检索结果-内蒙古大学图书馆

5th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Peng, Yan Qadir, Abdul Li, Yingsong Khan, Khushal College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China College of Intelligent System and Control Science Engineering Harbin Engineering University China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Andrew's Sine Estimator (ASE) has recently been used to invent adaptive filtering, which can combat more kind of noises than conventional estimators. Inspired by the LMS and its sparse forms, normalization and proportionate ASE algorithms with Lawson constraints are constructed with the respective given names of Lawson normalization ASE (LNASE) and Lawson proportionate NASE (LPNASE). Simulations are performed to analyze the performance of these algorithms under impulsive noise. © 2022 IEEE.

关键词： Impulse noise

来源：评论

学校读者我要写书评

暂无评论

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bound States in the Continuum

Enhanced Second-Harmonic Generation Driven by Dual-Quasi Bou...

引用

2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Yuhang Ren, Xingang Niu, Kaikun Huang, Zhixiang School of Electronic and Information Engineer Anhui University Hefei230601 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509657

Bound state in the continuum (BIC) is a dark mode with infinitely high quality factors(Q-factors), it has recently attracted considerable research attention in photonics. Here, we present a metasurface that can support two BICs, which consists of AlGaAs. By breaking the structural symmetry, the two BICs are transformed into quasi-bic modes because they are not protected by the symmetry. Furthermore, it is found that the two quasi-BICs resonate near the fundamental and the second harmonic respectively, which can be used to enhance efficiency of second harmonic generation. By exploiting the high Q characteristics of this structure at both frequencies, we can obtain a second harmonic generation efficiency of nearly 10-3 with 1GW/cm2of pump intensity. Our proposed metasurface will offer good prospects for nonlinear operation. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Harmonic generation

来源：评论

学校读者我要写书评

暂无评论

Optimized Gradient Clipping for Noisy Label Learning

arXiv

引用

arXiv 2024年

作者： Ye, Xichen Wu, Yifan Zhang, Weizhong Li, Xiaoqiang Chen, Yifan Jin, Cheng Shanghai University China Fudan University China Hong Kong Baptist University Hong Kong Shanghai Key Laboratory of Intelligent Information Processing China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

Previous research has shown that constraining the gradient of loss function w.r.t. model-predicted probabilities can enhance the model robustness against noisy labels. These methods typically specify a fixed optimal threshold for gradient clipping through validation data to obtain the desired robustness against noise. However, this common practice overlooks the dynamic distribution of gradients from both clean and noisy-labeled samples at different stages of training, significantly limiting the model capability to adapt to the variable nature of gradients throughout the training process. To address this issue, we propose a simple yet effective approach called Optimized Gradient Clipping (OGC), which dynamically adjusts the clipping threshold based on the ratio of noise gradients to clean gradients after clipping, estimated by modeling the distributions of clean and noisy samples. This approach allows us to modify the clipping threshold at each training step, effectively controlling the influence of noise gradients. Additionally, we provide statistical analysis to certify the noise-tolerance ability of OGC. Our extensive experiments across various types of label noise, including symmetric, asymmetric, instance-dependent, and real-world noise, demonstrate the effectiveness of our *** Codes 68T07, 68T10 © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Relational-Branchformer：一种新的视听语音识别框架

SSRN

引用

SSRN 2023年

作者： Xiao, Yewei Liu, Xuanming Zhu, Aosu Huang, Jian School of Information Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Intelligent Computing &Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This study adopts the state-of-the-art Branchformer series architecture in the field of automatic speech recognition (ASR) as a replacement for the previously popular Conformer architecture, providing a novel solution for audio-visual speech recognition tasks. Building upon this foundation, we improve the Branchformer architecture and propose the R-Branchformer (Relational-Branchformer) architecture. The introduction of the CARM (Convolutional Attention Relation Module)enhances the connectivity between the local and global branches by considering their relationships and interactions, facilitating mutual embedding of local and global contextual information, ultimately boosting model performance. Our model is based on the Connectionist Temporal Classification (CTC) loss, and we incorporate intermediate CTC losses between blocks. Moreover, by referencing and improving the GIC (Gated Interlayer Collaboration) module to replace the Inter CTC module, we further relax the conditional independence assumption of the CTC model, resulting in enhanced model performance. Furthermore, we propose the Audio-Visual Output Enhancement (AVOE) module, which effectively assimilates information from both audio and visual modalities to enrich the representation of audio-visual information. As a result, our R-Branchformer model achieves remarkable Word Error Rates (WERs) of 1.7% and 1.5% on the LRS2 and LRS3 test sets, respectively, showcasing its state-of-the-art performance in audio-visual speech recognition tasks. © 2023, The Authors. All rights reserved.

关键词： Architecture

来源：评论

学校读者我要写书评

暂无评论

Motion-Centric Retrieval of 3d Human Skeleton and 2d Human Image Sequences

SSRN

引用

SSRN 2024年

作者： Wang, Ying Zhang, Lu Peng, Jingliang Lv, Na Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China School of Information Science and Engineering University of Jinan Jinan250022 China

In this work, we propose a novel unified framework for cross-modal retrieval of 3D human skeletons and 2D human image sequences (videos), driven by deep motion analysis. To the best of our knowledge, this is the first published framework of its kind. Specifically, we design a novel neural network (NN) structure consisting of multiple parallel branches, each extracting feature vectors for one modality, and multiple logistic regression units, each predicting the match score between feature vectors of two modalities. Each branch is constructed on the model of Bi-directional Encoder Representations of Transformers (BERT). During retrieval, inter-modality matching is based on logistic regression over the feature vectors. We experimentally demonstrate that our proposed framework comprehensively supports both single-modal classification and cross-modal retrieval, and achieves state-of-the-art performance in both aspects. © 2024, The Authors. All rights reserved.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

引用

Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

引用

Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Excellent Student Training in Shielding Effectiveness Analysis with International Cooperation 10

Excellent Student Training in Shielding Effectiveness Analys...

引用

10th IEEE International Conference on Computer Science and network Technology, ICCSNT 2022

作者： Li, Yingsong Huang, Zhixiang Wang, Meiyu Jiang, Tao Sinkevich, Eugene Mordachev, Vladimir College of Information and Communication Engineering Harbin Engineering University Harbin15001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Belarusian State University of Informatics and Radioelectronics Emc RandD Laboratory Minsk Belarus

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

We present the excellent student training scheme in shielding effectiveness analysis with international cooperation of Belarus. As we know, the shielding is vital for modern information devices due to the radiating around us. Herein, the shielding effectiveness is important for training excellent students to master the design of the frequency selective components. In this method, we teach the students to use the EM simulation tools to construct different EM structures for obtaining the shielding, and the performance of these structures are verified from the simulations and measurements. Using this shielding effectiveness training scheme, a frequency selective surface is presented, simulated and discussed to analyze its performance. In future, the excellent student training methods given herein for shielding effectiveness can be expanded to develop other EM structures. © 2022 IEEE.

关键词： International cooperation

来源：评论

学校读者我要写书评

暂无评论

An Overlapping Local Community Detection Algorithm Based on Node Transitivity and Modularity Density 15th

An Overlapping Local Community Detection Algorithm Based on ...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Huang, Xintong Wu, Ling Guo, Kun College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

ISBN: (纸本)9789811625398

The local community detection (LCD) method can discover the local community structure in which the seed node is located. Compared with global community detection, local community detection is characterized by its low cost and high efficiency. However, most existing LCD methods only return a non-overlapping community. Individuals in the real world may participate in multiple communities, which can only be discovered by using overlapping local community detection methods. In this study, an overlapping local community detection algorithm based on modularity and node transitivity. First, the scope and structure information of the overlapping communities are obtained according to the node transitivity. Second, NMF is used to obtain the number of overlapping communities. Finally, the local modularity density based on edge weights is used to refine the detected local communities. The experimental results validate the high performance of our method to the other method in comparison. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

The Design and Implementation of KMeans Based on Unified Batch and Streaming processing 15th

The Design and Implementation of KMeans Based on Unified Bat...

引用

15th CCF Conference on Computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Chen, Hao Guo, Kun Chen, Yuzhong Guo, Hong College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

Clustering algorithms aim at gathering similar data points from a dataset in an unsupervised manner. Although the batch clustering algorithms have relatively high accuracy, they cannot make use of the dynamic clustering results efficiently. The requirement of using the whole dataset in calculation results in the problems of resource waste and high time cost. On the contrary, incremental clustering only needs to update the varied part of a model upon the arrival of new data, which makes it unnecessary to recluster the whole data all the time. The feature is very suitable for the streaming data process, but it decreases the accuracy of the algorithms and cannot satisfy the low latency requirement of real-time data processing. In response to this problem, the paper proposes a novel unified batch and streaming clustering model (UBSCM) based on streaming computation, which includes a streaming cluster feature updating mechanism (SCFUM). The Flink framework is used to implement a new streaming KMeans algorithm based on UBSCM (KMeansUBSP). The experiments on the real-world datasets validate that the new streaming KMeans algorithm is effective in clustering the batch and streaming data in a unified manner. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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：