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Formation Control Algorithm for Multiple Mobile Robots Based on Fuzzy Mathematics

Informatica (Slovenia) 2024年第10期48卷 51-64页

作者： Fan, Bingqian Artificial Intelligence and Big Data College Hebei University of Engineering Science Langfang050000 China

The formation of multiple mobile robots suffers from unsmooth formation trajectory tracking and large formation control errors. A three closed-loop sliding mode formation control method was proposed to solve these problems, which achieved stable three closed-loop control systems from both linear and angular velocity directions. Meanwhile, fuzzy theory was introduced to design a fuzzy sliding mode control model for multiple mobile robots, which made the gain switching smoother, and the problems of unsmooth and interference were solved in trajectory tracking. The results showed that the linear velocity fluctuation of the research designed model was controlled within 5 seconds, and the angular velocity fluctuation was controlled within 1 second. The overall mean value of position control indicators was 17.08, which was smaller than the comparison model. The average value of the comprehensive control index of position and speed was 2.8, which was smaller than the comparison model. The model designed in this research had higher control accuracy and more stable control effects, ensuring the efficient and high-quality execution of tasks by robots. This research provides a technical basis for the formation motion control of multiple mobile robots. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Angular velocity

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SGformer:Boosting transformers for indoor lighting estimation from a single image

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Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Leveraging Advanced Deep Learning Architectures for Precise Knee Osteoporosis Prediction: A Paradigm Shift in Diagnostic Healthcare 2

Leveraging Advanced Deep Learning Architectures for Precise ...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Keerthika, S. Janani, A. Krishnasamy, Lalitha DevaBrindha, S. Priyanka, S. Dharun, S.R. Department of Artificial Intelligence Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Kongu Engineering College Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College TamilNadu Perundurai India

ISBN: (纸本)9798331541217

Osteoporosis is a condition characterized by reduced bone mass and deterioration of bone tissue. It affects the people by increasing bone fragility and a higher risk of fractures. Globally, it affects approximately 200 million people, predominantly postmenopausal women and the elderly. Early detection of osteoporosis is crucial for reducing fracture risks, improving patient outcomes, and lowering healthcare costs. However, traditional diagnostic methods are often invasive and costly, particularly in developing regions. Current methods lack efficiency and affordability, underscoring the need for noninvasive and cost-effective diagnostic solutions. This paper explores the use of various deep learning techniques such as CNN, VGG16, ResNet, DenseNet, AlexNet, and GoogLeNet. Using deep learning models on knee X-ray images, this effort provides a simple, non-invasive approach to early osteoporosis detection. The models are assessed using accuracy metrics, and cross-validation on training, validation, and test datasets is used to validate their performance. The models' classification performance is also evaluated using F1-scores, accuracy, recall, and confusion matrices. The models are employed to analyze the images, with the aim of improving diagnostic accuracy and enabling early intervention. The overall efficiency is improved by using data augmentation approaches, optimizing the models and implementing efficient preprocessing steps. This study's primary objective is to develop and evaluate deep learning models that can use knee X-ray images to identify osteoporosis early, offering a quicker, non-invasive, and easier method of diagnosis. © 2024 IEEE.

关键词： Bone Fragility Deep Learning Early Detection Healthcare Efficiency Osteoporosis

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Advanced Parkinson’s Disease Classification using Hybrid Model: SVM and Random Forest 3

Advanced Parkinson’s Disease Classification using Hybrid Mo...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, S.H. Rajeshkannan, M. Anish, M.J. Sandeep, Pasupuleti Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331527549

Parkinson’s disease (PD) is a progressive and chronic neurodegenerative condition that significantly impairs motor and cognitive functions, posing a considerable burden on global healthcare systems. Early and precise diagnosis is vital to improving patient outcomes, yet traditional diagnostic techniques often encounter limitations due to overlapping symptoms with other neurological disorders and the subtle nature of early PD indicators. This research introduces a machine learning-driven approach that employs a hybrid ensemble model integrating Support Vector Machine (SVM) and Random Forest classifiers to enhance diagnostic accuracy by analyzing vocal and acoustic features. To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied, enabling the model to identify meaningful patterns within underrepresented data. Principal Component Analysis (PCA) is utilized to reduce dimensionality, preserving critical data variance while minimizing computational complexity and mitigating overfitting. Hyperparameter tuning further optimizes each classifier, bolstering the model’s accuracy and robustness. Comparative analyses demonstrate that this hybrid ensemble model effectively distinguishes PD patients from healthy individuals, showcasing its potential as a reliable predictive tool in clinical applications. This study highlights the promise of advanced machine learning methodologies in supporting early PD detection, offering clinicians a valuable resource for timely intervention and improved patient care. © 2025 IEEE.

关键词： hybrid classification Machine learning models Parkinson’s Disease principal component analysis (PCA) random forest algorithm support vector machine (SVM)

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Real-Time Driver Alertness Assessment Using Neural Networks

Real-Time Driver Alertness Assessment Using Neural Networks

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Yoga, M. Ramyasri, M.M. Lokitha, R. Jane Karunya, J. Jeeva, N. Kokila, M.M. Kongu Engineering College Department of Artificial Intelligence Tamilnadu India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Drivers have profited significantly from developments in computer technology with the introduction of intelligent car systems. However, driver weariness is a key contributing cause to many car accidents. This research suggests an intelligent system for detecting driver tiredness based on vision, employing a convolutional neural network (CNN) to capture latent facial traits rather than relying on hand-engineered features like blink rate, eye closure, and yawning. The driver is classified as either sleepy or not by a softmax layer, which enables the system to alert the driver and avoid collisions. The proposed approach relies on extracting features from driver photos using a CNN and assessing eye states and facial expressions to reliably identify tiredness. The algorithm's excellent accuracy in identifying sleepy states is demonstrated by the results, underscoring its potential to improve road safety and lower the number of accidents caused by fatigued drivers. © 2025 IEEE.

关键词： Convolutional neural networks

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Perpendicular-Cutdepth:Perpendicular Direction Depth Cutting data Augmentation Method

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Computers, Materials & Continua 2024年第4期79卷 927-941页

作者： Le Zou Linsong Hu Yifan Wang Zhize Wu Xiaofeng Wang Anhui Provincial Engineering Laboratory of Big Data Technology Application forUrban Infrastructure School ofArtificial Intelligence and Big DataHefei UniversityHefei230601China Institute of Applied Optimization School of Artificial Intelligence and Big DataHefei UniversityHefei230601China

Depth estimation is an important task in computer *** data at scale for monocular depth estimation is challenging,as this task requires simultaneously capturing RGB images and depth ***,data augmentation is crucial for this *** data augmentationmethods often employ pixel-wise transformations,whichmay inadvertently disrupt edge *** this paper,we propose a data augmentationmethod formonocular depth estimation,which we refer to as the Perpendicular-Cutdepth *** method involves cutting realworld depth maps along perpendicular directions and pasting them onto input images,thereby diversifying the data without compromising edge *** validate the effectiveness of the algorithm,we compared it with existing convolutional neural network(CNN)against the current mainstream data augmentation ***,to verify the algorithm’s applicability to Transformer networks,we designed an encoder-decoder network structure based on Transformer to assess the generalization of our proposed *** results demonstrate that,in the field of monocular depth estimation,our proposed method,Perpendicular-Cutdepth,outperforms traditional data *** the indoor dataset NYU,our method increases accuracy from0.900 to 0.907 and reduces the error rate from0.357 to *** the outdoor dataset KITTI,our method improves accuracy from 0.9638 to 0.9642 and decreases the error rate from 0.060 to 0.0598.

关键词： Perpendicular depth estimation data augmentation

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Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable *** available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal *** order to address the aforementioned issues,this paper delves into the study of attribute reduction *** of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute *** newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute ***,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute ***,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy *** effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information ***,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is *** experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction *** comprehensive analysis o

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

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An Innovative Method for Identifying Deepfake Videos Through Ensemble Learning 15

An Innovative Method for Identifying Deepfake Videos Through...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Keerthika, S. Santhiya, S. Jayadharshini, P. Ruthranayaki, J. Hariarasu, R. Naveenan, M. Kongu engineering College Department of Artificial Intelligence Perundurai India Kongu engineering College Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350370249

The detection of deepfake videos is essential due to the significant potential harm posed by manipulated media. Various deep learning techniques, including customized convolutional neural networks (CNNs), MobileNet, and DenseNet, have demonstrated promise in this domain. This study introduces a system for detecting deepfake videos by leveraging tooth and mouth movements, which are notably difficult to replicate accurately. The proposed methodology utilizes multiple transfer learning algorithms such as InceptionV3, MobileNet, VGG16, VGG19, Xception, and DenseNet121 to enhance the algorithm's capability to detect and classify deepfake videos based on biological signal attributes derived from jaw and tooth frames. The study specifically targets the identification of deepfake videos by extracting and cropping frames from the input video to emphasize the mouth and nose regions. These preprocessed frames are subsequently fed into a customized CNN trained to classify videos as real or fake, achieving an accuracy of 91.7%. Additionally, pre-trained CNN models DenseNet121 and MobileNetV2 further improve accuracy, attaining 88.4% and 90.9%, respectively. An ensemble approach that combines the predictions of these models results in an overall accuracy of 93.2%. © 2024 IEEE.

关键词： convolutional neural networks deep learning Deepfake detection multi-transfer learning video classification

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Advancements in Breast Imaging: CNN-based Thermogram Quality Enhancement 2

Advancements in Breast Imaging: CNN-based Thermogram Quality...

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

作者： Venkatachalam, Nirmala Risha Dharshini, S. Charanya, B. Ashwati, K. Easwari Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Breast thermography is a medical imaging technique that is free from radiation and is non-invasive which apprehends variations in temperature on the breast surface. The significant information, like early detection of potential abnormalities for breast health assessments, are provided. This study proposes an innovative approach to enhance the visual quality of breast thermograms utilizing a specialized Convolutional Neural Networks. The proposed methodology involves a tailored image enhancement model that is CNN-based to ascend the resolution, contrast, and overall quality of breast thermogram images. This research not only advances the state-of-the-art in breast thermogram image processing but also holds promise for enhancing the diagnostic capabilities of thermography in early breast cancer detection. The proposed CNN-based framework demonstrates its potential as a valuable tool in the realm of medical image analysis, contributing to improved clinical decision-making and patient outcomes in breast health assessments. © 2024 IEEE.

关键词： Abnormality Detection Breast Health Assessment Breast Thermography Convolutional Neural Network Diagnostic accuracy Image Enhancement Non-invasive Radiation free

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Quasi-Parallel Dense Video Captioning: A Novel Approach to Achieving Ground Truth Event Captions using Hierarchical Attention 2

Quasi-Parallel Dense Video Captioning: A Novel Approach to A...

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

作者： Kalai Selvi, T. Naveenkumar, S. Thompson, Michael Kesavaraj, R. Easwari Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Video captioning is a complex process, involving the automatic generation of descriptive, natural language narratives for video content. The current landscape of video captioning models primarily emphasizes crafting coherent, grammatically sound captions, yet often falls short in comprehensively capturing the essential events and actions portrayed in the video. Capturing ground truth events and localization of the generated predictions stays a challenge. This study introduces a novel video captioning model, Quasi-Parallel Dense Video Captioning (QPDVC), which directly targets authentic events within the video. QPDVC operates by concurrently analysing random frames, generating captions for each frame. These captions are then amalgamated, localized, and condensed to construct a cohesive story or summary. The distinct design of QPDVC is meticulously crafted to achieve elevated precision, rendering it particularly apt for applications such as sophisticated AI-based CCTV surveillance and advancing the realm of video captioning research. This approach not only enhances the interpretative capability of video captioning systems but also ensures a more accurate portrayal of the actual content. The model's framework provides an efficient mechanism to process and synthesize information, thereby offering a more detailed and nuanced understanding of the video's content. As the necessity for accurate video understanding grows across various domains, from security surveillance to content analysis, QPDVC's precision-driven methodology represents a significant step forward in the evolution of video captioning technology. © 2024 IEEE.

关键词： Attention Computer Vision Decoder Parallelization Deep Learning Encoder Feature Extraction Machine Learning Video Captioning

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