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Noise4Denoise:Leveraging noise for unsupervised point cloud denoising

Computational Visual Media 2024年第4期10卷 659-669页

作者： Weijia Wang Xiao Liu Hailing Zhou Lei Wei Zhigang Deng Manzur Murshed Xuequan Lu Deakin University GeelongVIC 3216Australia Department of Mechanical Engineering and Product Design Engineering Swinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science University of HoustonHoustonTX 77204-3027USA Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia

Existing deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth ***,in practice,it is not always feasible to obtain clean point *** this paper,we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during *** demonstrate that it is feasible for neural networks to only take noisy point clouds as input,and learn to approximate and restore their clean *** particular,we generate two noise levels for the original point clouds,requiring the second noise level to be twice the amount of the first noise *** this,we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise,and thus learn the displacement of each noisy point in order to recover the corresponding clean *** experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise,obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.

关键词： point clouds denoising geometry processing deep learning

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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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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural Networks

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SN computer science 2024年第8期5卷 1035页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory Networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory Networks (POCBi-LSTM) Weld defects Weld structures

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A robust image encryption method based on block diagonal chaotic architecture with CNN performance evaluation metric

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Multimedia Tools and Applications 2025年 1-35页

作者： Mamandi, Vida Ardalani, Nahid Ghalamkari, Behbod Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Mechanical Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In this paper, a new block diagonal chaotic model (BDC) is investigated due to higher necessity of advanced secure data transmission method in wireless medium and considerable limitation on computational storage space. In this model, the image sparse coding using BDC matrix in noisy and Rayleigh fading channels is studied. The structured BDC model is designed with two schemes, including chaotic pseudo inverse matrix (CPI-m) and block diagonal chaotic structured matrix (BDC-m) using two chaotic maps, in which the initial values for generating nonlinear chaotic sequences are used as private key at the receiver side. The simulation results show that the retrieved data measured based on CPI and BDC schemes outperforms the chaotic compressive sensing (CCS) method over AWGN and Rayleigh fading channels by 6% and 22.7%, respectively. Also, the sensitivity of recovered BDC-based image increases to 10(−32) compared to that of 10(−16) for conventional CCS based image. Moreover, the encryption key space increases from 10(37) for standard CCS-based measurements to β×10111 for measured data based on the BDC model, which guarantees that the competitors access no information. Considering datasets from iterative reconstruction process, the image features are extracted using convolutional neural network (CNN) model by ResNet-18 and GoogleNet architectures. The improved performance of data based on CPI and BDC model is validated by retrieved images accuracy over the classifier support vector machine (SVM). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fading channels

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Price Prediction of Resale Vehicle Through Supervised Learning Approaches 4

Price Prediction of Resale Vehicle Through Supervised Learni...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Paul, Ankit Guchait, Chiranjit Bhukta, Priyanath Computer Science Engineering Academy of Technology Adisaptagram India Mechanical Engineering Academy of Technology Adisaptagram India

ISBN: (纸本)9798350389128

The world is changing day by day and the expectations of every individual are also becoming high. Out of all the expectations, one of them is to buy a car. But all are not able to always buy a new car, so they will prefer a used one. In this study, we have conducted an analysis to help individuals make informed decisions about used car prices. By using supervised learning algorithms like multiple linear regressions and random forest regression along with Python, we have developed a model which can predict the price of resale vehicles. The dataset used here for prediction has been taken from *** and the final accuracy (R2) of multiple linear regression is 0.93464 and for random forest regression is 0. 93907. © 2024 IEEE.

关键词： Multiple linear regression

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Optimized and reliable nose cone layout for a non-control hypersonic flying vehicle

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Aerospace Systems 2025年 1-18页

作者： Nadjafi, Mohammad Malekian, Mohammad Amin Kia, Mostafa Abbasi Tehran Iran Mechanical Engineering Department Tarbiat Modares University Tehran Iran Department of Computer Science Lorestan University Lorestan Khorramabad Iran

Ballistic missiles are perhaps the most critical and common flying devices today, flying at very high altitudes compared to aeroplanes. Among the important points about these flying devices is their very high speed in the final phase, usually several times the speed of sound. Choosing essential design parameters for making these devices has always been a significant challenge for designers. Structural, propulsion, aerodynamic, and control parameters are the most influential. The topic of nose cone aerodynamics is one of the essential design components to achieve the most reliable and optimal designs. Deciding the right nose cone has been one of the main challenges in recent years in this field. This article aims to review and analyse some examples of standard nose cones with this structure. For this purpose, first, the arrangement and layout of various designs and their specifications have been presented. The nose cone modelling has been performed for all modes, and each layout's results have been introduced. Finally, a model based on the results is proposed, the most optimal and reliable arrangement based on aerodynamic parameters. © Shanghai Jiao Tong University 2025.

关键词： Ballistic missiles

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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya Lakshmi K.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer *** suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation *** time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁ*** of the most signiﬁcant requirements is to design optimal solutions to optimize their *** is no particular technique that can answer all optimization *** proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized *** accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization *** total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%*** proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional *** proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁ*** model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Non-contact Inspection of Electrically Discharged Materials Using Machine Learning 1

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2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Jhala, Devrajsinh Patel, Nirmit Dharia, Jemil Butani, Jemin Patel, Devesh Kiran, M.B. Department of Computer Science and Engineering PDEU Gandhinagar India Department of Mechanical Engineering PDEU Gandhinagar India

ISBN: (数字)9789819731732

ISBN: (纸本)9789819731725

Electrical discharge machining (EDM) is a very popular non-conventional machining technique for electrically conductive materials. In this process, surface roughness is an important characteristic of EDM-machined surfaces. This is because it affects the performance and durability of the machined parts. However, the conventional contact-based inspection methods for measuring surface roughness are time-consuming and can damage the machined surface. In this paper, we propose a non-contact method for inspecting electrically discharged materials using machine learning. We use three regression algorithms to predict the surface roughness of EDM-machined surfaces based on non-contact measurements using a self-made augmented dataset. Out of all three, K-nearest neighbors (KNN) was found to be the best performing algorithm, with a mean squared error (MSE) of 0.00157 and R2-score of 0.99. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Surface roughness

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