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ImPINN: Improved Physics-informed neural networks for solving inverse problems 16

ImPINN: Improved Physics-informed neural networks for solvin...

引用

16th international conference on Cyber-Enabled Distributed computing and Knowledge Discover, CyberC 2024

作者： Bai, Yidi Chen, Xinhai Gong, Chunye Liu, Jie National University of Defense Technology Computer Department Changsha China

ISBN: (纸本)9798331506896

Physics-informed learning methods have gained significant attention as a function approximator for solving partial differential equation problems. However, the vanilla PINN tends to provide inaccurate solutions when solving inverse problems of partial differential equations with characteristics such as strong nonlinearity. To address this problem, we introduce a new approach, the Improved Physics-informed Neural Network (Im-PINN), which combines improvement strategies to enhance the convergence and accuracy of the vanilla PINN method for solving inverse problems. The ImPINN method adopts adaptive ideas in the activation function, loss function and sampling strategy. These strategies have significantly increased the efficiency and accuracy of the vanilla PINN method during the feature fitting process. Numerical experiments demonstrate that the improved PINN performs better on Burgers, Allen-Cahn, Korteweg-de Vries, heat transfer and Navier-Stokes equations and reduces the relative error of the unknown parameters by up to about two orders of magnitude. © 2024 IEEE.

关键词： Navier Stokes equations

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Drainage Pipeline Defect Detection Based on Improved YOLOX 3

Drainage Pipeline Defect Detection Based on Improved YOLOX

引用

3rd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2024

作者： Yang, Zhiwang Weng, Han Sun, Yuan Chen, Xiaojin Guan, Jiansheng School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China

ISBN: (纸本)9798331506582

Aiming at the problems of low automation and long detection cycle of urban drainage pipeline defect detection tasksan improved YOLOX-s object detection algorithm was proposed. Based on YOLOX-s, the MobileNetV3 network was used to replace the CSPDarknet53 feature extraction network to achieve lightweight improvement. Introduced SPPF(Spatial Pyramid Pooling Fast) structure to fuse local and global features from the outputs of three different layers of the MobileNetV3 feature extraction network, enhanced the model's discriminative ability for distinguishing objects from similar backgrounds. The CIoU(Complete-IoU) loss function was used to solve the lossless gradient backpropagation problem that existed when the prediction box and the ground truth box did not overlap during the training phase. It sped up the model convergence speed and optimized the fitting effect of the prediction box and the ground truth box. Experiments are conducted on the urban drainage pipe defect detection dataset. The results show that the improved YOLOX-s model reduces the calculation amount by 37.09%, the parameter amount by 39.13%, increases the detection speed by 29.12%, and increases the mAP(mean average precision) by 0.88%. While ensuring high detection accuracy, it significantly enhances the speed of model detection. © 2024 IEEE.

关键词： Image segmentation

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Research on graphite ore grade classification based on the integration of multi-level features from ResNet and Transformer

Research on graphite ore grade classification based on the i...

引用

2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Shu, Wei Wang, Jionghui Huang, Xueyu Yichun Lithium New Energy Industry Research Institute Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China Minmetals Exploration & Development Co.Ltd Beijing100010 China

ISBN: (纸本)9781510685840

Traditional graphite ore production typically relies on carbon-sulfur analyzers to determine the carbon grade of graphite ore. This method, however, is cumbersome and lacks timeliness. To address these issues, we propose TRA-FFNet, a graphite carbon grade image recognition and classification model that integrates multi-level features from ResNet and Transformer. Initially, ResNet-50 is employed as the backbone feature extraction network, with model parameters initialized through transfer learning to accelerate convergence. Subsequently, a Transformer module based on spatial feature compression fusion is designed to capture the weight relation-ships among different channels of the graphite ore feature map. Finally, a multi-level feature fusion module is incorporated at the network's terminal position to enhance the joint learning of both global and local features in graphite ore images. Experimental results show that the pro-posed model achieves an accuracy of 93.473% and an F1 score of 94.023% on our self-constructed dataset containing 19 classes of graphite ore, outperforming classic models such as MobileNetv2, EfficientNet-b3, ConvNext, RepVgg, and Swin Transformer. The proposed model achieves high-precision recognition of graphite ore grade in an end-to-end manner, offering a valuable detection solution for smart mining operations. © 2024 SPIE.

关键词： Photons

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Adaptive Decision Feedback Equalization for Underwater Acoustic OTFS System with Superposition Pilot 14

Adaptive Decision Feedback Equalization for Underwater Acous...

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14th IEEE international conference on Signal Processing, Communications and computing, ICSPCC 2024

作者： Zhang, Xuewei Jing, Lianyou Shi, Wentao Zhang, Xingliang He, Chengbing School of Information and Communication Engineering Dalian University of Technology Dalian China Ocean Institute Northwestern Polytechnical University Taicang China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350366556

Orthogonal time-frequency-space (OTFS) modulation transforms the time-varying channel into the delay-Doppler domain, and the fading coefficient of the channel in the delay-Doppler domain is near constant. OTFS modulation realizes the quantized representation of the channel in the delay dimension and the Doppler dimension, and the system has the good performance of anti-multipath effect and anti-Doppler expansion. Therefore, we extend the two-dimensional decision feedback equalization (2D-DFE) to the OTFS system, and propose a 2D adaptive multichannel DFE for OTFS system with superposition pilot (SP). Based on the superior performance of the algorithm, this paper applies the method to underwater acoustic (UWA) communications to deal with UWA channel with large delay and large Doppler. At the same time, the sparse nature of the delay-Doppler domain channel is exploited to achieve fast convergence and fast tracking using the normalized least mean square (NLMS) algorithm. Simulation results show that the proposed equalizer could achieve more satisfactory BER performance and improve the band utilization. © 2024 IEEE.

关键词： Decision feedback equalizers

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Learning-Based Latency and Energy Optimization in SCMA-Enhanced UAV-MEC Networks

Learning-Based Latency and Energy Optimization in SCMA-Enhan...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Liu, Pengtao Lei, Jing Liu, Wei National University of Defense Technology College of Electronic Science and Technology Changsha410073 China

ISBN: (纸本)9798350345384

Sparse code multiple access (SCMA) technology can provide massive connectivity and timely computing in unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) networks. In this paper, the long-term task latency and energy consumption minimization problem in SCMA-enhanced UAV-MEC networks is investigated. We first formulate it as a Markov decision process (MDP) under a dynamic environment and then propose a joint computation offloading, SCMA resource allocation, and UAV trajectory algorithm based on convolutional neural network (CNN) and deep deterministic policy gradient (DDPG). Specifically, the UAV is taken as an agent to extract channel and task features of multiple devices through CNN for action exploration. The weighted sum of task latency and energy consumption is used as a reward with a penalty for failing to complete the task within the deadline or when the UAV battery runs out. Eventually, the near-optimal strategy is obtained through experience training and interaction with the environment. Simulation results illustrate that the proposed algorithm can achieve convergence and has greater advantages over other benchmark algorithms. © 2023 IEEE.

关键词： Mobile edge computing

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Crawling Gait Enables Stable Quadrupedal Locomotion in A Bioinspired Robotic Rat

Crawling Gait Enables Stable Quadrupedal Locomotion in A Bio...

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2024 IEEE international conference on Real-Time computing and Robotics, RCAR 2024

作者： Wang, Ruochao Quan, Xiaolong Xiao, Hang Du, Rongjie Qin, Boyang Shi, Qing Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China

ISBN: (纸本)9798350372601

Constrained by the performance of actuators and controllers, achieving stable walking in small-scale quadruped robots has been more challenging compared to larger ones. Rats, in their natural habitat, can achieve stable walking even in extreme environments like narrow spaces by utilizing crawling motion to lower their center of mass. Inspired by this, we conducted a comparative study of the walking stability between crawling and trot gaits on a ratinspired robot named SQuRo. Initially, we collected motion data for both crawling and trot gaits of SQuRo in a simulated environment. Subsequently, we analyzed the dynamic stability of the robot using Poincaré maps. The results indicate that SQuRo's phase trajectories converged into closed loops with periodicity under both gaits, demonstrating the presence of limit cycles. Moreover, fixed points were observed in the Poincaré maps for both gaits, suggesting the dynamic stability of quadrupedal locomotion. Compared to the trot gait, the convergence loops of the crawling gait phase trajectories were smaller, indicating more stable locomotion. We tested the performance of SQuRo's crawling gait in challenging outdoor environments, where SQuRo demonstrated stable walking consistently. This work provides a new approach for achieving stable walking in small-scale quadruped robots and can be readily applied to controller design across different sizes of legged robots. © 2024 IEEE.

关键词： Rats

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A Multi - User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach 25

A Multi - User Effective Computation Offloading Mechanism fo...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Li, Hangfan Shi, Lin Zhong, Xiaoxiong Wang, Xinghan Ji, Yun Zhang, Sheng Guilin University of Electronic Technology Guilin541004 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9798350330014

With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within MEC system could save plenty of time on data transmission. Algorithms within Multi-armed bandits (MAB) framework are powerful tools helping users offloading tasks to their best servers. However, as the system scale growing, servers will face higher pressure and users will waste more time on offloading tasks. The situation by applying traditional MAB algorithms is more serious. Therefore, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) to solve such problem. BMSE contains two sub-algorithms, BMSE in User Level (BMSE-UL) and BMSE in System Level (BMSE-SL). BMSE- UL is applied by users for initial purpose which will help themselves discard servers with obvious bad performance. BMSE-SL is applied in the MEC system which could offload tasks from different users collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness by extensive experiments. © 2023 IEEE.

关键词： Mobile edge computing

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Enhancement of Security in Cloud computing Using Optimal Risk Access Control Model

Enhancement of Security in Cloud Computing Using Optimal Ris...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Arunarani, A.R. Sherin Shibi, C. Kanimozhi, N. Sumathy, G. Maheshwari, A. SRM Institute of Science and Technolgy Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur603203 India

ISBN: (纸本)9798350319200

This paper develops an Optimal Risk Access Control Model (ORACM) for enhancing security in cloud computing. In this proposed model, three important risk aspects are considered such as environment attribute, resource attribute and subject attribute. Based on these factors, the suggested risk validation indexes were created, and by fusing Adaptive Snake Optimization (ASO) and Policy Approach Access Control (PAAC), the risk validation computation method would be quantitatively validated. In the PAAC, the optimization method is taken into account while choosing the decision. Based on the validation, optimization and choosing the best risk decision is reducing waiting time. conventional access control continues to receive access needs as extended as they are genuine once the architecture is in an emergency state (like when the CPU and memory remain saturated), raising the danger of a system crash. Consider a case where the system is presently in an emergency, on the other hand. The authentication token may be misused, leading to the unauthorized access of system resources and services, if the security of the primary environment depreciates. The suggested strategy is put into practice, and its effectiveness is assessed by taking into account performance indicators including waiting time, convergence analysis, and CPU usage. It is also contrasted with traditional approaches. © 2023 IEEE.

关键词： adaptive snake optimization and convergence analysis policy approach access control risk access control method

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Data-driven classification model of lightning generated LF/VLF radiation electric field waveforms 37

Data-driven classification model of lightning generated LF/V...

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37th international conference on Lightning Protection, ICLP 2024

作者： Lilang, Xiao Weijiang, Chen Yu, Wang Kai, Bian Zhong, Fu Nianwen, Xiang Hengxin, He Yang, Cheng Huazhong University of Science and Technology Wuhan China State Grid Corporation of China Beijing China Wuhan NARI Co. Ltd of State Grid Electric Power Research Institute Wuhan China Electric Power Research Institute State Grid Anhui Electric Power Company Hefei China Hefei University of Technology School of Electrical Engineering and Automation Hefei China

ISBN: (纸本)9783800764174

A convolutional neural network (CNN) model is proposed for the classification of very low frequency and low frequency (VLF/LF) lightning electric field waveforms. This model adopts multi-scale convolutional kernels and shortcut connections to enhance the ability of lightning waveform classification. Based on the data recorded from five provinces in China, the proposed model achieves an accuracy of 99.6% for a four-type classification task including return strokes (RS), the intra-cloud (IC) lightning, preliminary breakdown (PB), and narrow bipolar events (NBE). Compared with classic machine learning methods and deep learning methods, the proposed model performs better in classification accuracy and convergence speed. Using the knowledge distillation method, a model suitable for low-computing-power platforms is obtained. The distilled model takes only 59ms for single classification and has a classification accuracy of 99.0%, demonstrating reliable application of the proposed model on low-computing-power platforms. © 2024 IEEE, © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Convolutional neural networks

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Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions 7

Deep Learning Approaches for Signal and Image Processing: St...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Deepan, S. Gera, Jaideep Pareek, Anupam Chinchmalatpure, Sheela Nayak, Gouri Sankar Bodapati, Jagadeesh Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Guntur522212 India Poornima University Faculty of Management of Commerce Ramchandrapura P.O. Vidhani Vatika Sitapura Extension Rajasthan Jaipur303905 India Vishwakarma Institute of Technology Maharashtra Pune India Vignan's Institute Of Information Technology Duvvada Andhra Pradesh Visakhapatnam530049 India Rajamundry India

ISBN: (纸本)9798350350067

Deep learning has revolutionized signal and image processing by enabling the creation of complex algorithms with many applications. This study examines deep learning signal and image processing optimization and hardware acceleration strategies. Experimentally evaluating stochastic gradient descent (SGD) and Adam optimization algorithms determines their convergence speed and effectiveness. We also examine how GPUs may accelerate model execution and deep learning inference. Our study reveals that deep learning optimization methodologies and platforms provide several practical challenges and trade-offs. This work may help signal and image processing researchers and practitioners design scalable and efficient solutions. Additionally, their methods illuminate deep learning. © 2024 IEEE.

关键词： Deep learning

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