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Analysis and development of smart production and distribution line system in smart grid based on optimization techniques involving digital twin

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Measurement: Sensors 2024年 34卷

作者： Arumugam, Thangaraja Kamble, Nitin Kundlik Guntreddi, Venkataramana Vishnu Sakravarthy, N. Shanthi, S. Ponnusamy, Sivakumar Business School Vellore Institute of Technology Tamilnadu Chennai India Department of Robotics and Automation Engineering D Y Patil College of Engineering Akurdi Maharashtra Pune411044 India Department of Electrical Telecommunication and Computer Engineering School of Engineering and Applied Sciences Kampala International University Isakha Uganda Department of Mechanical Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India School of Computer Science & Engineering Presidency University Karnataka Bengaluru560064 India Department of Computer Science and Engineering K.S.R. College of Engineering Tiruchengode TamilNadu Namakkal India

The term Digital Twin (DT) is defined as the virtual demonstration of an object that is represented through real-time datasets. DT is done through artificial intelligence to enhance decision-making techniques. DT includes the process of simulation, amalgamation, observation, analysis, and conservation. The DT is simply the exact reproduction of the physical structures. DT is used in the identification and evaluation of problems through real-time analysis. It is important to have prior analysis and evaluation of the object before existing in the real world. These digital twins help in the manufacturing and implementation of the production line system. DT includes the production line with the station division and the hours needed for the operating conditions for the assembly process. The systems are integrated to reduce the overall cost parameter. The physical simulation model is employed to obtain higher performance with reduced cost. An artificial neural network with a genetic algorithm is used for the optimization process to achieve a production line system using digital twins. © 2024 The Authors

关键词： Genetic algorithms

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IoT in Health Care Study by Text Analysis 2

IoT in Health Care Study by Text Analysis

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Dhumal, Yashomati R Gobinath, V.M. Jangirala, Srinivas Vidhya, M. Prakash, A. Shinde, Arundhati College of Engineering Electronics Department Pune India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Chengalpattu India O. P. Jindal Global University Jindal Global Business School Sonipat India Panimalar Institute of Technology Department of Computer Science and Engineering Varadarajapuram India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India College of Engineering Electronics Department Pune India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The Internet of Things (IoT) should be a new buzzword that emerged little by little (IoT). Organizations, governments, and the scientific community have already understood the relevance of IoT in the past few years, with a growing number of publications. A key component of this progress was IoT in health, but little research has been conducted on aspirations and potential developments. Only as the solution, they integrate bibliometric analysis and data mining to evaluate the longitudinal patterns of the IoT devices to Medical in this study. From 2000 to 2021, 778 articles were derived from the Web of science. Abstract textual data indicate that articles were divided into 30 groups, revealing eight IoT in health trends. The development of this field is worldwide, with distinctive patterns that should be noted to promote some areas. © 2022 IEEE.

关键词： Internet of things

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Deep Learning Approaches for Detecting and Predicting Knee Osteoarthritis Severity

Deep Learning Approaches for Detecting and Predicting Knee O...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Shilpa Karegoudra Veeresha R K Vamsidhar Yendapalli Department of Computer Science and Engineering NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Udupi India Department of Robotics and Artificial Intelligence NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) Udupi India Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Knee osteoarthritis stands as one of the most prevalent musculoskeletal ailments globally. Its initial signs include joint stiffness, discomfort, and limitations in functionality, marking it as a significant joint disorder. Older adults are more likely to have arthritis, and it normally gets worse with age. If there are no early, effective therapies used, it can lead to impairment in older people. Therapy must be started as soon as possible; otherwise, a full knee replacement may be necessary. The very subjective nature of the standard procedures creates a barrier to early illness progression detection. This study involves in-depth exploration and examination of the application of deep learning techniques for knee osteoarthritis detection and severity prediction. In this work EfficientNet-B6 was chosen for its superior performance in balancing accuracy, speed, and resource efficiency. Unlike models such as MobileNet or ResNet, X-ray images are used to train the model to predict the severity of knee osteoarthritis. The model classifies the data with an accuracy of 87%. A website for OA prediction is developed, the user needs to drag and drop an X-ray image in the dialog box given. The predicted output along with the severity will be shown within a few seconds. This website is very easy to use.

关键词： Deep learning Accuracy Medical treatment Predictive models Planning Osteoarthritis Sustainable development Older adults X-ray imaging Standards

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Design of Volumetric Magnetic Induction Tomography (VMIT) System Using COMSOL Simulation

Design of Volumetric Magnetic Induction Tomography (VMIT) Sy...

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International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET)

作者： Muhammad Nurul Puji Prawito Prajitno Warsito Purwo Taruno Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electronic and Instrumentation Physics of The Department of Physics Universitas Indonesia Depok Indonesia C-TECH LABS Edwar Technology Tangerang Indonesia

ISBN: (数字)9798350389920

ISBN: (纸本)9798350389937

Magnetic Induction Tomography (MIT) is a low-resolution imaging technique used to reconstruct the magnetic permeability distribution of an object. COMSOL Simulation of VMIT (Volumetric Magnetic Induction Tomography) system has been developed by using 8 coils as transmitter and 8 coils as receiver. The existence of sine excitation signal in the transmitter coil cause magnetic field changing, it causing emf at the receiver coil. The magnitude of the magnetic field induction is also influenced by the permeability of the medium between the transmitter and receiver coils. COMSOL and MATLAB simulation of VMIT 8 coil systems indicate that, by using ILBP algorithms, ball-shaped object with ferromagnetic materials (iron) can be reconstructed to produce a similar image. The sensitivity matrix significantly influences image reconstruction in MIT, leading to distorted elliptical images of objects, particularly when positioned at the top center, as the matrix shows homogeneity mainly in the middle area.

关键词： Coils Microwave antennas Transmitters Magnetic resonance imaging Receivers Tomography Radar antennas Permeability Magnetic fields Image reconstruction

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Happy: a debiased learning framework for continual generalized category discovery 24

Happy: a debiased learning framework for continual generaliz...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shijie Ma Fei Zhu Zhun Zhong Wenzhuo Liu Xu-Yao Zhang Cheng-Lin Liu MAIS Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China Centre for Artificial Intelligence and Robotics HKISI-CAS China School of Computer Science and Information Engineering Hefei University of Technology China and School of Computer Science University of Nottingham NG8 1BB Nottingham UK

ISBN: (纸本)9798331314385

Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while maintaining the ability to recognize previously learned classes. Although several settings are proposed to study the C-GCD task, they have limitations that do not reflect real-world scenarios. We thus study a more practical C-GCD setting, which includes more new classes to be discovered over a longer period, without storing samples of past classes. In C-GCD, the model is initially trained on labeled data of known classes, followed by multiple incremental stages where the model is fed with unlabeled data containing both old and new classes. The core challenge involves two conflicting objectives: discover new classes and prevent forgetting old ones. We delve into the conflicts and identify that models are susceptible to prediction bias and hardness bias. To address these issues, we introduce a debiased learning framework, namely Happy, characterized by Hardness-aware prototype sampling and soft entropy regularization. For the prediction bias, we first introduce clustering-guided initialization to provide robust features. In addition, we propose soft entropy regularization to assign appropriate probabilities to new classes, which can significantly enhance the clustering performance of new classes. For the harness bias, we present the hardness-aware prototype sampling, which can effectively reduce the forgetting issue for previously seen classes, especially for difficult classes. Experimental results demonstrate our method proficiently manages the conflicts of C-GCD and achieves remarkable performance across various datasets, e.g., 7.5% overall gains on ImageNet-100. Our code is publicly available at https://***/mashijie1028/Happy-CGCD.

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Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

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International Conference on computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

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Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

arXiv

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arXiv 2024年

作者： Lin, Junxiong Tao, Zen Tong, Xuan Mai, Xinji Wang, Haoran Wang, Boyang Wang, Yan Zhao, Qing Yu, Jiawen Lin, Yuxuan Yan, Shaoqi Gao, Shuyong Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China East China University of Science and Technology Shanghai China Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explicit modeling approach struggles to cover the complex and varied degradation processes encountered in the real world, such as high-order combinations of JPEG compression, blur, and noise. Implicit modeling for the degradation process can effectively overcome this issue, but a key challenge of implicit modeling is the lack of accurate ground truth labels for the degradation process to conduct supervised training. To overcome this limitations inherent in implicit modeling, we propose an Uncertainty-based degradation representation for blind Super-Resolution framework (USR). By suppressing the uncertainty of local degradation representations in images, USR facilitated self-supervised learning of degradation representations. The USR consists of two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a feature extraction network composed of Variable Depth Dynamic Convolution (VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with Uncertainty Suppression Loss to suppress the inherent model uncertainty of the Degradation Extractor. Furthermore, VDDC block integrates degradation information through dynamic convolution. Rhe VDDC also employs an Adaptive Intensity Scaling operation that adaptively adjusts the degradation representation according to the network hierarchy, thereby facilitating the effective integration of degradation information. Quantitative and qualitative experiments affirm the superiority of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor Systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Hyperspectral Remote Sensing Scene Classification with Spectral-Spatial Convolutional Network

Hyperspectral Remote Sensing Scene Classification with Spect...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jialin Zheng Zhen Li Puhong Duan Xudong Kang Wei Fu College of Electrical and Information Engineering Hunan University China College of Computer Science and Technology (CCST) Zhejiang University China Institute of Remote Sensing Satellite China Academy of Space Technology (CAST) China School of Robotics Hunan University China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Remote sensing scene classification has garnered significant interest. However, few studies are dedicated to the classifi-cation of hyperspectral remote sensing scenes. Furthermore, current approaches that are designed for multispectral or visible images are not suitable for hyperspectral remote sensing scene classification. To solve these issues, a spectral-spatial convolutional network is proposed for hyperspectral remote sensing scene classification. First, a multiscale regional growth search method is developed to extract object region of the input data. Then, a three-branch network is designed to extract local spatial, global spatial, and spectral features. Finally, the obtained features are fed into fully connected layer to obtain class scores followed by a decision fusion rule to yield the final classification result. To evaluate the effect of hyperspectral remote sensing scene classification, a hyper-spectral remote sensing scene classification dataset (HRSS-C) is constructed, which consists of 1445 hyperspectral images with 11 different classes. Experiments on the HRSS-C reveal that our method gains superior classification performance with respect to other advanced approaches.

关键词： Training Scene classification Databases Feature extraction Search problems Convolutional neural networks Data mining

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Incentive Mechanism Design for Distributed Ensemble Learning

Incentive Mechanism Design for Distributed Ensemble Learning

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Huang Pengchao Han Jianwei Huang Department of Computer Science University of California Davis USA School of Information Engineering Guangdong University of Technology China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Distributed ensemble learning (DEL) involves training multiple models at distributed learners, and then combining their predictions to improve performance. Existing related studies focus on algorithm development but ignore the important issue of incentives, without which self-interested learners may be unwilling to participate. We aim to fill this gap by presenting a first study on the incentive mechanism design in DEL. The mechanism specifies both the training data and the reward for learners with heterogeneous computation and communication costs. One challenge is that it is unclear how learners' diversity (in terms of training data) contributes to the ensemble accuracy. To this end, we decompose the ensemble accuracy into a diversity-precision tradeoff to guide the mechanism design. Another challenge is that the mechanism design is a mixed-integer program with a large search space. To this end, we propose an alternating algorithm that iteratively updates each learner's training data size and reward. We prove that the algorithm converges and is polynomial in the number of learners. Numerical results using MNIST dataset are consistent with our analysis. Interestingly, we show that the mechanism may prefer a lower level of learner diversity to achieve a higher ensemble accuracy. Our code is made publicly available.

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