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recognition of Traffic Signs with Artificial Neural Networks: A Novel Dataset and Algorithm 3

Recognition of Traffic Signs with Artificial Neural Networks...

引用

3rd international conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Kerim, Abdulrahman Efe, Mehmet Onder Hacettepe Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781728176383

Traffic sign classification is a prime Issue for autonomous platform industries such as autonomous cars. Towards the goal of recognition, most recent classification methods deploy Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). In this work, we provide a novel dataset and a hybrid ANN that achieves accurate results that are very close to the state-of-the-art ones. When training and testing on German Traffic Sign recognition Benchmarks (GTSRB) a top-5 classification accuracy of 80% was achieved for 43 classes. On the other hand, a top-2 classification accuracy of 95% was reached on our novel dataset for 10 classes. This accomplishment can be linked to the fact that the proposed hybrid ANN combines 9 different models trained on color intensity, HOG (Histograms of Oriented Gradients) and LBP (Local Binary pattern) features.

关键词： artificial neural networks image classification pattern recognition data preprocessing

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Dongmei Fan Wendou Yan Yi Liang Zhi Cheng Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou China

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid.

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conference Committees

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pattern recognition and Machine Learning (PRML), IEEE international conference on

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3rd international conference on Smart Multimedia, ICSM 2022

3rd International Conference on Smart Multimedia, ICSM 2022

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3rd international conference on Smart Multimedia, ICSM 2022

ISBN: (纸本)9783031220609

The proceedings contain 34 papers. The special focus in this conference is on Smart Multimedia. The topics include: Product Re-identification System in Fully Automated Defect Detection;a Real-Time Fall Classification Model Based on Frame Series Motion Deformation;GradXcepUNet: Explainable AI Based Medical Image Segmentation;non-invasive Anemia Detection from Conjunctival Images;3D Segmentation and Visualization of Human Brain CT Images for Surgical Training - A VTK Approach;the Energy 4.0 Concept and Its Relationship with the S3 Framework;a Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part I;A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part II;including Grip Strength Activities into Tabletop Training Environments;FUNet: Flow Based conference Video Background Subtraction;matrix World - A Programmable 3D Multichain Metaverse;matrix Syncer - A Multi-chain Data Aggregator for Supporting Blockchain-Based Metaverses;construction and Design of Food Traceability Based on Blockchain Technology Applying in the Metaverse;motion Segmentation Based on Pixel Distribution Learning on Unseen Videos;estimation of Music Recording Quality to Predict Automatic Music Transcription Performance;unleashing the Potential of Data Analytics Through Music;Impact of PGM Training on Reaction Time and Sense of Agency;epidural Motor Skills Measurements for Haptic Training;Sensorless Force Approximation Control of 3-DOF Passive Haptic Devices;passive Haptic Learning as a Reinforcement Modality for Information;IARG: Improved Actor Relation Graph Based Group Activity recognition;Lighting Enhancement Using Self-attention Guided HDR Reconstruction;moCap Trajectory-Based Animation Synthesis and Perplexity Driven Compression;hyperspectral Image Denoising Based on Dual Low-Rank Structure Preservation;simFormer: Real-to-Sim Transfer with Recurrent Restoration;metric Learning on Complex Projective Spaces.

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Analysis of English Pronunciation Transformation Text Model based on Neural Network pattern recognition Algorithm

Analysis of English Pronunciation Transformation Text Model ...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Yuanyuan Gong Jiang Xi teachers college Yintan City Jiangxi province China

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

This study proposes an English pronunciation transformation text model based on a neural network pattern recognition algorithm, which aims to address various challenges in speech processing through in-depth analysis and innovative approaches. The paper first introduces the basic principles of pattern recognition, emphasizing the transition from feature space to decision space. Subsequently, a literature review highlights the transformative impact of convolutional neural networks (CNNs) in diverse domains, including financial markets, neuroimaging, environmental monitoring, and more. Methodologically, the paper describes a novel pattern recognition algorithm based on spiking neural networks. The algorithm overcomes the limitations of multi-layer network learning through the Spike Propagation (Spike Prop) algorithm, which introduces error back-propagation for effective training. Ensemble methods are used to improve the generalization ability of the system, with a focus on neural network integration. For pronunciation transformation in text models, sound features are extensively studied, including features such as short-term energy, short-term average amplitude, and Mel-frequency cepstrum coefficients. The experimental part, conducted in a Windows 10 environment using Python, TensorFlow, and GTX1660 GPU, demonstrates positive results across 30 experimental groups. The system shows significant improvements in character recognition accuracy, a reduction in missing and multiple word occurrences, a reduction in typos, and an increase in word accuracy. These results provide a solid foundation for the future research and practical applications in natural language processing.

关键词： Training Analytical models Sentiment analysis Accuracy Text recognition Neural networks Spiking neural networks

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3rd international conference on Artificial Intelligence in HCI, AI-HCI 2022 Held as Part of the 24th HCI international conference, HCII 2022

3rd International Conference on Artificial Intelligence in H...

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3rd international conference on Artificial Intelligence in HCI, AI-HCI 2022 Held as Part of the 24th HCI international conference, HCII 2022

ISBN: (纸本)9783031056420

The proceedings contain 39 papers presendted at a virtual meeting. The special focus in this conference is on Artificial Intelligence in HCI. The topics include: Gamifying the Human-in-the-Loop: Toward Increased Motivation for Training AI in Customer Service;Analysis of the Impact of Applying UX Guidelines to Reduce Noise and Focus Attention;adoption and Perception of Artificial Intelligence Technologies by Children and Teens in Education;extracting and Re-mapping Narrative Text Structure Elements Between Languages Using Self-supervised and Active Few-Shot Learning;Speech Disorders Classification by CNN in Phonetic E-Learning System;transformer-Based Multilingual G2P Converter for E-Learning System;misinformation in Machine Translation: Error Categories and Levels of recognition Difficulty;emotional Communication Between Chatbots and Users: An Empirical Study on Online Customer Service System;give Me a Hand: A Scene-Fit Hand Posture Drawing Aid;trafne: A Training Framework for Non-expert Annotators with Auto Validation and Expert Feedback;customizable Text-to-Image Modeling by Contrastive Learning on Adjustable Word-Visual Pairs;Attention-Based CNN Capturing EEG Recording’s Average Voltage and Local Change;a New Human Factor Study in Developing Practical Vision-Based Applications with the Transformer-Based Deep Learning Model;scene Change Captioning in Real Scenarios;replacing Human Input in Spam Email Detection Using Deep Learning;Object Size Prediction from Hand Movement Using a Single RGB Sensor;a Systematic Review of Artificial Intelligence and Mental Health in the Context of Social Media;evaluation of Webcam-Based Eye Tracking for a Job Interview Training Platform: Preliminary Results;developing and Testing a New Reinforcement Learning Toolkit with Unreal Engine;evaluation on Comfortable Arousal in Autonomous Driving Using Physiological Indexes;Measuring and Predicting Human Trust in Recommendations from an AI Teammate;preface;foreword.

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification

Integrating Bi-LSTM and Extended Geological Constraints Meth...

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Cloud Computing, Big Data Applications and Software Engineering (CBASE), international conference on

作者： Xiang Li XiaLin Zhang ZhangLin Li ZhengPing Weng School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection.

关键词： Measurement Deep learning Cloud computing Accuracy Geology Computational modeling Big Data applications Data models pattern recognition Software engineering

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Electrical Power Enterprise Training System Based on Interactive 3D Virtual Reality

Electrical Power Enterprise Training System Based on Interac...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Qin Li Electric Power of Henan Zhengzhou China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, the electrical power enterprises require skilled personnel to operate and maintain the complex systems. The trainees need effective training for the systems to ensure safety, efficiency and reliability. The existing electrical power system faced several challenges due to complex systems and high voltage risks with limited hands-on experience. To overcome these issues, interactive 3D Virtual Reality (3D-VR) is proposed for maintaining realistic experience for the trainees. Initially, the Building Information Modelling (BIM) is utilized to generate 115 kV substation data for an accurate 3D model of electric power training system. Further, the BIM model is integrated with VR simulator, where it enables immerse experience for trainees in the substation environment. Then, the interactive 3D simulator is employed to create a realistic environment for enterprise training system to the trainers. Finally, Convolutional Neural Network (CNN) model is incorporated to evaluate the performance with complex pattern recognition. At last, the CNN model detects the unusual behaviors of trainees based on probability threshold and provides personalized feedback. From the results, the proposed interactive 3D-VR attained better results in terms usability (68.23 % ) and Learnability (45.32 % ) of when compared to existing VR.

关键词： Training Solid modeling Adaptation models Three-dimensional displays Substations Virtual reality pattern recognition Convolutional neural networks Integrated circuit modeling Usability

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Proceeding of PRIS 2021 - 2021 3rd international conference on pattern recognition and Intelligent Systems

Proceeding of PRIS 2021 - 2021 3rd International Conference ...

引用

3rd international conference on pattern recognition and Intelligent Systems, PRIS 2021

ISBN: (纸本)9781450390392

The proceedings contain 19 papers. The topics discussed include: metric learning for context-aware recommender systems;food safety pre-warning system based on robust principal component analysis and improved Apriori algorithm;toward tracing the source of web attacks targeted at web applications;method of physical inventory checking on cigarette stereoscopic warehouse based on uav;improved correlation filter visual tracker by using scale estimation network;face anti-spoofing by using feature fusion;age estimation from facial images using transfer learning and k-fold cross-validation;synthetic aperture radar image target recognition based on hybrid attention mechanism;a deep learning model capable of producing heatmap probabilities for characters in natural scenes;and research on distortion correction of particleboard surface defect image.

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A Neural Network Model-Based Inversion Method for Stratified Seafloor Geoacoustic Parameters in Shallow Water 3

A Neural Network Model-Based Inversion Method for Stratified...

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3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

作者： Zhang, Xiaorou Cui, Zhiqiang Wang, Qile Zhu, Hanhao Liu, Wei Chai, Zhigang Zhejiang Ocean University School of Marine Science and Technology Zhoushan China Zhejiang Ocean University School of Naval Architecture and Maritime Zhoushan China The 23rd Research Institute of China Electronic Technology Group Corporation Shanghai China

ISBN: (数字)9781665485951

ISBN: (纸本)9781665485951

At this stage, in the inversion research of shallow sea geoacoustic parameters, the traditional inversion method focuses on calculating the parameter inversion under the default seafloor layering, and does not clarify the seafloor layering. Therefore, when dealing with the experimental data under the unknown seabed stratification, the geoacoustic parameters that conform to the specific seabed stratification cannot be accurately given. Aiming at such problems, this paper proposes a layered shallow sea geoacoustic parameters inversion method based on a layered physical model of the seafloor and a neural network algorithm model. First, a subsea physical model with a layered structure is selected through simulation, and the fast field sound field method (FFM) is used to calculate the sound field, then the theoretical prediction value of the shallow sea acoustic pressure field under different layered conditions is obtained. Second, the pattern recognition neural network algorithm is established to analyze the characteristics of the predicted sound pressure field under different seabed layers. Finally, according to the layered results, the sound pressure field is substituted into the seabed parameters inversion model of the corresponding layers, and the seabed parameters inversion is performed to obtain the inversion results. Experiments results show that the number of model iterations in the neural network algorithm is less and the efficiency is higher. The problem of seabed stratification is effectively solved through the characteristic analysis of the sound pressure field, which greatly improves the convenience of the inversion work. © 2022 IEEE.

关键词： Acoustic fields

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