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  • Open Access

    ARTICLE

    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

    Shuai Chen1, Yinwei Ma2, Zhongshu Wang2, Zongmei Xu3, Song Zhang1, Jianle Li1, Hao Xu1, Zhanjun Wu1,*

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 125-141, 2024, DOI:10.32604/sdhm.2024.042594

    Abstract The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life. To this end, distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages, such as lightweight and ease of embedding. However, identifying the precise location of damage from the optical fiber signals remains a critical challenge. In this paper, a novel approach which namely Modified Sliding Window Principal Component Analysis (MSWPCA) was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors. The proposed method is able to extract signal… More > Graphic Abstract

    A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

  • Open Access

    ARTICLE

    Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

    HONGMIN CAO1,#,*, YING XUE2,#, FEI WANG1, GUANGYAO LI1, YULAN ZHEN1, JINGWEN GUO1

    BIOCELL, Vol.48, No.3, pp. 473-490, 2024, DOI:10.32604/biocell.2024.048946

    Abstract Background: Cytotoxic T lymphocytes (CD8+ T) cells function critically in mediating anti-tumor immune response in cancer patients. Characterizing the specific functions of CD8+ T cells in lung adenocarcinoma (LUAD) could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy. Methods: Gens related to CD8+ T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues. Weighted gene co-expression network analysis (WGCNA), consensus clustering, differential expression analysis, least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were conducted to classify molecular subtypes for LUAD… More > Graphic Abstract

    Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

  • Open Access

    ARTICLE

    Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

    Arpit Jain1, Nageswara Rao Moparthi1, A. Swathi2, Yogesh Kumar Sharma1, Nitin Mittal3, Ahmed Alhussen4, Zamil S. Alzamil5,*, MohdAnul Haq5

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 341-362, 2024, DOI:10.32604/csse.2023.036973

    Abstract Recently, the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy. World Health Organization (WHO) and many others advised controlling Corona Virus Disease in 2019. The limited treatment resources, medical resources, and unawareness of immunity is an essential horizon to unfold. Among all resources, wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) droplets. All countries made masks mandatory to prevent infection. For such enforcement, automatic and effective face detection systems are crucial. This study presents a face… More >

  • Open Access

    ARTICLE

    PCA-LSTM: An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning

    Yizhao Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

    Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related to impulsive ground-shaking. Thirdly, a… More >

  • Open Access

    ARTICLE

    Transcriptome-Wide Identification and Functional Analysis of PgSQE08-01 Gene in Ginsenoside Biosynthesis in Panax ginseng C. A. Mey.

    Lei Zhu1,#, Lihe Hou1,3,#, Yu Zhang1, Yang Jiang1, Yi Wang1,2, Meiping Zhang1,2, Mingzhu Zhao1,2,*, Kangyu Wang1,2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 313-327, 2024, DOI:10.32604/phyton.2024.047938

    Abstract Panax ginseng C. A. Mey. is an important plant species used in traditional Chinese medicine, whose primary active ingredient is a ginsenoside. Ginsenoside biosynthesis is not only regulated by transcription factors but also controlled by a variety of structural genes. Nonetheless, the molecular mechanism underlying ginsenoside biosynthesis has always been a topic in the discussion of ginseng secondary metabolites. Squalene epoxidase (SQE) is a key enzyme in the mevalonic acid pathway, which affects the biosynthesis of secondary metabolites such as terpenoid. Using ginseng transcriptome, expression, and ginsenoside content databases, this study employed bioinformatic methods to systematically analyze the genes encoding… More >

  • Open Access

    ARTICLE

    Identification of Important FPGA Modules Based on Complex Network

    Senjie Zhang1,2, Jinbo Wang2,*, Shan Zhou2, Jingpei Wang2,3, Zhenyong Zhang4,*, Ruixue Wang2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1027-1047, 2024, DOI:10.32604/cmc.2023.046355

    Abstract The globalization of hardware designs and supply chains, as well as the integration of third-party intellectual property (IP) cores, has led to an increased focus from malicious attackers on computing hardware. However, existing defense or detection approaches often require additional circuitry to perform security verification, and are thus constrained by time and resource limitations. Considering the scale of actual engineering tasks and tight project schedules, it is usually difficult to implement designs for all modules in field programmable gate array (FPGA) circuits. Some studies have pointed out that the failure of key modules tends to cause greater damage to the… More >

  • Open Access

    ARTICLE

    A New Encrypted Traffic Identification Model Based on VAE-LSTM-DRN

    Haizhen Wang1,2,*, Jinying Yan1,*, Na Jia1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055

    Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the variational autoencoder (VAE) is combined… More >

  • Open Access

    REVIEW

    A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

    Xiaozhong Yu1,2,*, Jinhua Zheng1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 197-225, 2024, DOI:10.32604/cmc.2023.043943

    Abstract In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to the network structure differences; and… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

    S. Sofana Reka1, Ankita Bagelikar2, Prakash Venugopal2,*, V. Ravi2, Harimurugan Devarajan3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 781-794, 2024, DOI:10.32604/cmc.2023.043369

    Abstract The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality, flavor and nutritional value. The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers. The impact of rotten fruits can foster harmful bacteria, molds and other microorganisms that can cause food poisoning and other illnesses to the consumers. The overall purpose of the study is to classify rotten fruits, which can affect the taste, texture, and appearance of other fresh fruits, thereby reducing their shelf life. The agriculture and food industries… More >

  • Open Access

    ARTICLE

    Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

    Baixuan Tang1,2,#, Xiaolong Xu1,2,#, Fei Dai3, Song Wang4,*

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 35-55, 2023, DOI:10.32604/iasc.2023.036715

    Abstract Person re-identification (ReID) aims to recognize the same person in multiple images from different camera views. Training person ReID models are time-consuming and resource-intensive; thus, cloud computing is an appropriate model training solution. However, the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments, leading to significant communication overheads. This paper proposes a federated person ReID method with model-contrastive learning (MOON) in an edge-cloud environment, named FRM. Specifically, based on federated partial averaging, MOON warmup is added to correct the local training of individual edge servers and improve the model’s… More >

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