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检索条件"任意字段=International Conference on Signal and Data Processing, ICSDP 2019"

共 2690 条记录，以下是71-80 订阅

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POWERLINK Communication Fault Detection Method Based on data Frames Sequence Analysis

POWERLINK Communication Fault Detection Method Based on Data...

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2022 international conference on signal processing and Communication Technology, SPCT 2022

作者： Wang, Jian Xie, Chuang Dong, Ce Key Laboratory of Networked Control System ShenYang China Shenyang institute of automation Chinese academy of sciences ShenYang China Institutes for Robotics and Intelligent Manufacturing ChineseAcademy of Sciences ShenYang China

ISBN: (纸本)9781510664333

Fault diagnosis, location and maintenance of industrial networks are extremely important components of network management, which can effectively improve network availability, shorten network failure time, help improve network performance, satisfy network service quality and ensure network security. This paper proposes a communication fault detection method based on the analysis of communication protocol message flow for POWERLINK, an industrial Ethernet protocol widely used in the field of industrial communication at present. By monitoring network status and building fault judgment logic, it can analyze the rationality of message sequence and protocol consistency in the message flow so as to effectively detect the dynamic upper layer protocol fault in the network and determine the location of the fault. In order to verify the effectiveness of this method, we designed a POWERLINK network communication fault diagnosis system, which can correctly identify and analyze the wrong data frame sequence in the data message flow and finally achieve the diagnosis and location of POWERLINK network communication fault. © 2023 SPIE.

关键词： Fault detection

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The grab tracking method for 3D reconstruction of a waste pool 3

The grab tracking method for 3D reconstruction of a waste po...

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Ding, Xiaosong Feng, Chong Li, Liqiang Chen, Xi International Business School Beijing Foreign Studies University Beijing100089 China

ISBN: (纸本)9781510668522

With 3D reconstruction method, this paper conducts experiments based on the data of a waste incineration plant in eastern China, and analyzes and compares the visual 3D monitoring, laser sensor 3D monitoring, and grabbing bucket tracking. By comparison, the grab tracking method is very appropriate for 3D reconstruction in terms of cost, accuracy, and implementation. With the proposed method, the volume monitoring not only provides practical suggestions for the construction of the optimal waste dump volume monitoring decision-making system, but also helps waste incineration enterprises achieve the goal of ''carbon peaking and carbon neutrality''. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer vision

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Nonnegative Block-Term Decomposition with the β-Divergence: Joint data Fusion and Blind Spectral Unmixing 48

Nonnegative Block-Term Decomposition with the β-Divergence:...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Prévost, C. Leplat, V. University of Lille Cnrs Centrale Lille Umr 9189 CRIStAL LilleF-59000 France Moscow Russia

ISBN: (纸本)9781728163277

We present a new method for solving simultaneously hyperspectral super-resolution and spectral unmixing of the unknown super-resolution image. Our method relies on three key elements: (1) the nonnegative decomposition in rank-(Lr, Lr, 1) block-terms, (2) joint tensor factorization with multiplicative updates, and (3) the formulation of a family of optimization problems with β-divergences objective functions. We come up with a family of simple, robust and efficient algorithms, adaptable to various noise statistics. Experiments show that our approach competes favorably with state-of-the-art methods for solving both problems at hand for various noise statistics. © 2023 IEEE.

关键词： blind spectral unmixing blockterm decomposition hyperspectral super-resolution Nonnegative tensor factorization β-divergence

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Intelligent Perception Systems for Multi-Modal data processing in Industrial Application Contexts 36

Intelligent Perception Systems for Multi-Modal Data Processi...

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Doctoral Consortium Papers Presented at the 36th international conference on Advanced Information Systems Engineering, CAiSE-DC 2024

作者： Bono, Annaclaudia Via E. Orabona 4 Bari Italy Via Amendola 122 D/O Bari Italy

Intelligent perception systems represent critical enabling technologies to bring innovation in any physical environment and improve the quality of life tending toward what is known as an intelligent future. This research explores the use of advanced monitoring perception systems with intelligent capabilities in industrial contexts. By combining networks of sensors that generate multimodal data, signal and image processing, and artificial intelligence techniques, the produced know-how can be transferred to industrial application fields for continuously monitoring goods and people and promoting the transition to a sustainable, human-centred, and resilient industry. A significant challenge facing monitoring systems is their inherent complexity, often resulting in usability issues. This study seeks to address this challenge by developing a new methodological framework to make these systems easily accessible and user-friendly for individuals regardless of their level of technical skills. The sector of interest is that of Precision Agriculture (PA), in detail Precision Viticulture (PV), where intelligent systems can make agricultural production more efficient and ensure sustainable practices are adopted to increase food production and meet growing global demand while maintaining high-quality standards. This paper provides an overview of the PhD research proposal considering the main open problems and the main steps that must be considered. © 2024 Copyright for this paper by its authors.

关键词： Artificial Intelligence Image processing Intelligent perception systems Multimodal data Sustainability

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Mongolian-Chinese Machine Translation Based on Text Context Information 8

Mongolian-Chinese Machine Translation Based on Text Context ...

引用

8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Zhang, Junjin Tian, Yonghong Song, Zheyu Hao, Yufeng Inner Mongolia University of Technology College of Data Science and Application Hohhot China

ISBN: (纸本)9798350302455

Mongolian-Chinese neural machine translation has the problem that it cannot make full use of context information for document-level translation. In order to solve this problem, a Mongolian-Chinese neural machine translation model using the context information of the passage is *** model makes better use of context information for document-level translation by introducing local encoders and global encoders in the encoder and caching mechanisms in the decoder. Through experiments, the document-level Mongolian-Chinese machine translation model integrated with context information is compared with the sentence-level Mongolian-Chinese machine translation model based on Transformer. The experimental results verify the advantages of the document-level Mongolian-Chinese machine translation model integrated with context information in translation performance. © 2023 IEEE.

关键词： Neural machine translation

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Mixup-Inf-Net: A data augmentation algorithm for segmentation of new coronary pneumonia infections

Mixup-Inf-Net: A data augmentation algorithm for segmentatio...

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2022 international conference on signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zeng, Kai Pei, Xuan Chen, Peizhi Xiamen University of Technology Xiamen China

ISBN: (纸本)9781510664616

In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.

关键词： COVID-19

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Application of Consistent Hash Based on Virtual Node in Traffic Big data 7

Application of Consistent Hash Based on Virtual Node in Traf...

引用

7th international conference on Intelligent Computing and signal processing, ICSP 2022

作者： Wang, Miao Sun, Quanling Anhui Jianzhu University School of Electronic and Information Engineering Anhui Hefei China

ISBN: (纸本)9781665478571

With the development of internet and artificial intelligence, the data volume of all industries has increased exponentially. How to store these massive data has become the core problem at this stage. Taking traffic big data as an example, this paper introduces the framework of distributed storage and consistent hashing technology. At the same time, an improved consistent hashing algorithm based on virtual node is proposed. It solves the hot spot problem in network storage and the problem of data skew caused by uneven node distribution. Experimental results show that this adaptive consistent hashing algorithm based on virtual node can provide better node allocation efficiency and better adaptability. © 2022 IEEE.

关键词： Big data

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PYRAMID TRANSFORMER DRIVEN MULTIBRANCH FUSION FOR POLYP SEGMENTATION IN COLONOSCOPIC VIDEO IMAGES 30

PYRAMID TRANSFORMER DRIVEN MULTIBRANCH FUSION FOR POLYP SEGM...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Wang, Ao Wu, Ming Qi, Hao Shi, Hong Chen, Jianhua Chen, Yinran Luo, Xiongbiao Xiamen Univ Dept Comp Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen 361102 Peoples R China Fujian Med Univ Fujian Canc Hosp Canc Hosp Fuzhou 350014 Peoples R China

ISBN: (纸本)9781728198354

Colonoscopic polyp segmentation is essential and valuable to early diagnosis and treatment of colorectal cancer. It remains challenging to accurately extract these polyps due to their small sizes, irregular shapes, image artifacts, and illumination variations. This work proposes a new encoder-decoder architecture called pyramid transformer driven multibranch fusion to precisely segment different types of colorectal polyps during colonoscopy. Specifically, our architecture employs a simple, convolution-free pyramid transformer as its encoder that is a flexible and powerful feature extractor. Next, a multibranch fusion decoder is employed to reserve the detailed appearance information and fuse semantic global cues, which can deal with blurred polyp edges caused by nonuniform illumination and the shaky colonoscope. Additionally, a hybrid spatial-frequency loss function is introduced for accurate training. We evaluate our proposed architecture on colonoscopic polyp images with four types of polyps with different pathological features, with the experimental results showing that our architecture significantly outperforms other deep learning models. Particularly, our method improves the average dice similarity and intersection over union to 90.7% and 0.848, respectively.

关键词： Polyp segmentation vision transformer convolutional neural networks colorectal cancer colonoscopy

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White Rabbit Technology Evaluation for the SPD Experiment 3

White Rabbit Technology Evaluation for the SPD Experiment

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Kozyrev, Daniil V. Kotov, Kirill A. Ryabikov, Dmitry S. Boykov, Alexey V. Mamoutova, Olga V. Bolsunovskaya, Marina V. Peter the Great St.Petersburg Polytechnic University Lab. of Industrial Systems for Streaming Data Processing St. Petersburg Russia

ISBN: (纸本)9798331516321

The high accuracy profile of the IEEE1588-2019 and its open-source implementation known as White Rabbit provide a standard technology for sub-nanosecond synchronization. The Spin Physics Detector experiment will rely on White Rabbit to perform triggerless data acquisition and event reconstruction. An overview of reports on White Rabbit accuracy and precision performance, including effects of temperature variation, shows the need for experimental evaluation of White Rabbit technology in specific test setups using particular White Rabbit devices. The presented results include measurements and analysis of a White Rabbit network topology with three cascaded WRS-18A switches and two CuteWR-A7 end nodes from Sync Technology. The results cover synchronization signal skew and relative jitter histogram analysis, the temperature effects of extreme cooling and heating on a switch and an end node, as well as the effects of ambient temperature changes. Obtained results demonstrate that both synchronization signal skew and rms jitter remain within the required limits of sub-nanosecond accuracy and 50 ps precision. © 2024 IEEE.

关键词： Timing jitter

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Removal of Diagonal Strip Noise in Airborne Ice Penetrating Radar data 10

Removal of Diagonal Strip Noise in Airborne Ice Penetrating ...

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10th international conference on Environmental and Engineering Geophysics, ICEEG 2023

作者： Luo, Kun Liu, Sixin Tang, Xueyuan Li, Lin College of Geo-Exploration Science and Technology Jilin University Changchun130026 China Polar Research Institute of China Shanghai200136 China School of Oceanography Shanghai Jiao Tong University Shanghai200030 China

Airborne ice-penetrating radar (IPR) is a powerful geophysical method for detecting ice thickness, subglacial topography and internal layers in the vast and frigid Antarctic ice sheet. data processing is an essential step in improving the signal-to-noise ratio (SNR). In order to eliminate the diagonal strip noise mainly present in airborne IPR data of the Amery ice shelf and Grove Mountain regions, traditional ground penetrating radar processing methods such as F-K (frequency-wavenumber domain) filtering and F-K migration are applied. The validity of the data processing methods is proved by the quantitative index of image quality evaluation. It provides a reference for subsequent airborne radar data processing and application. © Published under licence by IOP Publishing Ltd.

关键词： Topography

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