检索结果-内蒙古大学图书馆

2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Wang, Binfei Zhang, Deyu Zhou, Yue Wang, Yue Wang, Xiaoqing Shenyang Ligong University School of Information Science and Engineering Shenyang China Anhui University of Technology School of Computer Science and Technology Maanshan China

ISBN: (纸本)9798350368888

Aiming at the problem where the deployment of multiple positioning systems based on Ultra-Wideband (UWB) technology is too close to each other, leading to mutual interference, we propose a self-organizing network algorithm based on the UWB positioning system. The algorithm requires that when a certain tag appears within the communication range of a base station, the main base station randomly generates a Personal Area Network ID and utilizes the network identifier to form a LAN for all modules within its communication range. Once the networking is completed, there will be no more communication between different systems. Experiments have shown that the localization system utilizing this method can effectively solve the interaction problem between different systems. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

来源：评论

学校读者我要写书评

暂无评论

Edge-aware Feature Aggregation Network for Polyp Segmentation

引用

Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

来源：评论

学校读者我要写书评

暂无评论

Reversed charge transfer induced by nickel in Fe-Ni/Mo_(2)C@nitrogen-doped carbon nanobox for promoted reversible oxygen electrocatalysis

引用

Journal of Energy Chemistry 2024年第1期88卷 202-212,I0005页

作者： Zhicheng Nie Lei Zhang Qiliang Zhu Zhifan Ke Yingtang Zhou Thomas Wågberg Guangzhi Hu School of Materials Science and Engineering Anhui University of Science and TechnologyHuainan 232001AnhuiChina Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental ScienceYunnan UniversityKunming 650504YunnanChina National Engineering Research Center for Marine Aquaculture Marine Science and Technology CollegeZhejiang Ocean UniversityZhoushan 316004ZhejiangChina Department of Physics UmeåUniversityUmeåS-90187Sweden

The interaction between metal and support is critical in oxygen catalysis as it governs the charge transfer between these two entities,influences the electronic structures of the supported metal,affects the adsorption energies of reaction intermediates,and ultimately impacts the catalytic *** this study,we discovered a unique charge transfer reversal phenomenon in a metal/carbon nanohybrid ***,electrons were transferred from the metal-based species to N-doped carbon,while the carbon support reciprocally donated electrons to the metal domain upon the introduction of *** led to the exceptional electrocatalytic performances of the resulting Ni-Fe/Mo_(2)C@nitrogen-doped carbon catalyst,with a half-wave potential of 0.91 V towards oxygen reduction reaction(ORR)and a low overpotential of 290 m V at 10 mA cm^(-2)towards oxygen evolution reaction(OER)under alkaline ***,the Fe-Ni/Mo_(2)C@carbon heterojunction catalyst demonstrated high specific capacity(794 mA h g_(Zn)~(-1))and excellent cycling stability(200 h)in a Zn-air *** calculations revealed that Mo_(2)C effectively inhibited charge transfer from Fe to the support,while secondary doping of Ni induced a charge transfer reversal,resulting in electron accumulation in the Fe-Ni alloy *** local electronic structure modulation significantly reduced energy barriers in the oxygen catalysis process,enhancing the catalytic efficiency of both ORR and ***,our findings underscore the potential of manipulating charge transfer reversal between the metal and support as a promising strategy for developing highly-active and durable bi-functional oxygen electrodes.

关键词： Metal-support interaction Charge transfer reversal Oxygen reduction reaction Oxygen evolution reaction Zinc-air battery

来源：评论

学校读者我要写书评

暂无评论

Design and Realization of Ultra-Wideband-based Indoor Personnel Positioning System 2

Design and Realization of Ultra-Wideband-based Indoor Person...

引用

2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Li, Boyu Wang, Yue Wang, Xiaoqing Shenyang Ligong University School of Information Science and Engineering Shenyang China Anhui University of Technology School of Computer Science and Technology Maanshan China

ISBN: (纸本)9798350368888

In order to meet the indoor positioning requirements of specific places such as factories, museums, libraries and shopping malls, a set of Ultra Wide Band (UWB) indoor personnel positioning system, which can be self-localized by the base station and self-networked by the system, has been designed and implemented. The system mainly includes the host computer, UWB base station module and tag module, real-time positioning tag coordinates through the Time of Arrival (TOA) positioning algorithm, real-time personnel tracking and positioning, but also can realize the historical trajectory of personnel traceability. This paper researches the base station self-localization algorithm and system self-organizing network algorithm on the basis of realizing the positioning function, realizes the rapid positioning of the base station coordinates, and solves the problem of avoiding the mutual interference of different systems. © 2024 IEEE.

关键词： Time of arrival

来源：评论

学校读者我要写书评

暂无评论

AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

引用

Chinese Journal of Electronics 2023年第1期32卷 1-12页

作者： WANG Zhibo LIU Kaixin HU Jiahui REN Ju GUO Hengchang YUAN Wei School of Cyber Science and Engineering Wuhan University School of Cyber Science and Technology Zhejiang University Department of Computer Science and Technology Tsinghua University

Collaborative inference(co-inference) accelerates deep neural network inference via extracting representations at the device and making predictions at the edge server, which however might disclose the sensitive information about private attributes of users(e.g.,race). Although many privacy-preserving mechanisms on co-inference have been proposed to eliminate privacy concerns, privacy leakage of sensitive attributes might still happen during inference. In this paper, we explore privacy leakage against the privacy-preserving co-inference by decoding the uploaded representations into a vulnerable form. We propose a novel attack framework named AttrL eaks, which consists of the shadow model of feature extractor(FE), the susceptibility reconstruction decoder,and the private attribute classifier. Based on our observation that values in inner layers of FE(internal representation) are more sensitive to attack, the shadow model is proposed to simulate the FE of the victim in the blackbox scenario and generates the internal ***, the susceptibility reconstruction decoder is designed to transform the uploaded representations of the victim into the vulnerable form, which enables the malicious classifier to easily predict the private attributes. Extensive experimental results demonstrate that AttrLeaks outperforms the state of the art in terms of attack success rate.

关键词： Deep learning Privacy Collaboration Transforms Feature extraction Prediction algorithms Iron

来源：评论

学校读者我要写书评

暂无评论

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

引用

computer Modeling in engineering & sciences 2023年第7期136卷 223-239页

作者： Tao Duan Yue Liu Jingze Li Zhichao Lian d Qianmu Li School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China School of Cyberspace Security Nanjing University of Science and TechnologyWuxi320200China

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence *** semantic segmentation can help the unmanned driving system by achieving road accessibility *** segmentation is also a challenging technology for image understanding and scene *** focused on the challenging task of real-time semantic segmentation in this *** this paper,we proposed a novel fast architecture for real-time semantic segmentation named *** from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial *** also proposed two kinds of SIF with cascaded and paralleled structures,*** SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature *** from previous stages usually contain rich low-level details but high-level semantics for later *** convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational *** FIF consists of a pooling layer and an upsampling operator followed by projection convolution *** concise component provides more spatial details for the *** with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

关键词： Real-time semantic segmentation convolutional neural network feature fusion unmanned driving fringe information flow

来源：评论

学校读者我要写书评

暂无评论

Toward actionable testing of deep learning models

引用

science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Auto-3D-house Design from Structured User Requirements

引用

Machine Intelligence Research 2025年第2期22卷 368-385页

作者： Minkui Tan Qi Chen Zixiong Huang Qi Wu Yuanqing Li Jiaqiu Zhou School of Software Engineering South China University of TechnologyGuangzhou 510006China Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide SA 5005Australia School of Automation Science and Engineering South China University of TechnologyGuangzhou 510006China

We study the task of automated house design,which aims to automatically generate 3D houses from user ***,in the automatic system,it is non-trivial due to the intrinsic complexity of house designing:1)the understanding of user requirements,where the users can hardly provide high-quality requirements without any professional knowledge;2)the design of house plan,which mainly focuses on how to capture the effective information from user *** address the above issues,we propose an automatic house design framework,called auto-3D-house design(A3HD).Unlike the previous works that consider the user requirements in an unstructured way(e.g.,natural language),we carefully design a structured list that divides the requirements into three parts(i.e.,layout,outline,and style),which focus on the attributes of rooms,the outline of the building,and the style of decoration,*** the processing of architects,we construct a bubble diagram(i.e.,graph)that covers the rooms′attributes and relations under the constraint of *** addition,we take each outline as a combination of points and orders,ensuring that it can represent the outlines with arbitrary ***,we propose a graph feature generation module(GFGM)to capture layout features from the bubble diagrams and an outline feature generation module(OFGM)for outline ***,we render 3D houses according to the given style requirements in a rule-based *** on two benchmark datasets(i.e.,RPLAN and T3HM)demonstrate the effectiveness of our A3HD in terms of both quantitative and qualitative evaluation metrics.

关键词： Automated house design user requirements understanding outline processing layout generation graph feature generation.

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Image Captioning: Integrating Attention Mechanisms with Adaptive Fusion Gates

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer science 2024年第3期51卷 212-221页

作者： Sheng, Shou-Jun Zhou, Zi-Wei School of Computer and Software Engineering University of Science and Technology Liaoning AnShan114051 China

In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image. However, a prevalent issue with current attention mechanisms is their tendency to overlook essential elements within the image, prioritizing contextual aspects of the object when generating descriptive text. This constraint results in a decrease in the precision of the textual descriptions produced. To address this issue and improve the accuracy of image interpretation, a proposed model for image description utilizes an attention-based approach and includes a multi-layer decoder and a fusion gate. This model is based on an encoder-decoder architecture and utilizes Residual Network (ResNet) framework for feature extraction during the encoding phase, thereby extending the encoder-decoder structure into the decoding phase. Within this framework, an adaptive fusion gate mechanism is introduced and combined with multi-layer cascade decoders to facilitate the generation of utterances. This allows decoders from lower layers to actively contribute to the final text prediction phase, thereby incrementally improving the accuracy of the predicted text and generating more precise descriptions. MS COCO 2014 dataset has been utilized for the purpose of training and validating the effectiveness of this model in understanding images. The results clearly and unequivocally establish the model’s capacity to generate exceptional predictions. When compared with the top-performing models, it has demonstrated significant enhancements, as indicated by a 0.096 rise in BLEU_1 metric, a 0.153 improvement in ROUGE_L metric, and a remarkable 0.32 increase in CIDEr metric on MS COCO dataset. The overall improvement in performance across all evaluation criteria highlights the model’s alignment with the requirements of image understanding applications. © (2024), (International Associ

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Study on the Combination of S-growth Curves Based on Ultra-Wideband Displacement Ranging for Predicting Landslides 2

Study on the Combination of S-growth Curves Based on Ultra-W...

引用

2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Wang, Xiaoshuang Zhang, Deyu Zhou, Yue Wang, Yue Wang, Xiaoqing Shenyang Ligong University School of Information Science and Engineering Shenyang China Anhui University of Technology School of Computer Science and Technology Maanshan China

ISBN: (纸本)9798350368888

In order to improve the prediction accuracy of landslide deformation prediction, a combination prediction method based on S-shaped growth curve is adopted to combine the Verhulst model and Pearl model, which are commonly used in landslide early warning and forecasting, with optimal weights, and the combination prediction model is applied to the UWB-based displacement ranging and deformation dataset for validation studies to realize the prediction of slope displacement changes. The results show that the Verhulst model is better than the Pearl model in the single prediction model, but the Pearl model is better in the fitting effect, while the combined prediction model based on the optimal weights can make full use of the advantages of the single prediction model, and can effectively improve the accuracy of the landslide deformation prediction. The combination prediction model based on optimal weights can fully utilize the advantages of a single prediction model and effectively improve the accuracy of landslide deformation prediction. The combination prediction model based on optimal weights can make full use of the advantages of a single prediction model and can effectively improve the accuracy of landslide deformation prediction. © 2024 IEEE.

关键词： Prediction models

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：