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Visibility restoration for real-world hazy images via improved physical model and Gaussian total variation

Frontiers of computer science 2024年第1期18卷 267-269页

作者： Chuan LI Enping HU Xinyu ZHANG Hao ZHOU Hailing XIONG Yun LIU College of Computer and Information Science Southwest UniversityChongqing 400715China School of Big Data and Intelligent Engineering Chongqing College of International Business and EconomicsChongqing 401520China College of Artificial Intelligence Southwest UniversityChongqing 400715China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China

1 Introduction Under real-world haze conditions,the existence of haze particles in the atmosphere reduces the visibility of captured ***,the noise is inevitably introduced into the degraded image,which further deteriorates the visual quality of the *** enhance the visibility and quality of outdoor real-world hazy images,numerous algorithms have been proposed to remove haze from a single input *** existing methods are broadly lumped into two categories:prior-based methods[1,2]and learning-based methods[3–6].Unfortunately,the widely used atmospheric scattering model and the corresponding haze removal methods fail to take the noise interference into account,which may result in poor visibility restoration performance.

关键词： visibility remove noise

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A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

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computers, Materials & Continua 2024年第2期78卷 1631-1648页

作者： Lanyao Zhang Shichao Kan Yigang Cen Xiaoling Chen Linna Zhang Yansen Huang School of Mechanical Engineering Guizhou UniversityGuiyang550025China School of Computer Science and Engineering Central South UniversityChangsha410083China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing100044China College of Civil Engineering Guizhou UniversityGuiyang550025China Guizhou Lianjian Civil Engineering Quality Inspection Monitoring Center Co. Ltd.Guiyang550025China

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be ***,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside ***,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature ***,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly *** two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection *** experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior *** the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 ***,it achieves 100%optimal detection performance in five *** the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

关键词： Anomaly detection normalizing flow source domain feature space target domain feature space bidirectional mapping residual network

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An Intelligent Self-Driving Car’s Design and Development, Including Lane Detection Using ROS and Machine Vision Algorithms 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Sujatha, E. Sundar, J. Sathiya Jeba Raju, D. Naveen Lakshminarayanan, S. Suganthi, N. Thandalam Chennai India Department of Computer Science and Engineering Velammal Institute of Technology Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India Sri Sairam Engineering College Chennai India SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

It is challenging to find a solution for lane detection. It has aroused the curiosity of the computer vision field for many years. It has been found that computer vision and machine learning algorithms struggle to tackle the multi-feature identification problem known as lane detection. Even though there are a few different machine learning approaches that may be used for lane identification, these approaches are often employed for classification rather than feature development. On the other hand, contemporary techniques of machine learning may be used to discover features that have a high recognition value, and they have shown success in feature identification tests. These strategies haven’t been applied correctly, which compromises their efficiency and accuracy when it comes to lane recognition. In this study, we provide a fresh approach to solving the problem. A brand-new preprocessing and Region of Interest (ROI) selection method is presented in this article. The major objective is to extract white features by making use of the HSV color transformation, adding preliminary edge feature detection while doing preprocessing, and then selecting ROI based on the preprocessing that was proposed. With the help of this cutting-edge preprocessing strategy, the lane may be found. The integrated autonomous vehicle that we envision is one that is controlled by a Robotic Operating System and that is capable of making intelligent driving choices. The unique filtering and noise reduction techniques that were used on the visual feedback by means of the processing unit served as the basis for the digital image-processing algorithm that was responsible for the greatest performance achieved by the autonomous vehicle. Within the control system, we used two separate control units, one of which was a master and the other of which was a slave. The master control unit is in charge of the visual processing and filtering, while the slave control unit is in charge of the vehicle’s propulsio

关键词： Autonomous vehicles

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Prediction of Insulin-Dependent T2DM in Patients Using Machine Learning Model 2

Prediction of Insulin-Dependent T2DM in Patients Using Machi...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gowthami, S. Venkata Siva Reddy, R. Ahmed, Mohammed Riyaz School of Electronics and Communication Engineering REVA University Bangalore560064 India HKBK College of Engineering Department of Computer Science and Engineering Bangalore560045 India

ISBN: (纸本)9798350327533

Diabetes, a widespread and enduring chronic ailment, exerts its impact on millions of people across the globe. Individuals with diabetes need regular insulin injections to sustain optimal blood sugar levels. Insulin is indispensable for persons with type 1 diabetes, although persons with type 2 diabetes may require insulin at a later stage. However, the current approaches for suggesting insulin dosage frequently prove inadequate, exhibiting either imprecision or complex procedures. Certain approaches require individuals to bear the responsibility, necessitating the manual calculation of insulin dosage using blood sugar levels and other factors - a complex and prone-to-error undertaking. Some individuals require closed-loop insulin administration devices, which are expensive and include wearing a continuous glucose monitoring (CGM) system. Recent estimates indicate that over 40% of patients who are suffering from (T2DM) in India and Gulf countries are utilizing insulin either on its own or in conjunction with Oral Antidiabetic Drugs (OADs) at any one moment. Lack of sufficient understanding regarding the use of insulin is expected to impact the acceptance, adherence, and effectiveness of therapy. This highlights the urgent requirement to examine the knowledge, and attitude, and suggest the usage of insulin to patients with both juvenile and type 2 diabetes. Insulin therapy is a crucial component of diabetes treatment;individuals with juvenile diabetes and the majority of those with type 2 diabetes will need insulin at some point. Proper injection technique is crucial for achieving glycemic control when using injectable medications such as human insulin, insulin analog, and glucagon-like peptide-l receptor agonists to manage diabetes. The proposed study focuses on determining the subset of individuals with diabetes who are taking both insulin and Oral Antidiabetic Drugs (OAD), and to assess the influence of insulin on glycemic control, ultimately resulting in improved

关键词： Insulin pens Machine learning algorithms Oral Antidiabetic drugs Precision medicine Type 2 Diabetes Mellitus

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Skeleton-Based Action Recognition Using Graph Convolutional Network with Pose Correction and Channel Topology Refinement

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computers, Materials & Continua 2025年第4期83卷 701-718页

作者： Yuxin Gao Xiaodong Duan Qiguo Dai School of Engineering Guangzhou College of Technology and BusinessFoshan528138China School of Computer Science and Engineering Dalian Minzu UniversityDalian116000China SEAC key Laboratory of Big Data Applied Technology Dalian Minzu UniversityDalian116000China

Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion *** video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation *** this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action ***,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature ***,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature ***,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition *** evaluate the model using top-1 and top-5 classification *** the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,*** the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.

关键词： Pose correction multi-stream fusion GCN action recognition

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Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network 21

Multi-Agent Graph Reinforcement Learning based On-Demand Wir...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhao, Ze Yu Che, Yueling Luo, Sheng Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9783903176553

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices’ own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs’ trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs’ self-attentions for their collaborative WET, we propose the multi-agent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices’ energy requirements. © 2023 IFIP.

关键词： Reinforcement learning

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Convolution Neural Network for Gastrointestinal Cancer Detection and Classification using Deep Learning

Convolution Neural Network for Gastrointestinal Cancer Detec...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sathishkumar, R. Nirmalraj, M. Govindarajan, M. Jaisree, J. Haripriya, L. Santhiya, M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Women's Engineering College Department of Computer Science and Engineering Puducherry India Annamalai University Department of Computer Science and Engineering Tamilnadu Chidambaram India

ISBN: (纸本)9798350315127

Ailments of the gastrointestinal tract (GIT), including bleeding, ulcers, polyps, Crohn's disease, and cancer, are becoming more prevalent. Ulcers and bleeding in the small and large bowels are two of them that are particularly common. Medical experts find the manual diagnostic process to be both time-consuming and difficult. As a result, experts have suggested computerised techniques for the identification and classification of certain disorders. Medical video endoscopy produces a huge volume of images, thus it takes a long time for specialists to look over them all. Early detection and diagnosis of certain illnesses can result in successful therapy. SqueezeNet, ResNet-101, and DenseNet-169 are three deep learning-based models that we introduce in this paper and assess for their capacity to diagnose a dataset of lower gastrointestinal disorders. There are 5,000 images in the Kvasir dataset, similarly distributed among five different lower gastrointestinal conditions, including ulcerative colitis, polyps, normal cecum, and normal pylorus. The deep feature vector is processed using the softmax activation function, which divides the input images into five groups. Surprisingly, all of the convolutional neural network (CNN) models had exceptional performance, with DenseNet-169 reaching accuracy, specificity, precision, recall, and F1 Score values of 97.8%, 98.07%, 97.5%, 96.02% and 97.6%. © 2023 IEEE.

关键词： Convolutional neural networks

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Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

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Big Data Mining and Analytics 2023年第2期6卷 170-184页

作者： Mohammed G.Albayati Jalal Faraj Amy Thompson Prathamesh Patil Ravi Gorthala Sanguthevar Rajasekaran Department of Mechanical Engineering School of EngineeringUniversity of ConnecticutStorrsCT 06269USA Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Mechanical and Industrial Engineering Tagliatela College of EngineeringUniversity of New HavenWest HavenCT 06516USA.

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over ***,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure *** is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on *** available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system ***,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the *** this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive *** study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in *** paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.

关键词： semi-supervised machine learning fault classification fault detection and diagnostics heating,ventilation,and air-conditioning data-driven modeling energy efficiency

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Enhanced Automatic Recognition of Human Emotions Using Machine Learning Techniques

Enhanced Automatic Recognition of Human Emotions Using Machi...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Monisha, G.S. Yogashree, G.S. Baghyalaksmi, R. Haritha, P. Computer Science Department Panimalar Engineering College Poonamallee Chennai600123 India Information Technology College Poonamallee Chennai600123 India

Emotion recognition plays an increasingly significant role in interpersonal relationships. Research on emotion recognition is the most active within the area. Therefore, there are many different techniques for recognizing human emotion. The majority of the time, one's emotions can be expressed through speech, hand movements, or physical gestures. Facial expressions are used to convey most emotions. The recognition of facial expressions is largely dependent on facial expressions. The identification of human emotions has been extensively studied, but there are no effective techniques that can identify them correctly. In order to overcome this problem, the proposed method implements a real-time emotion recognition system by using machine learning. © 2022 Elsevier B.V.. All rights reserved.

关键词： Emotion Recognition

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Personalized Assistance Through Artificial Intelligence: Smart Voice Appliances 8th

Personalized Assistance Through Artificial Intelligence: Sma...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Jain, Archana Rathore, Yogesh Singh Maurya, Santosh Kumar Malik, Suraj Bansal, Nidhi Pandey, Mahima Shankar Saraswat, Bal Krishna Computer Science and Engineering Department School of Engineering and Technology IIMT University Uttar Pradesh Meerut India Computer Science and Application Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Computer Science and Engineering Department School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Galgotia College of Engineering and Technology Uttar Pradesh Greater Noida India Nagpur Campus Pune India

ISBN: (纸本)9789819766772

In today’s world of growing technology, we can do things we never thought we could do before, but to achieve these ideas, there is a need for a platform that can do all our work easily and comfortably. So, we humans have developed applications such as the Human Voice Assistant with the ability to interact with the environment in one of the most visible forms of human interaction namely HUMANVOICE. The most popular Android mobile app "Google Assistant" and "Google Voice Search" were developed by Google. Various applications such as Microsoft Cortana and Amazon Alexa are also used as voice assistants. The Apple iPhone’s voice assistant named "SIRI" helps the users to communicate with the services and applications of iOS by voice and respond to the user’s voice commands. We have built an application where the voice assistant will be available on a specific desktop or laptop. It can change the overall course of interaction between the user and the system. The system designed here is such that all the services provided by the system are accessible through the user’s voice commands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： engineering education

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