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检索条件"任意字段=3rd International Conference on Advances in Pattern Recognition"

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3rd international conference on Optimization, Learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, Learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530241

The proceedings contain 26 papers. The special focus in this conference is on Optimization, Learning Algorithms and Applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement pattern recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using Image Processing and Machine Learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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A Review of Recent advances in Deep Learning for Object Detection 3

A Review of Recent Advances in Deep Learning for Object Dete...

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3rd international conference on Innovations in Computer Science and Software Engineering, ICONICS 2022

作者： Fahad Safdar, Muhammad Sharif, Hanan Rehman, Faisal Bilal, Zahid Ahmad, Saqlain Maqsood, Hadia Lahore Leads University Department of Computer Science and Information Technology Lahore Pakistan

ISBN: (纸本)9781665461412

Computer Vision has given a way for computer systems to see by way of deciphering the surrounding objects, which has been considered a crucial problem over the years. There are quite a number of techniques devised for object detection and deep learning, but most of the research has been focused on deep learning in recent years. Visual object detection covers numerous recognition pattern tasks like image classification. This article aims to review a visual analytics approach for better understanding, identifying, and cleansing object detection frameworks and the effects of some factors such as sampling strategies, feature learning, detector architectures, proposal generation, etc., on visual object detection with special reference to detection components, learning strategies, and their applications along with benchmarks. © 2022 IEEE.

关键词： Object detection

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Detecting Islanding by Ensemble Classifier for pattern recognition Using HOG Features 3

Detecting Islanding by Ensemble Classifier for Pattern Recog...

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3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Pal, Kumaresh Goswami, Subhadip Debbarman, Sangeeta Manna, Pranendu Kumbhakar, Shyamal Bhuyan, Anshuman National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Techno India University Department of Electrical Engineering Kolkata India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350387193

The ever-increasing need for power encourages the growth of Distributed Generation (DG). Almost all DG sources are sustainably renewable. Islanding is one of the key challenges associated with excessive penetration of DG sources. The islanding could cause harm to the clients and their equipment. The islanding will be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In this paper, a novel approach for detecting islanding based upon image categorisation using ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. In the suggestive method, spectrogram images are obtained from the time-series signal which are derived from the point of common coupling. These images are utilised to extract the features using histogram of oriented gradient, which is then utilised as a feature vector input for the Ensemble classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter for deriving spectrogram image. The ensemble classifier's effectiveness is assessed with 5, 10, 20 and 25-fold cross-validations. The results from classification demonstrates that detection of islanding using ensemble classifier and histogram of oriented gradient feature being used for image classification achieved excellent results with 97.45% accuracy and validation error of 0.188. © 2024 IEEE.

关键词： Spectrographs

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New Invariant Meixner Moments for Non-uniformly Scaled Images 2nd

New Invariant Meixner Moments for Non-uniformly Scaled Image...

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3rd international conference on Digital Technologies and Applications

作者： Yamni, Mohamed Daoui, Achraf Karmouni, Hicham Sayyouri, Mhamed Qjidaa, Hassan Jamil, Mohammed Ouazzani Sidi Mohamed Ben Abdellah Fez Univ Dhar El Mahrez Fac Sci Lab Elect Signals & Syst Informat LESSI STICCED ST Fes Morocco Sidi Mohamed Ben Abdellah Univ Engn Syst & Applicat Lab Natl Sch Appl Sci BP 72My Abdallah Ave Km 5Imouzzer Rd Fes Morocco Cadi Ayyad Univ Natl Sch Appl Sci Marrakech Morocco Private Univ Fez UPF Syst & Sustainable Environm Lab SED Fac Engn Sci FSI Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

Invariants of discrete orthogonal moments have been applied in several research fields of image processing and pattern recognition due to their ability of representing digital images. Generally, moment invariants are invariant only for image translation, rotation and uniform scaling. These moments are not invariant when an image is scaled non-uniformly in the x- and y-axes directions. In this paper, we propose a new set of discrete orthogonal moments namely Meixner moments, which are invariant when an image is scaled uniformly/non-uniformly. The proposed invariants are completely independent of scale factors unlike some existing invariant moments in the literature. The experimental results show the efficiency of the proposed descriptors.

关键词： Non-uniform scaling Discrete orthogonal moments Meixner moments pattern recognition

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A temporal-spatial feature coordination network for change detection of VHR images 3

A temporal-spatial feature coordination network for change d...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

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Prediction and Incentive of Number of Participants in Rural Cultural Square Activities by Integrating pattern recognition Algorithm 3

Prediction and Incentive of Number of Participants in Rural ...

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3rd international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Ding, Hui Lianyungang Technical College School of Marxism Lianyungang China

ISBN: (纸本)9798350343922

The traditional method relies on historical data and simple statistical model to predict the number of participants in rural cultural square activities, which cannot fully take into account various complex factors and changes, and it is difficult to accurately predict and motivate the number of participants in rural cultural square activities. By integrating pattern recognition algorithms, a feasible method is provided to help organizers better plan and manage rural cultural square activities, in order to achieve the goals of community development and social interaction. This paper collected a large number of rural cultural square activity data and preprocessed the collected data. It combined the Long Short-Term Memory (LSTM) model and the Convolutional Neural Network (CNN) model to build the CNN-LSTM model. According to the number of participants predicted by CNN-LSTM model and the characteristics of activities, the incentive strategy is formulated. The experimental results showed that the accuracy of CNN-LSTM model in predicting the number of participants in rural cultural square activities was as high as 95.8%. The integration of CNN model and LSTM model can effectively improve the prediction of the number of participants in rural cultural square activities and improve the incentive effect. © 2023 IEEE.

关键词： Prediction models

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Optimization Strategy of Group Control Algorithm for Small and Medium-sized Elevators with Traffic pattern 3

Optimization Strategy of Group Control Algorithm for Small a...

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3rd international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2023

作者： Cheng, Haoyu Chen, Zhong Yu, Mengao Ding, Linzhi Changsha University of Science and Technology School of Electrical Engineering Chang Sha China

ISBN: (纸本)9781510666689

In the elevator group control dispatching system, the deterministic analytical algorithm is still widely used in practical applications, but the traditional algorithm lacks self-Adaptability to the elevator group control strategy, the processing efficiency of the call signal is low, and the selection of the elevator to be distributed is not flexible. To solve the above problems, traffic flow is introduced to identify traffic patterns and flexibly switch to optimize the stop sequence of call signals;Select the allocatable elevator flexibly, and use Poisson signal to simulate the passenger flow on MATLAB for simulation verification. The results show that after optimization, the owner's waiting time, congestion and elevator energy consumption are improved effectively. © 2023 SPIE. All rights reserved.

关键词： pattern recognition

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recognition and classification of ceramic sculpture styles based on pattern recognition and intelligent systems 3

Recognition and classification of ceramic sculpture styles b...

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3rd international conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Feng, Henan Liu, Jinfang College of Plastic Arts and Design Kunsan University Korea Republic of Yunnan National University College of Arts Kunming China

ISBN: (纸本)9781510680449

So far, pottery to create art has a history of thousands of years. Ceramics, sculpture art not only conforms to the trend of the times, but also into the characteristics of the times, in their own creation and development of the brand of the times. This paper summarizes the style of Chinese contemporary ceramic sculpture by analyzing the development history and current situation of Chinese ceramic sculpture, and giving in-depth thoughts to the future development trend. Combining factor analysis and K-mean algorithm, this paper can comprehensively identify and categorize the styles of ceramic sculpture. Firstly, the factor analysis method is used to find out the potential correlation between different styles, and then the K-mean algorithm is used to classify the sculptures of different styles, so as to realize the comprehensive identification and classification of ceramic sculpture styles, and to provide more accurate and comprehensive data support for the research of culture and art. The experimental results proved that the accuracy, checking rate and F1 value of K-mean algorithm are 98%, 87% and 56% respectively, which are better than the classification effect of SVM (support vector machines) algorithm. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： K-means clustering

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Application of pattern recognition Techniques for MathE Questions Difficulty Level Definition 1

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Azevedo, Beatriz Flamia de Souza, Roberto Molina Pacheco, Maria F. Fernandes, Florbela P. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300252 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabil & Tecnol Regioes Montan P-5300253 Braganca Portugal Univ Tecnol Fed Parana BR-80230901 Curitiba Parana Brazil

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Active learning is a modern educational strategy that involves students in the learning process through diverse interactive and participatory activities. The MathE platform is an international online platform created to support students and lecturers in the Mathematics teaching and learning process. This platform offers a tool to aid and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. The study proposed in this paper refers to a comprehensive investigation of the patterns that may exist within the set of questions available on the MathE platform. The objective is to investigate how to evaluate the student's opinions about the question's difficulty levels based on the variables extracted from student answers collected through surveys applied among the platform's users. Moreover, a comparative study between variables is performed using correlation and hypothesis tests. Furthermore, based on the results obtained for samples of different sizes, it was possible to define the most appropriate number of answers that should be considered to categorize the question's difficulty level. The results demonstrated that the variables extracted could be used to carry out the question level, and 30 answers are the most appropriate number of questions that must be used to categorize the question level.

关键词： active learning e-learning higher education Mathematics

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft Computing Paradigms for Load Balancing in Cloud Computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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