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Multi-View consistency-based point cloud registration method with low overlap rate 3

Multi-View consistency-based point cloud registration method...

引用

2023 3rd international conference on Measurement Control and Instrumentation, MCAI 2023

作者： Cui, Zhiqiang Liao, Zhaoyang Lin, Xubin Sun, Kezheng Cheng, Taobo Zhou, Xuefeng Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou China

Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system's geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Optical data processing

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Coke Quality Prediction Based on Blast Furnace Smelting Process data 5th

Coke Quality Prediction Based on Blast Furnace Smelting Proc...

引用

5th EAI international conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023

作者： Zhang, ShengWei Li, Xiaoting Yang, Kai Zhu, Zhaosong Wang, LiPing Nanjing Normal University of Special Education Nanjing210038 China

ISBN: (纸本)9783031505799

Coke is the main material of blast furnace smelting. The quality of coke is directly related to the quality of finished products of blast furnace smelting, and the evaluation of coke quality often depends on the quality of finished products. However, it is impractical to evaluate coke quality based on finished product quality. Therefore, it is of great significance to establish an artificial intelligence model for quality prediction based on the indicators of coke itself. In this paper, starting from the actual production case, taking the indicators of coke as the feature vector and the quality of finished product as the label, different artificial intelligence models are established. These models predict coke quality, and compare and discuss related algorithms, which lays a foundation for further algorithm improvement. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Coke

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data Classification Analysis for Alzheimer Disease Diagnostic 27

Data Classification Analysis for Alzheimer Disease Diagnosti...

引用

27th international conference on Circuits, Systems, Communications and Computers, CSCC 2023

作者： Valcheva, Virginia Georgieva, Olga Sofia University 'St. Kliment Ohridski' Faculty of Mathematics and Informatics Sofia Bulgaria

ISBN: (纸本)9798350337594

Alzheimer disease is a biological state of a chronic neurodegenerative disease, which cause is still unknown. In this context it is important to retrieve for existing dependencies of different factors and appearances of the cognitive disorders. Following this aim the objectivity of the present study is to apply an extended supervised machine learning frame to medical data of Alzheimer's disease. The extension of the investigations is in two directions. First, we explore and compare the grouping ability of seven different classifiers that cover at large the diversity of the known classification concepts. Second, the classification analysis is accomplished by a described data mining procedure. It is applied to different features spaces for the aim of finding models that increase the disease diagnostic. The data spaces are determined by using demographic data, data of cognitive tests and biomarkers. The data spaces were compared in their ability to present patterns of dependencies and in accuracy to indicate the disease's states and progression. The results obtained show that algorithms Random Forest, XGBoost and Logistic Regression are promising in diagnosing Alzheimer's disease. The conducted experiments present that cognitive test data boosts the classification accuracy. © 2023 IEEE.

关键词： Classification (of information)

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database Optimizers in the Era of Learning 38

Database Optimizers in the Era of Learning

引用

38th IEEE international conference on data Engineering (ICDE)

作者： Tsesmelis, Dimitris Simitsis, Alkis Athena Res Ctr Maroussi Greece

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

In this tutorial, we review advances made recently in a decades-old problem, namely query optimization. Along with the traditional optimization techniques many of which are still being used successfully in production, various techniques inspired by AI, such as genetic algorithms, had been explored as early as in the '90s as potential solutions, without gaining at that time much traction, especially in commercial offerings. More recently, with the rapid progress on learning, several approaches have brought this technology within the core of a database management system (DBMS) aiming at developing scalable, learning solutions to all challenging components of the system optimizer. We present the early efforts in this area, describe advancements, limitations and open issues, and discuss future research directions.

关键词： Query processing conferences Tutorials Production data engineering Artificial intelligence Optimization

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Research on Big data-Driven Network Security Threat Intelligence Analysis and Early Warning Model

Research on Big Data-Driven Network Security Threat Intellig...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Zhong Juncong Guangdong Business and Technology University Zhaoqing Guangdong China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

In this paper, a hybrid model combining static clustering algorithm and dynamic support vector machine (SVM) mechanism is proposed for the analysis and early warning of software malicious behavior. The model uses static clustering algorithm to classify massive threat intelligence data, extract key threat features, and then uses dynamic SVM mechanism to conduct fine analysis of clustering results to realize accurate identification and real-time early warning of malicious behaviors. Firstly, people construct a data set containing multiple threat intelligence sources, and extract the key indicators of software malicious behavior through feature engineering. Then K-means and other clustering algorithms are used to group these features and form a preliminary threat pattern. SVM algorithm was introduced to perform the secondary analysis of the clustering results, and through model training and verification, the detection ability of unknown threats was improved. The experimental results show that the model has high accuracy and low false positive rate in identifying malware behavior. This is especially true when dealing with large, high-dimensional data sets.

关键词： Support vector machines Analytical models Accuracy Heuristic algorithms Computational modeling Software algorithms Clustering algorithms Network security Feature extraction Classification algorithms

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Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data 4

Machine learning-based prediction of sorghum biomass from UA...

引用

4th international conference on Computing and Communication Systems, I3CS 2023

作者： Gano, Boubacar Ahmed, Nurzaman Shakoor, Nadia Donald Danforth Plant Science Center Saint LouisMO63132 United States

ISBN: (纸本)9798350323771

Unmanned aerial vehicle (UAV)-based remote sensing applications in plant phenotyping have received attention in modern plant breeding programs that increasingly have the need to automate time-consuming manual measurements of agronomic traits. This paper focuses on the prediction of sorghum biomass using machine learning algorithms such as Linear Regression, KNeighbors Regressor, and the XGBoost Regressor. Results from a field experiment of 344 sorghum genotypes conducted at the Donald Danforth Plant Science Center (Saint Louis, MO, USA) showed accurate prediction models. The K-Neighbors Regression model performed better than the other two models (R2=0.65, RMSE =4968.60kg/ha). The developed approach in this study could be used as a decision support tool for sorghum biomass phenotyping in breeding programs. © 2023 IEEE.

关键词： Remote sensing

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Hierarchical Diffusion for Offline Decision Making 40

Hierarchical Diffusion for Offline Decision Making

引用

40th international conference on Machine Learning

作者： Li, Wenhao Wang, Xiangfeng Jin, Bo Zha, Hongyuan Chinese Univ Hong Kong Sch Data Sci Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Tongji Univ Sch Software Engn Shanghai Peoples R China Shanghai Res Inst Intelligent Autonomous Syst Shanghai Peoples R China

Offline reinforcement learning typically introduces a hierarchical structure to solve the longhorizon problem so as to address its thorny issue of variance accumulation. Problems of deadly triad, limited data and reward sparsity, however, still remain, rendering the design of effective, hierarchical offline RL algorithms for generalpurpose policy learning a formidable challenge. In this paper, we first formulate the problem of offline long-horizon decision-MakIng from the perspective of conditional generative modeling by incorporating goals into the control-as-inference graphic models. A Hierarchical trajectory-level Diffusion probabilistic model is then proposed with classifier-free guidance. HDMI employs a cascade framework that utilizes the reward-conditional goal diffuser for the subgoal discovery and the goal-conditional trajectory diffuser for generating the corresponding action sequence of subgoals. Planning-based subgoal extraction and transformer-based diffusion are employed to deal with the sub-optimal data pollution and long-range subgoal dependencies in the goal diffusion. Numerical experiments verify the advantages of HDMI on long-horizon decision-making compared to SOTA offline RL methods and conditional generative models.

关键词： Diffusion

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Colorful h-star Core Decomposition 38

Colorful h-star Core Decomposition

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38th IEEE international conference on data Engineering (ICDE)

作者： Gao, Sen Li, Rong-Hua Qin, Hongchao Chen, Hongzhi Yuan, Ye Wang, Guoren Beijing Inst Technol Beijing Peoples R China ByteDance Beijing Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

The h-clique based higher-order cohesive subgraph mining is an important operator in graph analysis. The h-clique core and h-clique densest subgraph are two representative higherorder cohesive subgraph models which have been widely used in many practical applications. However, computing these two models on large graphs is often very costly due to the hardness of counting the h-cliques. In this paper, we propose a relaxed higher-order cohesive subgraph model, called colorful h-star core, based on counting the number of colorful h-stars. Unlike the h-cliques, we show that the colorful h-stars can be counted and updated very efficiently using a novel dynamic programming (DP) algorithm. Based on the proposed DP algorithm, we develop an efficient colorful h-star core decomposition algorithm which takes O(hxm) time and uses O(hxn+m) space, where m and n denote the number of edges and nodes of the graph respectively. In addition, we also propose a graph reduction technique based on our colorful h-star core model to accelerate the computation of the state-of-the-art approximation algorithm for h-clique densest subgraph mining. Moreover, we show that the colorful h-star core can also provide a very good approximation of the h-clique densest subgraph. The results of comprehensive experiments on 11 large real-world datasets demonstrate the efficiency, scalability and effectiveness of the proposed algorithms.

关键词： Computational modeling Heuristic algorithms Scalability conferences Approximation algorithms data engineering Dynamic programming

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GLOBAL OPTIMIZATION SOLUTION FOR DYNAMIC ADAPTIVE 360-DEGREE STREAMING 47

GLOBAL OPTIMIZATION SOLUTION FOR DYNAMIC ADAPTIVE 360-DEGREE...

引用

47th IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Wei, Xuekai Zhou, Mingliang Jia, Weijia Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China Beijing Normal Univ Zhuhai BNU UIC Inst Artificial Intelligence & Future Net Zhuhai Peoples R China UIC Guangdong Key Lab AI & Multimodal Data Proc Zhuhai Peoples R China

ISBN: (纸本)9781665405409

This paper proposes a global optimization solution that combines different types of perceptual information to further improve transmission efficiency. First, a new rate-distortion (R-D) model is proposed to reflect the characteristics of 360-degree video. Second, a globally optimized adaptive bitrate control algorithm is proposed using both the R-D models to adjust the bitrate for each tile in a segment. Finally, a new model parameter updating strategy that is robust to quality variations is proposed to further reduce prediction errors. Comparison results indicate that the proposed method can outperform state-of-the-art methods in terms of various quality of experience (QoE) objectives(1).

关键词： 360-degree video streaming quality of experience content-adaptive global optimization

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Prediction Model of Graduate Employment Rate Based on Chaotic Analysis and Least Squares Support Vector Machine

Prediction Model of Graduate Employment Rate Based on Chaoti...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Lei Zheng Lijuan Chen School of Business Shenzhen City Polytechnic Shenzhen Guangdong China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Graduate employment rate is one of the main indicators to measure the quality of college graduates, and its model and forecast play an important role in college employment. In view of the fact that the employment rate of college graduates is restricted by many factors, which leads to the relatively random and chaotic characteristics of employment, this project intends to use the method combining chaos theory and LS-SVM to predict the employment of college students. The chaotic analysis theory is introduced to reconstruct the historical data of graduate employment rate in phase space, and then the historical data of graduate employment rate after phase space reconstruction is used to train the least square support vector machine, and the graduate employment rate prediction model is established according to the optimal parameters. Finally, the simulation and comparative analysis are carried out with the current classical graduate employment rate prediction model. It is found that the prediction accuracy of this model is more than 93 % , and a better prediction result of graduate employment rate is obtained.

关键词： Support vector machines Analytical models Employment Education Information processing Predictive models Reconstruction algorithms data models History Forecasting

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