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Radar style transfer for metric robot localisation on lidar maps

CAAI Transactions on Intelligence Technology 2023年第1期8卷 139-148页

作者： Huan Yin Yue Wang Jun Wu Rong Xiong College of Control Science and Engineering Zhejiang UniversityHangzhouChina Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologyKowloonHong KongChina

Lidar and visual data are affected heavily in adverse weather conditions due to sensing mechanisms,which bring potential safety hazards for vehicle *** sensing is desirable to build a more robust navigation *** this paper,a cross-modality radar localisation on prior lidar maps is ***,the proposed workflow consists of two parts:first,bird's-eye-view radar images are transferred to fake lidar images by training a generative adversarial network *** with online radar scans,a Monte Carlo localisation framework is built to track the robot pose on lidar *** whole online localisation system only needs a rotating radar sensor and a pre-built global lidar *** the experimental section,the authors conduct an ablation study on image settings and test the proposed system on Oxford Radar Robot Car *** promising results show that the proposed localisation system could track the robot pose successfully,thus demonstrating the feasibility of radar style transfer for metric robot localisation on lidar maps.

关键词： lidar local maps

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Deep neural network for emojis-based sentiment classification

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Sagar, Kalpna Prakasam, Shanmugapriya Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Business Systems Rajalakshmi Engineering College Thandalam India

Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a Jellyfish Algorithm based on a Deep Convolution Neural Network (JA-DCNN) is developed for the sentiment classification of the emojis. Initially, the emoji images are collected from the open-source system. The proposed sentiment classification process uses a Jellyfish Algorithm (JA) optimized Deep Convolution Neural Network (DCNN). In DCNN, the hyperparameters are selected with the help of JA to improve accuracy and efficiency. The proposed method aims to improve the training features, maintain the resolution, reduce memory consumption, and improve the system’s accuracy. The proposed method decreases the computation cost and memory consumption, which empowers the system’s performance in emojis-based sentiment classification. The proposed method is implemented in MATLAB, and performance metrics of similarity and quality measurements evaluate performances. To validate the performance of the proposed methodology, it is compared with conventional methods such as MobilenetV2, ResNet18, and ResNet50, which have been proven to obtain better performance metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification Deep convolution neural network Emojis Jellyfish algorithm Sentiment analysis

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Cotransport of nanoplastics with nZnO in saturated porous media:From brackish water to seawater

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Journal of Environmental sciences 2025年第2期148卷 541-552页

作者： Yuanzhang Hou Yange Wang Yidi Zhang Zhibo Lu Zhenbo Zhang Zhiqiang Dong Yuping Qiu Department of Environmental Science College of Environmental Science and EngineeringTongji UniversityState Key Laboratory of Pollution Control and Resources ReuseShanghai Institute of Pollution Control and Ecological SecurityShanghai 200092China School of Medicine Tongji UniversityShanghai 200065China Municipal Environmental Protection Engineering Co. Ltd of CERC Shanghai GroupShanghai 201906China China Railway Engineering Group Co. Beijing 100039China

The ocean serves as a repository for various types of artificial ***(NPs)and nano zinc oxide(nZnO),which are frequently employed in personal care products and food packaging materials,are likely simultaneously released and eventually into the ocean with surface ***,their mutual influence and shared destiny in marine environment cannot be *** study examined how nanomaterials interacted and transported through sea sand in various salinity *** showed that NPs remained dispersed in brine,while nZnO formed *** seawater of 35 practical salinity units(PSU),nZnO formed heteroaggregates with NPs,inhibiting NPs mobility and decreasing the recovered mass percentage(Meff)from 24.52%to 12.65%.In 3.5 PSU brackish water,nZnO did not significantly aggregate with NPs,and thus barely affected their ***,NPs greatly enhanced nZnO transport with Meff increasing from 14.20%to 25.08%,attributed to the carrier effect of higher mobility *** from brackishwater to seawater was simulated in salinity change experiments and revealed a critical salinity threshold of 10.4 PSU,below which the mobility of NPs was not affected by coexisting nZnO and above which nZnO strongly inhibited NP *** study highlights the importance of considering the mutual influence and shared destiny of artificial nanoparticles in the marine environment and how their interaction and cotransport are dependent on changes in seawater salinity.

关键词： Homoaggregate Heteroaggregate Transport Sea sand Salinity threshold

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Density Based Clustering Approach Combined with Fuzzy Logic for Elimination of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2025年 1-14页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road Mankundu West Bengal Chandannagar712139 India

The reduction of impulse noise is crucial in processing pictures since it directly impacts the patterns of noise present. This paper proposes a two-step technique, known as DCIFF (DBSCAN clustering identified fuzzy filter), to effectively eliminate impulsive noise from digital images. The suggested DCIFF methodology employs the DBSCAN clustering algorithm to identify and classify noise. Subsequently, a noise reduction technique is presented, which relies on the principles of fuzzy reasoning. The density-based clustering approach DBSCAN is utilized to identify commonalities throughout all 5 × 5 sized windows that are created by centering each of the pixels in the picture. Next, to properly reduce noise, a sized 7 × 7 window is formed with the centre of each identified disturbed pixel. The non-noisy pixel intensities are multiplied by their triangle fuzzy membership values, and then the products are added together. The total result is then divided by the sum of the fuzzy membership of all the non-noisy pixels’ intensities. The outcome is utilized to eliminate the noise from the central pixel that is affected by noise. The DCIFF is assessed using standard image evaluation criteria for various pictures. On the Lena image modified by random valued impulsive noise at 80% noise density, DCIFF provided peak-signal-to-noise ratios (PSNR) of 25.86 dB and structural similarity indexes (SSIM) of 0.8429. In contrast to prior research on the same topic, the current study findings consistently demonstrate striking outcomes. © The Institution of Engineers (India) 2025.

关键词： Fuzzy logic

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Non-Invasive Skin Disease Diagnostic System Using Deep Learning 4

Non-Invasive Skin Disease Diagnostic System Using Deep Learn...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Tomar, Vivek Bisht, Kamlesh Singh Panwar, Ayush Dimri, Reetesh Kumar Negi, Mayank Singh Sharma, Swati Computer Science & Engineering Department Uttarakhand Dehradun India Kiet Group of Institutions Computer Science & Engineering Department U.P. Ghaziabad India

ISBN: (纸本)9798350387490

Human skin is an important part of the body that needs attention. Skin diseases are quite common in a country like India with varied climate and high pollution levels. The usual diagnosis of skin diseases is based on visual examination and certain tests performed by a dermatologist. But this is a time-consuming process and can also end up in the wrong diagnosis. Skin disease diagnosis has undergone a revolution because of the significant development in the field of machine learning, notably convolutional neural networks (CNN). CNNs have the potential to completely change the way skin illnesses are diagnosed, tracked, and treated. This study investigates the application of various machine learning techniques in skin problem identification. By using CNN analysis of skin disease images, identification of patterns and making accurate predictions can be done with an unprecedented accuracy and consistency. In addition to diagnosis, machine learning models can treat different skins individually according to their type, thus simplifying the doctor's role. This study aims to use CNN to improve dermatology practices by introducing efficiency and accuracy. This study includes a comprehensive literature review examining existing CNN models and performance metrics. Goals include collecting health data, using CNN's predefined models, comparing their performance, recommending the best models for diagnosing skin diseases, and improving the user interface. It presents a benchmark-based neural network model that may contribute to advances in dermatology in identifying skin diseases through machine learning using data from HAM10000 and DERMNET. The effectiveness of the model is determined by comparing it with other existing models by evaluating quality indicators such as accuracy, precision, and F1 score. © 2024 IEEE.

关键词： Convolutional neural networks

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Birch Clustering Approach for Detection of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2024年 1-9页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road West Bengal Mankundu712139 India

computer vision relies on image processing for autonomous driving, surveillance, and medical imaging. Clustering, an unsupervised learning approach, is essential for picture data organization and smooth pre-processing. Photo noise has been removed using K-Means, K-Medoid, and Fuzzy C-Means clustering methods. K-means, K-Medoid, and Fuzzy C-Means may not cluster huge datasets well with limited memory or CPU. Traditional clustering methods struggle to accommodate running durations and quality as dataset quantities rise. Birch clustering, or Balanced Iterative Reducing and Clustering utilizing Hierarchies, is frequently used in image processing because of its scalability and efficiency. Hierarchies help BIRCH summarize the dataset while maintaining as much information as feasible. The smaller summary follows the larger dataset. BIRCH is often used alongside other clustering methods to compress the dataset for the next step. Birch clustering is scalable, efficient in high-dimensional spaces, and can handle enormous datasets. Birch clustering regularly builds a tree structure to arrange images into a hierarchy of sub-clusters for effective segmentation and representation. Birch clustering divides images into sections by examining pixel intensities or characteristics for image segmentation. Birch clustering identifies typical centroids inside clusters to simplify feature extraction and allow meaningful picture data displays. Its noise reduction and data distribution adaptability make it suited for many academic and industrial image processing tasks. Birch clustering's hierarchical tree structure allows for scalability, unlike k-means' centroids-based clusters. Birch clustering creates a hierarchical tree using image data sub-clusters and centroids. It handles massive datasets thanks to its efficient memory storage. It offers scalable and efficient clustering with decreased computing complexity by dividing and combining data to create a hierarchy. The detection capabilitie

关键词： Image segmentation

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A Comparative Analysis of CNN and Transfer Learning Algorithms in Predicting Alzheimer's Disease Stages Using Neuroimaging

A Comparative Analysis of CNN and Transfer Learning Algorith...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sharma, Meenakshi Bhagat, Bhavneet Kumari, Vandana Bhumi Sharma, Naman Singh, Dipti Global Group of Institutes Department of Computer Science Engineering Amritsar India Guru Nanak Dev University Department of Computer Science Engineering Amritsar India

ISBN: (纸本)9798350373783

Alzheimer's disease (AD) prediction plays a critical role in early detection and intervention, allowing for better patient care and management. This paper investigates the predictive capabilities and state-of-art of Convolutional Neural Networks (CNN) and transfer learning models, including Inception V3, Resnet-18, and Resnet-50, for the progression of Alzheimer's disease (AD). The Alzheimer's disease Neuroimaging Initiative (ADNI) dataset used in this study, which includes multimodal neuroimaging, clinical, genetic and demographic data, the research aims to provide accurate predictions at various stages of the EA. This dataset enables robust training and evaluation of deep learning algorithms to predict AD progression. The data undergoes preprocessing and feature engineering to prepare a robust data set. Evaluations covering accuracy, precision, recall and F1 score, using k-fold cross-validation, are performed to measure the performance of the chosen algorithms. CNN and Inception V3 demonstrate remarkable performance with an accuracy of 93.75% and 94%. Comparative analyzes include exploration of Inception V3, Resnet-18, and Resnet-50, shedding light on their individual contributions to AD prediction. This paper contributes to the development of reliable and efficient deep learning approaches for early prediction and diagnosis of AD, with the potential to improve patient outcomes. Comparing the predictive capabilities of CNNs and transfer learning models provides valuable insights into their effectiveness in modeling AD progression. © 2024 IEEE.

关键词： Convolutional neural networks

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Digital twinning of vertical centrifugal casting

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Concurrent engineering Research and Applications 2024年第1-4期32卷 78-89页

作者： Anadkat, Dhaval Dave, Anjali Sata, Amit Shukla, Minal Jarboui, Slaheddine Department of Mechanical Engineering Marwadi University Rajkot India Department of Mechanical Engineering Lukhdhirji Engineering College Morbi India MGL Group Rajkot India Deparment of Computer Engineering College of Computer Science King Khalid University KSA Abha Saudi Arabia

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation. © The Author(s) 2024.

关键词： Centrifugal casting

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