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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

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Local to global Tracker: A Siamese Network for Long-term Tracking 3

Local to global Tracker: A Siamese Network for Long-term Tra...

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2021 3rd international conference on Robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Yang, Ya Zhou, Xinyu Jiang, Kaixun Hou, Xiaonan Wang, Chunlei Zhang, Wenqiang 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS. © Published under licence by IOP Publishing Ltd.

关键词： Target tracking

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Protection of Substation Grounding Grid by Gadolinium Magnesium Sacrificial Anode 3

Protection of Substation Grounding Grid by Gadolinium Magnes...

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2022 3rd international conference on Electronic Engineering and signal processing, EESP 2022

作者： Peng, Dingda Liu, Ruilong Huang, Wenxun Wang, Xiaobo Xia, Fubing Zhai, Zhe China Railway First Survey and Design Institute Group CO LTD. Shaanxi Xian710043 China Xian Rail Transit Group Company Limited Shaanxi Xian710021 China School of Materials Science and Engineering Xi'An University of Technology Shaanxi Xian710048 China

Since lower galvanic efficiency and potential reversal might occur in magnesium (Mg) sacrificial anode applied in anti-corrosion project for grounding grid in substation, rare earth gadolinium (Gd) was incorporated into Mg sacrificial anode material for activating and improving the performance of the anode, and the influence of Gd concentration on properties of Mg sacrificial anode was evaluated by electrochemical method. The results revealed that the incorporation of Gd could effectively improve the electrochemical properties of magnesium sacrificial anode. The present of 0.1%(wt) Gd in the anode caused the best electrochemical properties as follows: the OCP decreased to as low as -1.78V (SEC) while the galvanic efficiency increased to as high as 62%, and the minimal anodic corrosion current, 0.0186mA, was realized, also the reverse potential and current density in DC leakage process increased from 500mV to 900mV and 1mA/dm2 to 2.27mA/dm2, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Gadolinium

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Research on printed circuit boards defect detection based on fusion dynamic convolutional DiffusionDet 3

Research on printed circuit boards defect detection based on...

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3rd international conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Wang, Xiuyou Huang, Guangren Liu, Huaming Sun, Haoyu School of Computer and Information Engineering Fuyang Normal University Anhui Fuyang236037 China

ISBN: (纸本)9781510681880

Printed circuit board defects will directly affect the performance of electronic equipment. The industry has been troubled by how to effectively detect the defects of printed circuit boards. To solve this problem, an improved algorithm based on DiffusionDet is proposed to detect PCB defects directly without relying on the query of experience objects or learnable objects. The specific processing process is as follows: firstly, Object Detection Convolution (ODConv) is used to replace part of the static convolution kernel of the feature extraction network to broaden the dimension of convolutional operation learning, so as to obtain feature maps that are more conducive to defect detection. Secondly, the GIoU loss function is used to replace the original IoU loss function to improve the model's ability to optimize the position of preselected frames. The experimental results show that the improved DiffusionDet average accuracy (mAP) is increased from 98.12% to 98.83%, showing better performance in the detection of printed circuit plate defects. © 2024 SPIE.

关键词： computer debugging

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A Review of Deep Learning-Based Object Detection Current and Future Perspectives 3rd

A Review of Deep Learning-Based Object Detection Current and...

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Sirisha, Museboyina Sudha, S.V. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

ISBN: (纸本)9789811978739

Object detection is a method to detect and localize the objects present in images and videos and stands as one of the challenging fields of computer vision. Object detection plays a crucial role in multiple real-time applications like video surveillance, autonomous driving, medical image processing, etc. Object detection key challenges such as detecting small objects and addressing class imbalance are addressed with various deep learning detection models. This review article classifies the object detection models into three main categories—Two-stage, One-stage and Transformer based detectors discussing the recent advanced developments in object detection listing some of the most important works in each category. Benchmark datasets used for object detection task with different metrics used for evaluating the performance of object detectors are listed. Performance comparison among various object detectors is plotted showing how advanced detectors achieve better accuracy compared to the existing example detectors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems 3rd

Text Analysis and Classification for Preprocessing Phase of ...

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

作者： Kadam, Vaishali P. Khandale, Kalpana B. C, Namrata Mahender Department of Computer Science and I.T Dr. Babasaheb Ambedkar Marathwada University Maharashtra Aurangabad India

ISBN: (纸本)9783031057663

Stop word removal is a basic morphological analysis or a process used to remove those words that appear frequently or repeatedly within the document text. These words do not affect generating language expressions but appear many times in default language transformation and communication. This caused us to acquire more space in memory and the allocation of the same words caused problems with memory indexing. In the case of big data analysis and information management applications, identification of such particular words present within the document is an important task. In this paper, text analysis was carried out to identify various stop words within the text. It is used as a pre-processing operation for the development of various natural language text processing and data mining techniques. This is important in part-of-speech tagging, sentiment analysis, content classification, semantic analysis, search engines, text summarization, named entity recognition, etc. Our proposed system discusses the role of text analysis techniques and pre-processing of language by removing stop words from a given input document written in Marathi text using a linguistic and rule-based approach. The system is particularly discussed for Marathi. Very little work has been done on stop word removal using a supervised approach and evaluating its impact on the performance of information retrieval systems and summarizing the text. The result get accuracy at 32.6% to compress the text. © 2022, Springer Nature Switzerland AG.

关键词： Semantics

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Research on the segmentation of pension market based on K-means clustering algorithm

Research on the segmentation of pension market based on K-me...

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computer science and Management Technology (ICCSMT), international conference on

作者： Xinyu Wei Xufang Li School of Management Shanghai University of Engineering Science Shanghai China

With the acceleration of the aging process and the extension of life expectancy, the community is increasingly concerned about the pension problem. In order to better serve the needs of the elderly, accurately match the right pension mode, help pension industry in China to achieve more accurate marketing, improve customer recognition and core competitiveness, this article through the questionnaire survey to collect the elderly data, the K-means clustering algorithm for data processing and analysis, and according to the clustering results of endowment group classification in detail. The study found that it was most appropriate to cluster the sample data into four categories, and put forward suggestions according to the characteristics of the four groups of people.

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart Data Integration and processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented Computing, ICSOC 2021

International Workshop on Artificial Intelligence for IT Ope...

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart Data Integration and processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented Computing, ICSOC 2021

ISBN: (纸本)9783031141348

The proceedings contain 32 papers presendted at a virtual meeting. The special focus in this conference is on Artificial Intelligence for IT Operations. The topics include: A Taxonomy of Anomalies in Log Data;little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection;MMRCA: MultiModal Root Cause Analysis;IAD: Indirect Anomalous VMMs Detection in the Cloud-Based Environment;comparing Graph Data science Libraries for Querying and Analysing Datasets: Towards Data science Queries on Graphs;gamification for Healthier Lifestyle – User Retention;streaming and Visualising Neuronal signals for Understanding Pain;graph Analytics Workflows Enactment on Just in Time Data Centres Position Paper;say No2Ads: Automatic Advertisement and Music Filtering from Broadcast News Content;data Centred Intelligent Geosciences: Research Agenda and Opportunities Position Paper;Design Patterns in an ERP Environment;towards a Semantics-Based Search Engine for Smart Contract Information;a Blockchain-Based Autonomous System for Primary Financial Market;multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud Computing;occupancy Estimation from Thermal Images;constraint-Aware Trajectory for Drone Delivery Services;seamless Synchronization for Collaborative Web Services;Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform;AI-Enabled Processes: The Age of Artificial Intelligence and Big Data;hybrid Recommendation of Movies Based on Deep Content Features;continuously Testing Distributed IoT Systems: An Overview of the State of the Art;towards Scalable Blockchains Using Service-Oriented Architectures;snapshot of Research Issues in Service Robots;automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networks;interactive Process Improvement Using Simulation of Enriched Process Trees;a Reproducible Approach for Mining Business Activities from Emails for Process Analytics;human-in

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Fourier Domain CT Reconstruction with Complex Valued Neural Networks 3rd

Fourier Domain CT Reconstruction with Complex Valued Neural...

引用

3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Domokos, Zoltán Varga, László G. Department of Image Processing and Computer Graphics University of Szeged Árpád tér 2. Szeged6720 Hungary

ISBN: (纸本)9783031090363

In computed tomography, several well-known techniques exist that can reconstruct a cross section of an object from a finite set of it’s projections, the sinogram. This task – the numerical inversion of the Radon transform – is well understood, with state of the art algorithms mostly relying on back-projection. Even though back-projection has a significant computational burden compared to the family of direct Fourier reconstruction based methods, the latter class of algorithms is less popular due to the complications related to frequency space resampling. Moreover, interpolation errors in resampling in frequency domain can lead to artifacts in the reconstructed image. Here, we present a novel neural-network assisted reconstruction method, that intends to reconstruct the object in frequency space, while taking the well-understood Fourier slice theorem into account as well. In our case, the details of approximated resampling is learned by the network for peak performance. We show that with this method it is possible to achieve comparable, and in some cases better reconstruction quality than with another state of the art algorithm also working in frequency domain. © 2022, Springer Nature Switzerland AG.

关键词： Neural networks

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An Improved LEACH Protocol Based on Location and Residual Energy for Wireless Sensor Networks 22

An Improved LEACH Protocol Based on Location and Residual En...

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3rd Asia-Pacific conference on Image processing, Electronics and computers, IPEC 2022

作者： Liu, Yongmin Shi, Tingting Liu, Yiming Liu, Hanlin Xu, Zhuonong School of Computer and Information Engineering Central South University of Forestry and Technology Changsha China Research Center of Smart Forestry Cloud Central South University of Forestry and Technology Changsha China

ISBN: (纸本)9781450395786

Normally, in the classical Low Energy Adaptive Clustering Hierarchy protocol (LEACH), the choice of cluster heads does not rely on the location of node and the residual energy of node. Under the circumstances, the cluster head nodes often die prematurely, resulting in a short lifetime of the whole network. Based on traditional LEACH protocols, this paper gave a new rule of cluster formation by K-means clustering method. When ordinary nodes are upgraded to cluster head nodes, the location and residual energy of nodes are comprehensive considered to improve the election method of cluster head. At the same time, the ordinary node closing to the sink node is marked as a special node, and it jumps out of the cluster structure to transmit data directly to the sink node. Thus, the LEACH-CS (Cluster Selection) algorithm was proposed. Then, we derived the formula of total energy consumption, calculated the optimal value of the number of cluster centers in the lowest energy consumption and the selection of special node distance by mathematical deduction. Finally, the effectiveness of the method was verified by simulations. The simulation results demonstrate that compared with the traditional LEACH protocol and LEACH-C protocol, the proposed algorithm LEACH-CS can effectively reduce the total energy consumption of the network and greatly prolong the network lifetime. © 2022 ACM.

关键词： Energy utilization

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Robustness Investigation of FOC of PMSG Based Variable-Speed Wind Turbine Connected on Grid 3

Robustness Investigation of FOC of PMSG Based Variable-Speed...

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3rd international conference on Advanced Electrical Engineering, ICAEE 2024

作者： Aoumri, Mohammed Harrouz, Abdelkader Yaichi, Ibrahim Baala, Seddik Wira, Patrice University of Ahmed Draïa Adrar Algeria Institute for Research in Computer Science Mathematics Automation and Signal University of Haute Alsace Mulhouse France

ISBN: (纸本)9798350349351

Wind energy has emerged as an increasingly viable renewable energy source, driven by rising fossil fuel costs, energy security concerns, and environmental considerations. In 2022, wind energy production grew by 9% compared to 2021, maintaining an annual rate exceeding 12% since 2012. This study focuses on the application of Field Oriented Control (FOC), a prevalent control strategy in wind power generation systems, to a permanent magnet synchronous generator (PMSG) in a variable speed wind turbine configuration. The research methodology involved implementing FOC and conducting robustness analyses through PMSG parameter variations. The primary objectives were to maximize wind energy capture efficiency while ensuring sustained control performance. MATLAB/Simulink was employed to simulate and evaluate the various components of the wind turbine system (WTS), facilitating comprehensive analysis of the results. © 2024 IEEE.

关键词： Windmill

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