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  • Open Access

    ARTICLE

    Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

    Lu Wei, Zhong Ma*, Chaojie Yang

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 981-1000, 2024, DOI:10.32604/cmes.2023.027085

    Abstract The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing. Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices. In order to reduce the complexity and overhead of deploying neural networks on Integer-only hardware, most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network. However, although symmetric quantization has the advantage of easier implementation, it is sub-optimal for cases where the range could be skewed and not symmetric. This often comes at the cost of lower accuracy. This… More > Graphic Abstract

    Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

  • Open Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga whale optimization (BWO) algorithm is… More > Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access

    EDITORIAL

    Grad-CAM: Understanding AI Models

    Shuihua Wang1,2, Yudong Zhang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1321-1324, 2023, DOI:10.32604/cmc.2023.041419

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

    Asif Rahim1, Yanru Zhong2, Tariq Ahmad3,*, Sadique Ahmad4,*, Mohammed A. ElAffendi4

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1947-1976, 2023, DOI:10.32604/cmc.2023.039340

    Abstract Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and… More >

  • Open Access

    ARTICLE

    A Time-Varying Parameter Estimation Method for Physiological Models Based on Physical Information Neural Networks

    Jiepeng Yao1,2, Zhanjia Peng1,2, Jingjing Liu1,2, Chengxiao Fan1,2, Zhongyi Wang1,2,3, Lan Huang1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101

    Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this paper, taking the electrophysiological differential… More >

  • Open Access

    ARTICLE

    A Robust Approach for Detection and Classification of KOA Based on BILSTM Network

    Abdul Qadir1, Rabbia Mahum1, Suliman Aladhadh2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1365-1384, 2023, DOI:10.32604/csse.2023.037033

    Abstract A considerable portion of the population now experiences osteoarthritis of the knee, spine, and hip due to lifestyle changes. Therefore, early treatment, recognition and prevention are essential to reduce damage; nevertheless, this time-consuming activity necessitates a variety of tests and in-depth analysis by physicians. To overcome the existing challenges in the early detection of Knee Osteoarthritis (KOA), an effective automated technique, prompt recognition, and correct categorization are required. This work suggests a method based on an improved deep learning algorithm that makes use of data from the knee images after segmentation to detect KOA and its severity using the Kellgren-Lawrence… More >

  • Open Access

    REVIEW

    Recent Advances of Deep Learning in Geological Hazard Forecasting

    Jiaqi Wang1, Pengfei Sun1, Leilei Chen2, Jianfeng Yang3, Zhenghe Liu1, Haojie Lian1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1381-1418, 2023, DOI:10.32604/cmes.2023.023693

    Abstract Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. More >

  • Open Access

    REVIEW

    Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

    Loc Vu-Quoc1,*, Alexander Humer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

    Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional… More >

  • Open Access

    ARTICLE

    TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation

    Peng Geng1, Ji Lu1, Ying Zhang2,*, Simin Ma1, Zhanzhong Tang2, Jianhua Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127

    Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions can provide position information is… More >

  • Open Access

    ARTICLE

    A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

    Bin Li*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649

    Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence plots (PDP) indicate that… More >

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