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pattern recognition and neural networks for acoustic monitoring and conservation of the historic port of Ancona 3

Pattern recognition and neural networks for acoustic monitor...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Di Loreto, Samantha Ricciutelli, Alessandro Lori, Valter Serpilli, Fabio Montelpare, Sergio Università degli studi G. D'Annunzio Viale Pindaro 42 Pescara60123 Italy Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60124 Italy

The soundscape of Ancona's historic port is a vital aspect of its cultural heritage, capturing the essence of its maritime and urban environment. This research focuses on the application of advanced sound event recognition techniques to characterize and preserve the unique auditory landscape of the port. Utilizing convolutional neural networks (CNNs) we developed an automated system capable of identifying and classifying sound events within the port's diverse acoustic environment. Our approach involves the extraction of significant "soundmarks" - unique sounds that define the auditory identity of the port, such as ship horns, dockyard activity, and traditional marketplace sounds. By capturing and analyzing these soundmarks, our system not only characterizes the existing soundscape but also aids in the preservation of these auditory elements, ensuring that they are protected in the event of environmental changes or disasters. The findings of this study underscore the importance of soundscape preservation as a critical component of heritage conservation, particularly for historic sites where auditory elements play a significant role in cultural identity. Our methodology provides a framework for the automatic extraction and classification of soundmarks, offering a valuable tool for heritage conservationists and urban planners in safeguarding the acoustic heritage of historical buildings and sites globally. © 2024 Copyright for this paper by its authors.

关键词： Urban planning

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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)9783031530357

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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Blockchain Technology's Role in an Electronic Voting System for Developing Countries to Produce Better Results 3

Blockchain Technology's Role in an Electronic Voting System ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Milcah Blessy, I. Manikandan, G. Robinson Joel, M. Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350343632

The centralized approach is used by traditional electronic voting systems. These systems have a central administration that controls the database and the entire system as a whole as well as the complete voting process. Accidental or deliberate, this might lead to issues including possible database tampering and duplicate voting. Many of these problems have been addressed by the application of permissionless blockchain technology in modern voting platforms;nonetheless, each voting action requires a certain amount of processing effort due to the conventional consensus process of such blockchains. The removal of the standard consensus procedures employed by public blockchains delay has a major influence on power consumption, impairs system productivity, and increases system utilization of energy. The main goal of this review was to evaluate the current status of voting systems that are electronic and powered by blockchain election research, along with any related difficulties, in order to anticipate potential future developments. This investigation led to the discovery that some of the problems now plaguing election systems may be resolved by blockchain technologies. In order to create an all-encompassing facial recognition system, the project suggests a unique Connected Neural Network noted as CNN architecture for recognising facial features. This pipeline involves the collection of real-time data, such as visages of people, pre-processing, training of models, and ultra-parameter optimisation. Additionally, an online application to post participation utilising Face recognition and the newly built innovative CNN model is being developed. Age detection AGES expanded as Ageing pattern Subspace, which is frequently done with the aid of technology or algorithms, is a method of estimating or identifying a person's age according to a variety of criteria. © 2023 IEEE.

关键词： Ageing pattern Subspace Blockchain Connected Neural Network Electronic voting system Face 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

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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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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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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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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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