing China

ISBN: (纸本)9781665498142

Autonomous underwater vehicle (AUV) navigation and localization is crucial and challenging in Global Positioning System-denied environments. AUV carrying synthetic aperture sonars (SASs) can provide precise motion estimation of the platforms. This study focuses on enhancing the performance of AUV localization using dual-sided SASs. The two-dimensional velocities of AUV are estimated using dual-sided SAS echoes. An extended Kalman filter (EKF) is then adopted for dead reckoning using the estimated velocities. The experimental results demonstrate that the proposed method has better localization accuracy than that of the Doppler velocity log (DVL). © 2021 IEEE.

关键词： autonomous underwater vehicles localization synthetic aperture sonar time delay estimation

来源：评论

学校读者我要写书评

暂无评论

Base Station Selection Method for UWB Indoor Positioning Based on TDOA Fingerprint 5

Base Station Selection Method for UWB Indoor Positioning Bas...

引用

5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Wang, Mei Yang, Fan Zhou, Zou Qiu, Hongbing Guilin University of Electronic Technology Ministry of Education Key Laboratory of Cognitive Radio and Information Processing Guilin541004 China College of Information Science and Engineering Guilin University of Technology Guilin541007 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781665415996

ISBN: (纸本)9781665415989

The ultrawideband (UWB) indoor positioning system has poor positioning accuracy and stability in the indoor environment with complex structure, which is affected by non-line-of-sigh (NLOS), multi-path effect, physical measurement and other factors. Therefore, this paper proposes a UWB base station (BS) selection positioning method based on time difference of arrival (TDOA) fingerprint. Firstly, TDOA information and BS selection information of the reference point are collected in the off-line phase. Secondly, in the on-line phase, the improved time reversal resonating strength (TRRS) algorithm is used to match the fingerprint database, and the BS selection information recorded by the fingerprint points with the highest matching degree is used for positioning solution. Finally, according to the experimental data, in the 30m*5.5m*3.1m NLOS exhibition hall, the average three-dimensional error is 10.93cm, and the average variance is 10.27cm2. The experimental results show that the proposed method has high reliability and stability. © 2021 IEEE.

关键词： Time difference of arrival

来源：评论

学校读者我要写书评

暂无评论

Effects of colored noise on the dynamics of quantum entanglement of a one-parameter qubit-qutrit system

引用

Chinese Physics B 2022年第5期31卷 141-149页

作者： Odette Melachio Tiokang Fridolin Nya Tchangnwa Jaures Diffo Tchinda Arthur Tsamouo Tsokeng Martin Tchoffo Research Unit of Condensed Matter Electronic and Signal ProcessingDepartment of PhysicsDschang School of Sciences and TechnologyUniversity of DschangPO Box:67 DschangCameroon Material Science Research Group Physics LaboratoryDepartment of PhysicsPost Graduate SchoolUniversity of MarouaPO Box:814 MarouaCameroon Centre d’Etudes et de Recherches en Agronomie et en Biodiversite Faculte d’Agronomie et des Sciences AgricolesUniversite de DschangCameroun

We analyzed the effect of colored noise on the negativity dynamics of a hybrid system consisting of a qubit-qutrit and not interacting,prepared from the start in an entangled one-parameter state and acting with noise in local and non-local *** this pink and brown noise we investigated two different situations:in the first situation,the noise is produced by a bistable oscillator with an unknown exchange rate;however,in the second situation,the noise is generated by a set of bistable *** found that entanglement decreases with time to zero,and undergoes the phenomenon of sudden death and *** pink noise is more prone to entanglement than the brown noise and the non-local environment is more prone to entanglement than the local *** the number of fluctuators is increased,entanglement decays faster and finally,for certain parameters of the initial state,the subsystems are not affected by the noise.

关键词： entanglement hybrid system qutrit colored noise

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：