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Borehole Inner Surface visualization System with vibration Cancellation and Trajectory Smoothing Based on optical Monocular video Camera 9

Borehole Inner Surface Visualization System with Vibration C...

引用

9th International conference on Geographical information systems Theory, Applications and Management, GISTAM 2023

作者： Zong, Nan Al-Nuaimy, Waleed Lakany, Heba Worthington, Paul School of Electrical Engineering Electronics and Computer Science University of Liverpool Brownlow Hill Liverpool United Kingdom Department of Engineering & Development Robertson Geologging Ltd. York Road Deganwy United Kingdom

ISBN: (纸本)9789897586491

The rapid digitization and modelling of the planet brings with it increased demand for the tools necessary to process and visualize disparate streams of multivariate and often highly complex geophysical data streams. The rationalization of detection hardware and the integration of sensors offers the potential to economically visually explore and map geophysical environments such as the interior of subterranean boreholes. This paper addresses the challenge of visual reconstruction of the geometry of the inner surface of a borehole from video data collected via a monocular optical camera. We introduce a novel system of algorithms to unwrap the cylindrical borehole inner surface data and to compensate for the offsets and errors arising during data acquisition. Three modules are designed for this task: Unwrapping module consisting o algorithms to generate visualization results of borehole inner surfaces;vibration cancellation module that compensates for rotation and drift errors caused by the movement of detectors, balancing computational cost and performance;Trajectory smoothing based on image convolution signal processing methods to filter out anomalies and interruptions that arise as a result of the other processing stages. The proposed system integrates these modules to generate planar side-view images with a high level of spatial accuracy. This system also contributes to establish a novel and easy-to-access visualization tool of boreholes with simplified detectors that only consists of a monocular camera and a fixed circular LED band. Results has demonstrated the system is capable of resisting high frequency drift and the effects of rotation and vibrations in harsh subterranean environments. This novel combination of video and image processing marks a significant improvement over currently available or published borehole video exploration techniques, and can be further extended and enhanced to deliver more accurate multi-sensor 3D modeling and reconstruction of the

关键词： Data visualization

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Monte Carlo Simulation of Underwater optical Communication System with Optimized Coefficients 1

Monte Carlo Simulation of Underwater Optical Communication S...

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1st IEEE International conference on Advances in Signal processing, Power, Communication, and Computing, ASPCC 2024

作者： Patro, Sagarika Giri, Rajat Kumar International Institute of Information Technology Department of Electronics and Telecommunication Odisha Bhubaneswar India

ISBN: (纸本)9798350355093

High-speed data transmission in challenging aquatic environments may be possible with underwater optical communication (UWOC) systems. Nonetheless, displaying light proliferation submerged is complicated because of dissipating, retention, and other water-explicit properties. In this paper, we present a novel methodology that propels conventional Monte Carlo simulation procedures to work on both adaptiveness and effectiveness in UWOC framework simulation. Our work presents a more sensible photon communication model that adjusts to various water types, like clear sea, beach front waters with turbid conditions, by consolidating wavelength-dependent scattering and attenuation effects. Polarization effects, which have an effect on how light behaves in these underwater channels, are also taken into account. Not at all like earlier examinations, the proposed simulation is validated utilizing real time information, prompting more down to earth and solid outcomes. We introduce an adaptive optimization algorithm that automatically adjusts the communication parameters in real time to make the simulation even more effective. This permits the framework to limit the bit error rate (BER), guaranteeing ideal execution under changing circumstances. Furthermore, by consolidating machine learning procedures, we can anticipate the best correspondence boundaries without expecting to run broad reproductions for each new situation. The system becomes more responsive as a result. The proposed methodology propels the UWOC frameworks by giving an adaptable and strong structure equipped for taking care of the remarkable difficulties of various aquatic conditions. © 2024 IEEE.

关键词： Adaptive algorithms

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A novel heating load forecasting method based on information entropy attention mechanism 3

A novel heating load forecasting method based on information...

引用

3rd International conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Yin, Jianhua Zhang, Guobin Huo, Hongyan Zhang, Qian Inner Mongolia Electric Power Science and Research Institute Huhhot010020 China

ISBN: (纸本)9781510668522

Due to the high delay and large inertia characteristics, there are issues with inefficient regulation and energy waste in district heating systems. Improving the predictive ability of regional heating load can help improve the accuracy of thermal network control. In order to improve the accuracy of heating load prediction, this paper proposes a new heating load prediction algorithm, STL-IEA-LSTM, which combines LSTM model with information entropy attention mechanism to separately model the data modeling of heating load after STL algorithm decomposition, so as to enhance the prediction ability of heating load. The experimental evaluation conducted on the operating data of a heating station in Shijiazhuang during the heating season demonstrated that the STL-IEA-LSTM combination model constructed in this study achieved a MAPE of 0.7031%, indicating its superior performance in predicting heating load. Comparative analysis with other models, including STL-LSTM, entropy-based LSTM, classic LSTM, XGBoost, and MLP, further confirmed the effectiveness of the proposed method in achieving high prediction accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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A New Traffic Prediction Algorithm with Machine Learning for optical Fiber Communication System 3

A New Traffic Prediction Algorithm with Machine Learning for...

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3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Malhotra, Abhiraj Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350302288

optical Fiber Communication systems are increasingly used for transport networks due to the low cost, large capacity, and high bit rate of optical transmission [1]. However, effective traffic management and predicting future congestion levels is a critical challenge in order to ensure smooth and reliable operations. Machine learning algorithms can be used to accurately predict the future traffic volumes in such networks, providing real-time information on expected capacity usage and allowing operators to take proactive measures to prevent congestion. In this paper, we propose a novel Machine Learning based traffic prediction algorithm for optical Fiber Communications systems (OFCSs). The proposed algorithm uses a combination of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for predicting future traffic patterns. We train the model using a sequence of historical traffic data from an OFCS and evaluate it against benchmarks in terms of prediction accuracy and processing time. The results show that the proposed algorithm can make accurate traffic predictions, with a mean absolute error (MAE) of 0.036 for one-step-ahead predictions and 0.077 for five-steps-ahead. Furthermore, the processing time is significantly less than other benchmark models, providing a practical and reliable approach for predicting future traffic in OFCSs. We also discuss the implications of this study in terms of improving optical fiber capacity utilization and meeting the increased demand of growing data traffic. © 2023 IEEE.

关键词： optical fibers

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Number Plate Recognition Using optical Character Recognition (OCA) and Connected Component Analysis (CCA) 1

引用

5th International conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Ramya, Puppala Chowdary, Tummala Haswanth Teja, Pisupati Krishna Hrushikesh, Tadepally Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

The number of automobiles has expanded dramatically during the last few decades. As a result, tracking them became extremely difficult. In the event of a traffic ticket or excessive speeding, identifying the automobile owner has become nearly impossible. Image processing was used to identify car license plates to make this practicable. That license plates will be retrieved from collected photographs utilizing perception and computer vision algorithms, and then we will utilize OCR information to recognize the license number. OCR stands for optical character recognition. We employ cameras to record high-speed photos of number plates for image recognition, and image processing algorithms to identify and validate the sequence of characters, as well as to convert the number plate image to text. We now utilize number plate recognition (NPR) to detect license plates. NPR is a computer vision technique that enables equipment to scan license plates on automobiles swiftly and automatically without the need for human intervention. Image processing methods include Hidden Markov models, linear filtering, neural networks, and others. Our goal here is to recognize license plates so that we can readily follow automobiles in the event of a traffic penalty or excessive speeding. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character segmentation Number plate recognition (NPR) Recognized characters Vehicle number plate

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Interpreting and understanding the image using deep learning and imprecise information from the monitoring system

Interpreting and understanding the image using deep learning...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Kubanek, Mariusz Bobulski, Janusz Karbowiak, Lukasz Czestochowa University of Technology Department of Computer Science Czestochowa Poland

ISBN: (纸本)9798350324457

The interpretation and understanding of images using deep learning algorithms in monitoring systems is an important area of research in computer vision. In this paper, we propose a novel approach for interpreting and understanding images of human behaviour captured from cameras and drones in monitoring systems. In the work, we presented a pedestrian detection method. This is the first element of a larger system of interpreting the behaviour of traffic participants. We begin by pre-processing the images to remove noise and unwanted artefacts. We then use labelled and unlabelled data to train a deep learning algorithm to accurately classify and predict human behaviour. To account for imprecise information, we incorporate probabilistic models into the algorithm to allow it to make predictions with uncertainty. To evaluate the performance of our proposed approach, we use standard metrics, such as precision, recall, and F1 score, and compare our results to existing methods. We also identify the limitations of existing approaches and propose new methods for interpreting and understanding images that can improve their accuracy and reliability. Our proposed approach has the potential to provide a more accurate and robust method for interpreting and understanding images of human behaviour in monitoring systems. It can be used in various applications like traffic monitoring, security systems, and surveillance. Our results demonstrate the importance of incorporating imprecise information into deep learning algorithms to improve their performance and highlight the potential for future research in this area. © 2023 IEEE.

关键词： autonomous car computer vision pedestrian safety smart city

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Learning from synthetic data for object detection on aerial images

Learning from synthetic data for object detection on aerial ...

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2024 International conference on Image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Guo, Chao Yu, Yinhui Huang, Jinze College of Communication Engine Jilin University Jilin Changchun China College of Information Management Minnan Institute of Technology Fujian Shishi China Jilin Provincial Radio and Television Station Jilin Changchun China

ISBN: (纸本)9781510680425

Camera-equipped Unmanned Aerial Vehicles (UAVs) have been developed as autonomous vision systems and are widely used in various fields such as surveillance, search and rescue, and agriculture. Object detectors need to be robust to changing light, weather, and different application scenarios in real-time applications. Object detection algorithms have evolved considerably in these years, however the performance of object detection for UAVs has lagged behind. As it is largely limited by the dataset. Obtaining manually labeled object detection datasets on UAVs is undoubtedly expensive, and requires comprehensive consideration of the UAV's position, the object's size, and the weather, etc. Meanwhile, UAVs are prohibited from flying in certain scenarios, and the collection of data also raises privacy issues. To address the above problems, this paper proposes a method to enhance the data based on the Unreal Engine (UE) and Airsim synthetic dataset. We also take a Generative Adversarial Network (GAN)-based domain adaptive approach to make reduce the domain difference between synthetic and real data. © 2024 SPIE.

关键词： Generative adversarial networks

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Single cell clustering using ZINB Model and variational graph attention autoencoder 3

Single cell clustering using ZINB Model and variational grap...

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3rd International conference on Biomedical and Intelligent systems, IC-BIS 2024

作者： Zhang, Ge Kou, Xuye Chen, Yitong Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Single-cell RNA sequencing allows to discovery of new cell subtypes based on transcriptomic information. Clustering analysis is an effective approach for exploring single-cell heterogeneity. Nevertheless, current single-cell clustering methodologies are challenged by the high-dimensional, sparse, and dropouts on the one hand, and on the other hand, they neglect the thorough exploration of potential relationships between cells and noise removal. To tackle these challenges, this study introduces a novel self-optimized single-cell clustering algorithm named scZVEA, which combines Zero- Inflated Negative Binomial (ZINB) model and variational graph attention autoencoder. The scZVEA framework comprises three key modules. Firstly, a Deep Count Autoencoder (DCA) is designed to model data distribution and eliminate noise. Then, a variational graph attention autoencoder to extract latent features from the data. Lastly, the self-optimized clustering module enables the two previously independent clustering and feature modules to mutually benefit each other by iteratively updating cluster centers to further enhance clustering performance. Experimental findings based on six authentic scRNAseq datasets illustrate that the proposed clustering algorithm significantly enhances clustering accuracy when compared to alternative methods. © 2024 SPIE.

关键词： Clustering algorithms

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Application of Particle Image Velocimetry Method Based on Full Convolution Neural Network in Turbulence Field 16

Application of Particle Image Velocimetry Method Based on Fu...

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16th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Xie, Zhicheng Guo, Shuqiang School of Computing Northeast Electric Power University Jilin China

ISBN: (纸本)9798331505264

Exploring fluid flow phenomena has always been a hot topic in fluid mechanics research. Particle Image Velocimetry (PIV) is a non-contact technique applied in fluid mechanics laboratories for measuring the velocity field of fluids. The core of PIV lies in extracting displacement vector information from particle image pairs to ultimately generate a two-dimensional velocity field. Traditional methods typically estimate the two-dimensional velocity field using cross-correlation algorithms and optical flow techniques. In recent years, with the rapid development of artificial intelligence technologies and their high compatibility with PIV in image processing, there is significant potential and research value in designing deep learning techniques for accurately estimating fluid velocity vector fields in particle image velocimetry. Compared to traditional methods, the PIV technique proposed in this paper, based on fully convolutional neural networks, not only maintains the accuracy of velocity field estimation but also significantly enhances resolution and computational efficiency. Experiments demonstrate that the proposed method exhibits higher accuracy and stronger generalization capabilities. © 2024 IEEE.

关键词： optical flows

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Adaptive density peaking clustering algorithm combined with sparrow search algorithm 3

Adaptive density peaking clustering algorithm combined with ...

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3rd International conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Wu, Chenwen Li, Hengtong Lei, Dandan School of Electronics and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510668522

Aiming at the problem that the density peak clustering algorithm (DPC) can not adaptively adjust the cutoff distance and the clustering center needs to be specified manually, an adaptive density peak clustering algorithm combined with sparrow search algorithm (ISSA-ADPC) is proposed. Firstly, the parameter of local density and relative distance is considered comprehensively, and the adaptive selection of cluster centers is realized according to the arrangement order and downward trend of parameter;Secondly, the homogenized distribution Chebyshev chaotic map is used to initialize the sparrow population, and then the adaptive t distribution strategy is introduced to improve the sparrow search algorithm, which avoids the algorithm falling into local optimization. At the same time, the improved sparrow search algorithm is used to realize the adaptive adjustment of cutoff distance;Finally, Compared with the experimental results of K-means, DBSCAN, DPC algorithm on the artificial and UCI datasets, the proposed algorithm has higher accuracy, adjusted rand index and adjusted mutual information values, and has better clustering performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Chaotic systems

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