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检索条件"任意字段=International Workshop on Intelligent Computer in Pattern Analysis/Synthesis"

共 294 条记录，以下是21-30 订阅

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CEUR workshop Proceedings

CEUR Workshop Proceedings

引用

workshop on Information Technology and Scientific Computing in the Framework of the 11th international Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, ITTMM 2021

The proceedings contain 9 papers. The topics discussed include: synthesis and computer research of a belt conveyor models with intelligent control;design and computer research of a nonlinear stochastic models describing the dynamics of interacting populations;construction and simulation of continuous time portfolio with given properties;computer simulation of the stochastic RED algorithm;comparation of two single-server queueing systems with exponential service times and threshold-based renovation;on statistical analysis and prediction of sap flow density for smart urban tree monitoring;model of radio admission control for URLLC and adaptive bit rate eMBB in 5G network;surrogate modeling assistant software;and implementation of an analytical-numerical approach to stochastization of one-step processes in the Julia programming language.

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Advancements in Weather Forecasting: A Review of Satellite Imagery and AI Integration

Advancements in Weather Forecasting: A Review of Satellite I...

引用

international Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Yunxiang Liu Shixuan Cai School of Computer Information Engineering Shanghai Institute of Technology China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

This review examines the integration of meteorological satellite imagery with artificial intelligence (AI), focusing on traditional machine learning and deep learning methods in weather forecasting. It begins by explaining the basic concepts and importance of satellite imagery in capturing highresolution atmospheric data. The study highlights how AI, especially deep learning, improves the analysis of these complex datasets, enhancing weather pattern identification and anomaly detection. We discuss the integration of satellite data with traditional forecasting methods, recent advancements in hybrid algorithms, and sophisticated data assimilation techniques. The review also covers deep learning models like CNNs, RNNs, and GANs, demonstrating their superior performance in predicting weather phenomena. Challenges such as high computational costs, resource requirements, and model generalizability are addressed, along with future directions in model optimization and hybrid approaches. This synthesis underscores the significant impact of combining satellite imagery with AI on weather forecasting accuracy and reliability.

关键词： Deep learning Accuracy Satellites Reviews Computational modeling Weather forecasting Focusing Predictive models Satellite images Reliability

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Structural Modeling of Technical Text analysis and synthesis Processes 4

Structural Modeling of Technical Text Analysis and Synthesis...

引用

4th international Conference on Computational Linguistics and intelligent Systems (COLINS)

作者： Bekesh, Rostyslav Chyrun, Liliya Kravets, Petro Demchuk, Andriy Matseliukh, Yurii Batiuk, Taras Peleshchak, Ivan Bigun, Roman Maiba, Igor Lviv Polytech Natl Univ Lvov Ukraine

The article presents the application of generative Grammars in linguistic modeling. Description of sentence syntax modeling is used to automate the processes of analysis and synthesis of natural texts.

关键词： Generative grammar structured scheme sentences computer linguistic system

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pattern synthesis of MIMO Radar Based on Immune Differential Evolution Algorithm

Pattern Synthesis of MIMO Radar Based on Immune Differential...

引用

2020 international Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2020

作者： Jiang, Ling Jiang, Yi Chen, Guangfu Shi, Guimin Cheng, Qi School of Mathematics and Computer Wuyi University Wuyishan354300 China Key Lab. of Cogn. Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan354300 China Big Data Development Administration of Anji County Huzhou313000 China

In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed. © Published under licence by IOP Publishing Ltd.

关键词： Optimization

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Conceptual Model of Process Formation for the Semantics of Sentence in Natural Language 4

Conceptual Model of Process Formation for the Semantics of S...

引用

4th international Conference on Computational Linguistics and intelligent Systems (COLINS)

作者： Bisikalo, Oleg Vysotska, Victoria Burov, Yevhen Kravets, Petro Vinnytsia Natl Tech Univ Vinnytsia Ukraine Lviv Polytech Natl Univ Lvov Ukraine

The article explains the use of generative grammars in linguistic modeling. Describes syntax modeling of sentence that used to automate the analysis and synthesis of natural-language texts. The article proposes conten... 详细信息

关键词： Content Generative Grammars Sentence Structure NLP Information Resource Content analysis computer Linguistic System Content Management System

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Peculiarities of Generation of Semantics of Natural Language Speech by Helping Unlimited and Context-Dependent Grammar 4

Peculiarities of Generation of Semantics of Natural Language...

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4th international Conference on Computational Linguistics and intelligent Systems (COLINS)

作者： Lytvyn, Vasyl Kubinska, Solomiya Berko, Andriy Shestakevych, Tetiana Demkiv, Lyubomyr Shcherbyna, Yuriy Lviv Polytech Natl Univ Lvov Ukraine Ivan Franko Natl Univ Lviv Lvov Ukraine

The article deals with the use of generative grammars in linguistic modeling. Description of sentence syntax modeling is used to automate the analysis and synthesis of natural-language texts.

关键词： Sentence structure generative grammar computer linguistic system

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An Hybrid Attention-Based System for the Prediction of Facial Attributes 4th

An Hybrid Attention-Based System for the Prediction of Fac...

引用

4th international workshop on Brain-Inspired Computing, BrainComp 2019

作者： Khellat-Kihel, Souad Sun, Zhenan Tistarelli, Massimo Computer Vision Laboratory University of Sassari Viale Italia 39 Sassari07100 Italy Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Room 1605 Intelligence Bulding 95 Zhongguancun East Road Beijing100190 China Computer Vision Laboratory Department of Biomedical Sciences and Information Technology University of Sassari Viale S. Pietro 43/b Sassari07100 Italy

ISBN: (纸本)9783030824266

Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach. © 2021, The Author(s).

关键词： Network architecture

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computer research of the controlled models with migration flows 10

Computer research of the controlled models with migration fl...

引用

workshop on Information Technology and Scientific Computing in the Framework of the 10th international Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, ITTMM 2020

作者： Demidova, Anastasia V. Druzhinina, Olga V. Masina, Olga N. Petrov, Alexey A. 6 Miklukho-Maklaya St. Moscow117198 Russia Federal Research Center "Computer Science and Control" of Russian Academy of Sciences 44-2 Vavilov St. Moscow119333 Russia Bunin Yelets State University 28 Communards St. Yelets399770 Russia

Nonlinear models of the interconnected communities population dynamics are considered taking into account migration and competition. Formulations of optimal control problems are proposed for models with migration flows. The control quality criterion for a three-dimensional migration-population model is considered in the framework of optimal control problems with phase and mixed constraints. computer research of nonlinear models with migration flows allowed us to obtain the results of numerical experiments on trajectory search and parameter estimation. To solve optimal control problems, we used numerical optimization methods and intelligent symbolic computing algorithms. These algorithms are based on the application of numerical optimization methods in combination with methods for generating control functions. The transition to the corresponding stochastic model with migration flows and optimal control is performed. In the stochastic case, the method of constructing self-consistent stochastic models is used. A comparative analysis of deterministic and stochastic models is carried out. The effects typical for controlled three-dimensional models with migration flows are revealed. Specialized software packages are used as tools for researching of models and solving of optimal control problems. These software packages implement algorithms for constructing trajectories, parametric optimization algorithms, and generating control functions, as well as numerical solutions of stochastic systems of differential equations. The obtained results can be used in problems of computer modeling of ecological, demographic and socio-economic systems, as well as in the problems of synthesis, optimal control and stability analysis of multidimensional stochastic models describing migration flows. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Stochastic models

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20th international Conference on Web Information Systems Engineering, WISE 2019 and on the international workshop on Web Information Systems in the Era of AI, 2019

20th International Conference on Web Information Systems Eng...

引用

20th international Conference on Web Information Systems Engineering, WISE 2019 and on the international workshop on Web Information Systems in the Era of AI, 2019

ISBN: (纸本)9789811532801

The proceedings contain 15 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Tourism analysis on Graphs with Neo4Tourism;can Reinforcement Learning Enhance Social Capital?;influence Maximization Based on Community Closeness in Social Networks;cloud Service Access Frequency Estimation Based on a Stream Filtering Method;leveraging pattern Mining Techniques for Efficient Keyword Search on Data Graphs;preface;local Differential Privacy: Tools, Challenges, and Opportunities;intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data;a Novel Event Detection Model Based on Graph Convolutional Network;range Nearest Neighbor Query with the Direction Constraint;knowledge Graph Data Management: Models, Methods, and Systems;SLIND$$^+$$: Stable LINk Detection;reInCre: Enhancing Collaborative Filtering Recommendations by Incorporating User Rating Credibility.

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Semantic Segmentation of 3D Facial Models Using 2D Annotations

Semantic Segmentation of 3D Facial Models Using 2D Annotatio...

引用

IEEE international workshop on intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Krasimir Tonchev Radostina Petkova Nikolay Neshov Agata Manolova Teleinfrastructure Lab Technical University of Sofia Bulgaria Faculty of Telecommunications Technical University of Sofia Bulgaria

ISBN: (纸本)9781665426060

Semantic information retrieved from the human face can improve human-machine interaction, add new level of information compression and expand the multi-modality in data analysis. Extracting such information is done using semantic segmentation of images of the human face. It consists of automatically identifying the areas of human facial image, defining the different face parts, that meaningful information for humans. These areas include nose, eyes, forehead, ears, etc. In this work we propose a new algorithm based on auto-encoder architecture for semantic segmentation of 3D models of the human face. These models are represented as mesh objects which further motivates us to use graph-convolutional neural networks for the implementation of the auto-encoder. Since no data of 3D face models with annotated facial parts is available, we approach the problem using publicly available 2D annotated data and analysis-by-synthesis approach. Experimental results validate our approach for 3D face semantic segmentation.

关键词： Solid modeling Image segmentation Three-dimensional displays Semantics Neural networks Nose computer architecture

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