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Sewer Defect Classification using Synthetic Point Clouds 16

Sewer Defect Classification using Synthetic Point Clouds

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16th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / 16th international conference on computer vision Theory and Applications (VISAPP)

作者： Haurum, Joakim Bruslund Allahham, Moaaz M. J. Lynge, Mathias S. Henriksen, Kasper Schon Nikolov, Ivan A. Moeslund, Thomas B. Aalborg Univ Visual Anal People VAP Lab Rendsburggade 14 Aalborg Denmark

ISBN: (纸本)9789897584886

Sewer pipes are currently manually inspected by trained inspectors, making the process prone to human errors, which can be potentially critical. There is therefore a great research and industry interest in automating the sewer inspection process. Previous research have been focused on working with 2D image data, similar to how inspections are currently conducted. There is, however, a clear potential for utilizing recent advances within 3D computer vision for this task. In this paper we investigate the feasibility of applying two modern deep learning methods, DGCNN and PointNet, on a new publicly available sewer point cloud dataset. As point cloud data from real sewers is scarce, we investigate using synthetic data to bootstrap the training process. We investigate four data scenarios, and find that training on synthetic data and fine-tune on real data gives the best results, increasing the metrics by 6-10 percentage points for the best model. Data and code is available at https://***/aauvap/sewer3dclassification.

关键词： 3D Deep Learning Defect Classification Synthetic Data Sewers Point Clouds Transfer Learning

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Secured On-Demand Adaptive Routing (SOAR) Protocol for Data Transmission in IoT Environment 1

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3rd EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2020

作者： Deepavathi, P. Mala, C. Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirapalli India

ISBN: (数字)9783030787509

ISBN: (纸本)9783030787493;9783030787523

The emerging concept Internet of Things (IoT) has the capability to communicate data among devices throughout the entire world without human intervention. Existing reactive routing-based protocol needs tremendous bandwidth, consumes more processing time and generates large number of control packets for communication. This chapter proposes an efficient Secured On-demand Adaptive Routing (SOAR) protocol for IoT environment with secured data transmission among nodes. The proposed protocol is simulated and tested by using NS2 simulators. The simulation results show that throughput and packet delivery ratios are better than existing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

关键词： Internet of things

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8th international conference on Technologies and Innovation, CITI 2022

8th International Conference on Technologies and Innovation,...

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8th international conference on Technologies and Innovation, CITI 2022

ISBN: (纸本)9783031199608

The proceedings contain 19 papers. The special focus in this conference is on Technologies and Innovation. The topics include: Data Stream Processing Method for Clustering of Trajectories;Low-Cost Energy Consumption Monitoring System Using NodeMCU;trend of the Use and Investment of Blockchain Technology in the Banking Sector in Ecuador;effectiveness of Monitoring Indicators in the Architecture of a Collaborative System;ioT Monitoring for Real-Time control of Industrial Processes;metric Identification Evaluating Security Information: A Systematic Literature Review;Evaluation of User Interface (UI) and User Experience (UX) for Web Services of a Weather Data Monitoring Platform;Comparison of Free Android Mobile 3D Modeling Tools for AR Apps;a Web App for Teaching Specialized English Vocabulary – Case of Study: computer Sciences;predicting Academic Performance in Mathematics Using Machine Learning Algorithms;analysis of Classification Algorithms for the Prediction of Purchase Intention in Electronic Commerce;alignment Techniques in Domain-Specific Models;IVRMaker, An Interactive and Customizable Telephone Chatbot Services Platform;digital Transformation of Health Care Services: Médikal Case Study;texture and Color-Based Analysis to Determine the Quality of the Manila Mango Using Digital Image Processing Techniques;Detection of Motorcyclists Without a Safety Helmet Through YOLO: Support for Road Safety;computer vision-Based Ovitrap for Dengue control.

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Stock Price Prediction using Machine Learning 3

Stock Price Prediction using Machine Learning

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Gangwar, Abhinav Kumar, Ayush Bijpuria, Eshikha Galgotias University Department of Computer Science and Engineering U.P Greater Noida India

ISBN: (纸本)9781665438117

In Stock Market Prediction, the aim is to predict the longer term price of the monetary stocks of an organization. The recent trend available market prediction technologies is that the use of machine learning that makes predictions supported the values of current exchange indices by coaching on their previous values. Machine learning itself employs completely different models to create expectation simpler and legitimate. The paper centers around the work of Regression and LSTM principally based Machine figuring out how to foresee stock qualities. Variables pondered region unit open, close, low, high and volume. In a financially volatile market, because the exchange, it's vital to own a really precise prediction of a future trend. as a result of the monetary crisis and marking profits it's important to possess a safe expectation of the upsides of the stocks. Anticipating a non-straight sign requirements progressed calculations of AI. The writing contains concentrates with totally extraordinary AI calculations like ANN (counterfeit neural organizations) with various element decision. The consequences of this examination can show that the algorithmic principle of grouping SVM (Support Vector Machines) with the help of highlight decision PCA (Principal part investigation) can have the accomplishment of building a benefit. © 2021 IEEE.

关键词： Data Analytics

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Hybrid Feature Extraction Improves Image Retrieval by Fusing Diverse Methods for Enhanced Content-Based Search

Hybrid Feature Extraction Improves Image Retrieval by Fusing...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： B. Buvaneswari Zainab Alassedi Gotte Ranjith kumar Mohammed I. Habelalmateen Ghazi Mohamad Ramadan Department of Information Technology Panimalar Engineering College Chennai India National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India The Islamic university Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

Content-based image retrieval (CBIR) is a critical domain in computer vision, dedicated to extracting images from databases based on their visual content rather than relying on text-based queries. Feature extraction plays a pivotal role in CBIR, converting image attributes like color, texture, and shape into numerical representations, facilitating efficient image matching. It lacks a comprehensive exploration of the distinct impacts of fusion methods and often falls short in detailing feature extraction architectures. This paper introduces a novel approach to tackle the proposing a hybrid feature extraction method. The method evaluates its performance using the Corel 1K dataset, a collection of 1,000 images spanning diverse categories, serving as a benchmark for assessing the efficacy of content-based image retrieval techniques in real-world scenarios. The results achieved by the proposed hybrid feature extraction method surpass those of existing models, with impressive precision (0.956), recall (0.872), and F -measure (0.892). This method is compared to the KMFO model and performance evaluation in CBIR using the SVM model. Future research is focus on refining and optimizing advanced feature extraction techniques to enhance the efficiency and effectiveness of CBIR systems.

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Deep Echo State Network Based Neuroadaptive control for Uncertain Systems 3rd

Deep Echo State Network Based Neuroadaptive Control for Unc...

引用

3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Xu, Baolei Chen, Qing Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province Sichuan University of Arts and Sciences Dazhou635000 China School of Electrical Engineering Chongqing University of Science and Technology Chongqing401331 China

ISBN: (纸本)9789811961410

This work presents a deep echo state network (DESN) based neuroadaptive control approach for a class of single-input single-output (SISO) uncertain system. In which, a DESN based on multiple reservoirs is applied for approximating the uncertain parts of the control system and the rigorous stability condition under the presented control strategy is analyzed. The availability of the approach is proved by comparison with the control technique using radial basis function neural network (RBFNN) and the control scheme using traditional echo state network (ESN) via numerical simulations, demonstrating that superior tracking performance is achieved by the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Uncertain systems

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An Identification of Guava Fruit Disease Using ML

An Identification of Guava Fruit Disease Using ML

引用

international conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Khandakar Rabbi Ahmed Towfika Salam Subarna Nandi Nazmul Hasan Omor Faruk Al Rashid Shafiun Miraz Md. Shafi Reza Farjana Rahman Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Rajshahi Bangladesh Dept. of Data Science London South Bank University 103 Borough Rd London Dept. of Computer Science and Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Graduate School of Integrated Energy of AI Jeonbuk National University Jeonju North Jeolla South Korea

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers’ socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley Data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used.

关键词： Training Accuracy Transfer learning Production computer architecture Optical fiber networks Convolutional neural networks

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Research on Three-Phase Electronic Load Based on Three-Vector Model Predictive Current control by Expected Sector

Research on Three-Phase Electronic Load Based on Three-Vecto...

引用

international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： Renxue Wan Mingzheng Shu Zhen Wang School of Electrical and Information Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Aiming at the problems such as low simulation accuracy, slow dynamic response and poor anti-interference ability of traditional control methods for linear or nonlinear loads, a three-vector model predictive current control method based on fast prediction sector judgment is proposed. Firstly, the mathematical model of α-β coordinate system is established, and the relationship between the expected voltage vector and the basic voltage vector is analyzed. Then using the sector decision condition proposed in this paper, the sector in which the desired voltage vector is located can be quickly determined. This method greatly reduces the amount of computation required to perform 6 times cost function evaluation in traditional three-vector control method. Finally, two-step prediction is introduced to compensate for sampling and calculation delay. The simulation and experimental results show that the proposed control method can shorten the adjustment time by 0.1s compared with the traditional PI control, effectively improve the simulation accuracy of nonlinear load, and reduce the calculation amount by about 70% compared with the traditional three-vector method.

关键词： Current control Accuracy Computational modeling Predictive models Vectors Mathematical models Nonlinear dynamical systems Phase locked loops Load modeling Predictive control

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Comparative Study of Activation Functions and Their Impact on the YOLOv5 Object Detection Model 3rd

Comparative Study of Activation Functions and Their Impact o...

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3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Doherty, John Gardiner, Bryan Kerr, Emmett Siddique, Nazmul Manvi, Sunilkumar S. Intelligent Systems Research Centre Ulster University Northland Road DerryBT48 7JL United Kingdom School of Computer Science and Engineering REVA University Karnataka560064 India

ISBN: (纸本)9783031092817

Object detection is an important aspect of computer vision research, involving determining the location and class of objects within a scene. For an object detection system to run in real-time, it is vital to minimise the computational costs while maintaining an acceptably high accuracy. In a Convolutional Neural Network (CNN) there is a direct correlation between the accuracy and the computational cost incurred by increasing the number of layers. Activation functions play a key role in a CNN to utilise nonlinearity to help balance the computational cost and accuracy. In this paper, a series of improvements are proposed to the state-of-the-art one-stage real-time object detection model, YOLOv5, providing the capability to enhance the overall performance. The validity of replacing the current activation function in YOLOv5, Swish, with a variety of alternative activation functions was investigated to aid in improving the accuracy and lowering the computational costs associated with visual object detection. This research demonstrates the various improvements in accuracy and performance that are achievable by appropriately selecting a suitable activation function to use in YOLOv5, including ACON, FReLU and Hardswish. The improved YOLOv5 model was verified utilising transfer learning on the German Traffic Sign Detection Benchmark (GTSDB) achieving state-of-the-art performance. © 2022, Springer Nature Switzerland AG.

关键词： Object detection

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A Review of Anomaly Detection Techniques Using computer vision 3rd

A Review of Anomaly Detection Techniques Using Computer Visi...

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3rd international conference on Recent Innovations in Computing, ICRIC 2020

作者： Mohindru, Vandana Singla, Shafali Department of Computer Science & Engineering College of Engineering Chandigarh Group of Colleges Mohali Punjab India

ISBN: (纸本)9789811582967

Obtaining videos for surveillance purpose or to use them for future predictions is a challenging task as a video has a large number of image frames displayed in a sequence, and modeling every frame is not possible, so various methods are used for building an intelligent vision system, which is used for obtaining videos and also in video anomaly detection. This paper provides an overview of research directions for different types of anomalies and also tells about different techniques in machine learning for managing the problem of anomaly detection in videos and images using computer vision. computer vision is used to make computers capable of extracting information from digital images or videos. It trains computers to interpret and understand the visual world. When machines detect errors, abnormal or unnatural behavior of datasets, it is called anomaly detection. In this paper, anomaly detection techniques and anomaly detection in datasets using computer vision are classified accordingly. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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