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Research on attention-based multiscale information fusion with the real-time instance segmentation method 2

Research on attention-based multiscale information fusion wi...

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2nd international conference on Algorithms, High Performance computing and Artificial Intelligence, AHPCAI 2022

作者： He, Yifan Huang, Hua Li, Qishen Zhang, Guiwen School of Information Engineering Nanchang Hangkong University Jiangxi Nanchang China School of Software Nanchang Hangkong University Jiangxi Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Jiangxi Nanchang China

ISBN: (纸本)9798350334784

Instance segmentation is a comprehensive computer vision task that involves a wide range of other tasks. Recently, the study of real-time instance segmentation methods has received more attention for the development of autonomous driving. Although existing real-time instance segmentation methods are fast, their accuracy does not meet practical needs. Most methods go for segmentation based on object detection, and their effectiveness is overly dependent on the effectiveness of detection. This paper proposes a new attention-based multiscale information fusion method based on Cheng, T. et al. [1]. Firstly, the PPM module of the baseline network is replaced with the module Multiscale Context Attention (MSCA) designed in this paper based on the baseline network, which uses atrous convolution with different ratios to obtain information of four scales, and then uses non-local attention to enhance the information of features. It can effectively suppress the interference of redundant information on the instance segmentation results. Secondly, a new feature fusion approach is designed, which no longer uses bilinear interpolation, but sub-pixel up sampling combined with attention. We did experiments related to this module on the coco dataset and demonstrated its effectiveness, with a 0.5% improvement over the baseline network. © 2022 IEEE.

关键词： Computer vision

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AITA: Accurate Network Topology recognition via Active Interception and Topology Analysis

AITA: Accurate Network Topology Recognition via Active Inter...

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Electronic Technology, Communication and Information (ICETCI), IEEE international conference on

作者： Rui Sun Department of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

Recent advances in wireless communication technologies have ushered in a significant opportunity to revolutionize the new network communication and combat strategies. The high efficiency of modern communication war requires that the key nodes and topology of wireless communication organization can be easily mastered with low computing cost. In complex and diverse communication situations, accurate identification of network topology becomes a key problem to improve communication efficiency and performance optimization. Traditional topology identification algorithms often rely on a single technology or data source, there are various communication environments and dynamic topology, so a more comprehensive and accurate method is needed. In this paper, we proposed a novel method called AITA for accurate topology identification of complex communication networks by a structured fusion of active listening and topology analysis. Specially, it involves sending out the interfering crafted packets to devices within the network and observing their responses to infer the topology. The topology analysis strategies, including graph theory and structured modeling, are automatically triggered to enhance the precision of topology identification in the presence of a significant error in the estimated location of active interception. Experimental results show that the proposed method performs well in accuracy, power consumption and recognition performance, and is superior to the traditional single detection and recognition methods. It provides a feasible and efficient solution to the topology identification problem in modern communication warfare, which lays the foundation for the future intelligent and adaptive communication system.

关键词： Wireless communication Analytical models Accuracy Adaptive systems Network topology Heuristic algorithms soft sensors

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Lunar Ejecta pattern Detection and Analysis Using Faster R-CNN and GAN Augmentation

Lunar Ejecta Pattern Detection and Analysis Using Faster R-C...

引用

international conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Premanand Ghadekar Chanchal Rathad Sourav Jangral Rushikesh Unde Adwait Mali Aryan Karande Dept. Of Information Technology Vishwakarma Institute of Technology Pune India

Gaining understanding of the Moon's geological and impact history requires a thorough analysis and inspection of its surface. Understanding the history of impact events, planetary development, crater formation mechanisms, geological composition, lunar regolith, planetary defense strategies, space weathering effects, and the broader field of comparative planetary science depend greatly on the analysis of ejecta patterns. This study examines the automated recognition and interpretation of complex lunar patterns from high-resolution lunar pictures using region-based convolutional neural networks. The model is customized and fine-tuned to identify a wide range of craters with prominent ejecta patterns. An essential component for enhancing the model's effectiveness and reliability is the integration of Generative Adversarial Networks. GAN s, a class of generative models, are employed to generate additional lunar images that closely resemble real lunar surface features. This augmentation strategy serves multiple technical purposes, such as improving resistance to noise and artifacts, expanding the training dataset, and enhancing overall robustness. The initial step encompasses essential tasks such as orthorectification and radiometric calibration, aimed at optimizing image quality and ensuring accurate geometric and radiometric characteristics. Subsequently, this study trained the model for feature extraction and region proposal generation. This is followed by region classification and bounding box regression to accurately pinpoint and categorize lunar crater patterns. The analysis encompasses characterizing ejecta patterns based on their size, shape, distribution, and their relationship with other lunar surface features. Proposed model has achieved an outstanding accuracy rate of 85.35% in the automatic recognition and interpretation of intricate lunar patterns.

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Deep Learning Based Image Analysis for Classification of Foot and Mouth Disease in Cattle

Deep Learning Based Image Analysis for Classification of Foo...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： R. Newlin Shebiah S. Arivazhagan Department of Electronics and Communication Engineering Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Sivakasi Tamilnadu India

Foot and mouth disease (FMD) poses a substantial threat to the global cattle industry, as it is a highly contagious viral infection. Timely detection and precise classification of FMD in cattle are essential for effective disease management and control. The proposed method harnesses the power of convolutional neural networks (CNNs) to automatically extract distinguishing features from cattle images and classify whether the cattle is diseased or not. Furthermore, a pre-trained architecture such as MobileNetV2 is employed and fine-tuned for FMD classification. Experiments were performed to assess the effectiveness of the proposed approach, taking into account metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate the efficacy of proposed deep learning-based classification approach, achieving impressive accuracy rates in differentiating between healthy and FMD-infected cattle.

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Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System

Promo-Zen: A Natural Language Processing Based Promotional T...

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Advancements in computing (ICAC), international conference on

作者： Chamath Udugampola Nethmi Weerasingha Department of Computer and Data Science NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. These objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. The system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8–10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use.

关键词： Named entity recognition Developing countries Message services Feature extraction Logic Data mining pattern matching Testing Context modeling

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

3rd International Symposium on Automation, Mechanical and De...

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3rd international Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022

ISBN: (纸本)9783031400698

The proceedings contain 38 papers. The special focus in this conference is on international Symposium on Automation, Mechanical and Design Engineering. The topics include: UWB Localization Based on Improved SO-Robust KF Fusion Algorithm;class-Imbalanced and Fine-Grained Object Detection in Video Surveillance for Production Safety;NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection;Image Reconstruction for Rain Removal in Both Wavelet and Spatial Frequency Sub-bands Using W-CycleGANs;Pedestrian Multi-object Tracking Based on ResNeXt and FairMOT;recognition of Leftover Food Based on Deep Learning;Task Offloading for UAV-Assisted of Collaborative Mobile Edge computing Network Based on Deep Reinforcement Learning;Multi-scale Rain Removal Across Multiple Frequencies Sub-bands Using MS-CycleGANs;research on the Optimization of the Assembly Sequence of Continuous Steel Arch Bridges with Multi-span Deck;different Structural Parameters Impacted on the Rough Surfaces Contact Force and Area Between the Base and the Magnetic Film Cantilever;performance Prediction Method of Laser Device Energy Component Based on Neural Network;Preliminary Design of 2D Ultrasonic Vibration Assisted Micro-milling (UVAMM) Using Piezoelectric Stack Actuator;self-Fitting Hearing Aid Algorithm Based on Improved Interactive Genetic Algorithm;bending Process Parameters Optimization of the 316L Tube with Small R/D Ratio: A Physical Prior Knowledge Based Bayesian Optimization Approach;Modified Johnson–Cook Model of AA5005 Alloy for Thermal Tensile Tests;vibration Prediction Method for New Buildings in Existing Metro Operation Area with soft Soil;mechanical Structural Improvement Design and Vibration Test of Coupling Guard for Pump System;effect of Horizontal Plates on the Sliding of Breakwater Caissons.

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Intrusion Detection on Industrial Networks Using Neuromorphic computing

Intrusion Detection on Industrial Networks Using Neuromorphi...

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international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Ali Mehrabi André Van Schaik International Centre for Neuromorphic Systems Western Sydney University

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

Cyber-attacks on Industrial Control Systems (ICS) present critical risks to operational stability, public safety, and national security. As industrial networks become more integrated and interconnected, their susceptibility to sophisticated cyber threats increases. Traditional security measures often fall short in detecting evolving attack patterns. This paper investigates the application of neuromorphic computing, which emulates the human brain's pattern recognition capabilities, as an innovative approach to intrusion detection in industrial networks. We introduce a hardware-based Spiking Neural Network (SNN) engineered to monitor and analyze communication packets in real time, detecting anomalies indicative of potential intrusions. The neuromorphic approach offers a promising solution to the growing challenge of cyber-attacks in critical infrastructure. The proposed system is implemented on an FPGA, focusing initially on monitoring key packet types such as LLDP, ARP, UDP, TCP, and Profibus RT, with scalability to accommodate additional protocols. The results demonstrate the efficacy of this SNN-based intrusion detection system in providing real-time, efficient, and accurate monitoring of industrial networks.

关键词： Neuromorphic engineering Scalability Intrusion detection Spiking neural networks Real-time systems Stability analysis Public security Security Monitoring Cyberattack

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Enhancing Weld Quality Assessment Using a Comprehensive Study on Combined Thermal and Residual Stress Through Lenet pattern recognition

Enhancing Weld Quality Assessment Using a Comprehensive Stud...

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international conference on computing and Networking Technology (ICCNT)

作者： G. Deepana V. L. Raja A. Parvathi Priya J. Geetha Priya M Rajendiran M. Dinesh Babu Department of Civil Engineering S.A. Engineering College Thiruverkaadu Chennai Tamilnadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamilnadu India Department of Chemistry R. M. K. Engineering College Kavaraipettai Tamilnadu India Department of CSE R.M.D. Engineering College Kavaraipettai Tamilnadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamilnadu India Department of Mechanical Engineering Raja lakshmi Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study aims to conduct a thorough examination of improving the evaluation of weld quality. This will be achieved by utilizing a mix of thermal and residual stress measurement, along with a pattern recognition approach employing the LeNet neural network architecture. The primary aim of this study is to provide a reliable and precise approach for assessing the quality of welds. In order to enhance the evaluation of weld quality, we propose the utilization of the LeNet convolutional neural network, which is a deep learning framework specifically designed for problems involving image identification. Photographs of weld cross-sections are analyzed using the network, which lets visual patterns of both thermal and residual stress distributions be captured. The findings of our research indicate a notable enhancement in the precision of weld quality evaluation when compared to conventional approaches. We use thermal and residual stress data along with deep learning techniques to make our method much better at finding and classifying defects. The proposed methodology exhibits significant potential for enhancing the overall safety and performance of welded structures across many sectors.

关键词： Measurement Deep learning Accuracy Welding Neural networks Feature extraction Residual stresses Convolutional neural networks Thermal stresses Convergence

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User Relationship Discovery Based on Telecom Data

User Relationship Discovery Based on Telecom Data

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yue Wang Wei Gao Xinzhou Cheng Xin Wang Lexi Xu Siwei Wang Yuanguang Wang Fanyu Meng Kunyan Li China Unicom Research Institute China Unicom Beijing China Full Customer Operation Center China Unicom Beijing China Science and Technology Innovation Department China Unicom Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the improvement of the mobile communication market, the smart home business of telecom operators is showing unprecedented development and contribution. With the full-service competition in the domestic telecommunications industry, operators are facing increasing competitive pressure. The home service has become one of the important competitions for operators. In order to solve the user’s relationship recognition problem, this paper compares five commonly used machine learning methods: logistic regression, decision tree, random forest, LightGBM and multi-layer perceptron. Based the experiments on the data set, the results demonstrate the efficient of the LightGBM method. Finally, this paper selects the LightGBM method as the technical method for user relationship recognition in the business.

关键词： Privacy Logistic regression soft sensors Smart homes Data models Telecommunications Decision trees

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Advanced Student Monitoring System for Physical Classes Using pattern recognition and Behavioral Analysis

Advanced Student Monitoring System for Physical Classes Usin...

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Advancements in computing (ICAC), international conference on

作者： R.K.A.R Rathnayaka Mallawaraarachchi S. M. A. H.G.N.D. Wijesiriwardana S.S.A Perera Jeewaka Perera Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research aims to develop an intelligent classroom system that utilizes Computer Vision and Deep Learning to measure students' concentration levels and performance, enabling teachers to gain insight into overall performance and adjust their teaching accordingly. The main goal of this research is to develop a precise way of tracking student concentration level and automate attendance marking in physical classroom. The proposed system consists of four components. To take attendance, it utilizes OpenCV and dlib for precise face recognition and eye gaze tracking to measure student focus levels. In the emotion analysis component, it employs DeepFace, dlib, and MediaPipe to identify students' emotional states and behaviors. For improving low-resolution video footage, the low-resolution enhancement component combines dlib and ESRGANG. In the concentration level component, Convolutional Neural Networks (CNN) are used to build a precise model for assessing students' concentration levels. The implemented system offers real-time attendance tracking, emotional and behavioral insights, and updates on students' concentration levels. It improves the detection of students at the back of the classroom using low-resolution footage. These features support educators in identifying students who need assistance, adjusting teaching methods, and creating a more engaging learning environment, ultimately enhancing classroom performance.

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