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FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

computers, Materials & Continua 2021年第12期69卷 3163-3178页

作者： M.Iyapparaja M.Sathish Kumar SSiva Rama Krishnan Chiranji Lal Chowdhary Byungun Yoon Saurabh Singh Gi Hwan Cho School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Industrial and System Engineering Dongguk UniversitySeoulKorea Division of Computer Science Jeonbuk National UniversityJeonjuKorea

Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes ***,resource management in an Industry 4.0 network is very complex and *** manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network *** follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response *** experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response *** implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time *** should also be noted that the average response time increases when the arrival rate increases.

关键词： Fog computing industry 4.0 fog layer Markovian queuing model

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Design of Digital Twin Circuit Principle Experiment system Based on NCSLab

Design of Digital Twin Circuit Principle Experiment System B...

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第42届中国控制会议

作者： Yu-Zhong Li Guo-Ping Liu Yi Yu School of Computer Science and Engineering Huizhou University School of System Design and Intelligence Manufacture Southern University of Science and Technology

This paper is concerned with the requirements of current distance learning of circuit principle experiment of automation specialty,automatic fault detection of experimental equipment,automatic fault isolation,uninterrupted experiment in case of fault,and maintaining the number of available ***,based on the networked control system laboratory(NCSLab) architecture,digital twin,remote control technology and circuit design,aiming at the circuit experiment principle course,the digital twin are realized by Simulink,and a digital twin circuit principle experiment system(DT-CPES) is designed and *** designed system shows that the system can realize the sharing of experimental equipment,the digital twin can predict,and can also verify with the actual measurement data of the circuit *** the digital twin fault isolation controller(DT-FIC),the fault diagnosis,fault isolation and replacement output of the experimental equipment can be ***,the developed system reduces the overall risk and cost of installation and maintenance of large-scale circuit experiments,and provides fault-tolerant redundancy for the experimental system with low computational burden and high reliability.

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Visualization and Interpretation of Mel-Frequency Cepstral Coefficients for UAV Drone Audio Data 13

Visualization and Interpretation of Mel-Frequency Cepstral C...

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13th International Conference on Data science, Technology and Applications, DATA 2024

作者： Wang, Mia Y. Chu, Zhiwei Entzminger, Conner Ding, Yi Zhang, Qian Department of Computer Science College of Charleston CharlestonSC United States Departmemt of Computer and Information System Purdue University West LafayetteIN United States Department of Biology College of Charleston CharlestonSC United States Department of Business Administration Anhui Polytechnic University China Department of Engineering College of Charleston CharlestonSC United States

ISBN: (纸本)9789897587078

Unmanned Aerial Vehicles (UAVs) have become a focal point in various fields, prompting the need for effective detection and classification methodologies. This paper presents a thorough investigation into UAV audio signatures using Mel-Frequency Cepstral Coefficients (MFCCs). We meticulously explore the influence of varying MFCC quantities on classification accuracy across diverse UAV categories. Our analysis demonstrates that employing 30 MFCCs produces promising outcomes, characterized by reduced variance and heightened discriminatory capability compared to alternative configurations. Moreover, we introduce a novel image-based dataset derived from our existing audio dataset, encompassing waveform, spectrogram, Mel filter bank, and MFCC plots for 26 UAV categories, each comprising 100 audio files. This dataset facilitates comprehensive analysis and the development of multimodal UAV detection systems. Our research highlights the significance of leveraging diverse datasets and identifies future paths for UAV detection and classification research. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Spectrographs

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Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

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Lung Cancer Risk Prediction Features Influence Model Based on Machine Learning Techniques

Lung Cancer Risk Prediction Features Influence Model Based o...

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IEEE Region 10 International Conference TENCON

作者： Subhash Mondal Ranjan Maity Chirag Rai Souptik Pramanik Amitava Nag Computer Science and Engineering Central Institute of Technology Kokrajhar Kokrajhar India Computer Science and Bussiness System Meghnad Saha Institute of Technology Kolkata India

Currently, lung cancer is a very common form of cancer. This is because many people are chain smokers nowadays, and many are affected due to their work hazards. The pollution level in modern cities is also a major cause of this type of cancer. This model is built to predict the chances of the occurrence of lung cancer in an individual with the help of certain conditions. The acquired dataset used in this study contains multiple features, but not all are necessary for predicting the risk of lung cancer. Hence, an embedding feature importance model Light Gradient Boosting Machine (LGBM) is used to find the impact of every feature, and the model had trained using the features with maximum influence. The dataset has been divided into two parts for training and testing the model. The models achieve a k-fold mean accuracy of 97.63% and above with all the features and more than 93% on the reduced features for all the deployed models. The models are developed based on a resource-constrained device perspective over the reduced features low resource dataset and use an algorithm to measure the execution time taken for every model to run and complete its prediction after fitting with the respective classifiers. The model developed on medical data should have maximum accuracy and is necessary for time efficiency that reflects on all the deployed models with stability and efficacy, indicating the robustness and non-overfitted model.

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A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

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12th CCF Conference on BigData, BigData 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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Brain Tumour Identification Using MRI Images: Classification and Optimization Based Random-Coupled Neural Network with Cat Hunting Optimization

Brain Tumour Identification Using MRI Images: Classification...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Nandini N P. Vanajakshi Vidyarani H J Vani B A Vinod Kumar K P Shwethasree R Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Bangalore Karnataka Department of Information Science and Engineering Vivekananda Institute of Technology Bangalore Karnataka Department of Computer Science and Business System Dr. Ambedkar Institute of Technology Bangalore Karnataka Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore Karnataka Department of Computer Science and Engineering Dr Ambedkar Institute of Technology Bangalore Karnataka

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Tumors in the brains may be Benign or malignant but they are growths of cells in the brain. Aches, seizures, vomiting, dementia, and impaired vision are generic symptoms of the ailment. In early diagnosis, detection of the disease and determining the right treatment method for the patients MRI scans are important. There are a number of Deep Learning-based detection techniques available, however their accuracy and error rates may be low. This study uses a Random-Coupled Neural Network with Cat Hunting Optimization (RCNNet-CHOpt) to identify and classify brain tumor in order to get around these issues. Only one kind of BraTS dataset is used by the suggested approach: The filters to enhance image quality and reduce noise, the input images are pre-processed using the Adaptive and Propagated Mesh Filtering (APMF). This is followed by feature extraction and classification using Mellin Transform (M-Trans) with Random-Coupled Neural Network (R-CNN). When combined, these two networks can accurately classify the images and identify important elements. Finally, to increase accuracy, the weight parameter of R-CNN is modified using the Cat Hunting Optimization (CHO) technique. The proposed method has a 99.8% recall and a 99.9% accuracy rate, outperforming existing methods. Among the primary advantages of the RCNNet-CHOpt are decreased false-positive rates and enhanced precision in identifying intricate brain tumor formations. Hence, it is an especially useful tool for clinical use in the identification of brain tumor because most of these applications manage large volumes of medical data.

关键词： Accuracy Magnetic resonance imaging Noise Brain tumors Medical treatment Transforms Real-time systems Optimization Biological neural networks Monitoring

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Generation through Adapter

Sal-Guide Diffusion: Saliency Maps Guide Emotional Image Gen...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiangru Lin Shenghua Zhong Yan Liu Gong Chen College of Computer Science Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science Shenzhen University Shenzhen China Department of Computer Science The Hong Kong Polytechnic University HK China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

The existing text-to-image generation methods based on stable diffusion yield better results in low-semantic prompt but often neglect the generation quality of high-semantic prompt such as emotional vocabulary, resulting in poor emotional image generation. In order to address this issue, we propose a novel approach called Sal-Guide Diffusion, which leverages saliency maps to guide emotional image generation with the goal of producing superior emotionally expressive images. In order to let the saliency maps guide the diffusion process, we introduce a lightweight adapter to extract emotional information from saliency maps and incorporate it into the diffusion process. Experimental results demonstrate that our proposed method generates higher-quality images across eight emotional dimensions, excelling in both generalization, emotional congruence, and subjective preference compared to stable diffusion or similar methods.

关键词： Vocabulary Image synthesis Buildings Diffusion processes Text to image Data mining

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MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES

arXiv

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arXiv 2024年

作者： Xing, Jiarui Wu, Nian Bilchick, Kenneth C. Epstein, Frederick H. Zhang, Miaomiao Department of Electrical and Computer Engineering University of Virginia United States Department of Computer Science University of Virginia United States Department of Biomedical Engineering University of Virginia Health System United States School of Medicine University of Virginia Health System United States

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs. Our framework consists of two major components: (i) a DENSE-supervised strain network leveraging latent motion features learned from a registration network to predict myocardial strains;and (ii) a LMA network taking advantage of the predicted strain for effective LMA detection. Experimental results show that our proposed work substantially improves the performance of strain analysis and LMA detection from cine CMR images, aligning more closely with the achievements of DENSE. © 2024, CC BY.

关键词： Strain

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