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  • Open Access

    ARTICLE

    Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

    Feng Zhao, Guangdi Liu*, Xiaoqiang Chen, Ying Wang

    Energy Engineering, Vol.121, No.7, pp. 1865-1882, 2024, DOI:10.32604/ee.2024.048209

    Abstract In light of the prevailing issue that the existing convolutional neural network (CNN) power quality disturbance identification method can only extract single-scale features, which leads to a lack of feature information and weak anti-noise performance, a new approach for identifying power quality disturbances based on an adaptive Kalman filter (KF) and multi-scale channel attention (MS-CAM) fused convolutional neural network is suggested. Single and composite-disruption signals are generated through simulation. The adaptive maximum likelihood Kalman filter is employed for noise reduction in the initial disturbance signal, and subsequent integration of multi-scale features into the conventional CNN… More >

  • Open Access

    ARTICLE

    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

    Yue Cao1,2, Longsheng Bao1, Xiaowei Zhang1,*, Zhanfei Wang1, Bingqian Li1

    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698

    Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More > Graphic Abstract

    Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

  • Open Access

    ARTICLE

    ETV6/FLT3 Fusion Is a Novel Client Protein of Hsp90

    Bui Thi Kim Ly*, Hoang Thanh Chi†‡

    Oncology Research, Vol.26, No.8, pp. 1201-1205, 2018, DOI:10.3727/096504018X15154104709325

    Abstract FMS-like tyrosine kinase-3 fragments from exon 14 to the end without any mutations or deletions have been reported to fuse to ETV6 (TEL) in a few cases of myeloid/lymphoid neoplasms with eosinophilia carrying a translocation t(12;13)(p13;q12). This fusion protein confers constitutive activation on the FLT3 fragment and induces factor-independent growth in transfected Ba/F3 cells, indicating that it is an oncoprotein. However, the mechanism controlling the stability of this oncoprotein is unknown. In this study, we focus on finding factors controlling the stability of ETV6/FLT3. We have shown that the stability of ETV6/FLT3 is regulated by… More >

  • Open Access

    ARTICLE

    Efficacy and Safety of Transcatheter Arterial Chemoembolization and Transcatheter Arterial Chemotherapy Infusion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

    Xinyang Liu*1, Zhichao Wang*1, Zongwei Chen†1, Longzi Liu*, Lijie Ma*, Liangqing Dong*, Zhao Zhang*, Shu Zhang*, Liuxiao Yang*, Jieyi Shi*, Jia Fan*‡, Xiaoying Wang*, Qiang Gao*‡

    Oncology Research, Vol.26, No.2, pp. 231-239, 2018, DOI:10.3727/096504017X15051752095738

    Abstract Hepatocellular carcinoma (HCC) is a worldwide health threat with increasing incidence and a high mortality rate. Most HCC patients are diagnosed at an advanced stage and are unable to undergo potential curative surgery. Transcatheter arterial chemoembolization (TACE) and transcatheter arterial chemotherapy infusion (TACI) are two of the main palliative treatments for advanced HCC patients. The clinical efficacy and safety of TACE and TACI are controversial. For this reason, we conducted a systematic review and meta-analysis to summarize the current evidence. We searched for randomized controlled trials (RCTs) and cohort studies that compared the clinical outcomes… More >

  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    Elevating Image Steganography: A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

    Muhammad Zaman Ali1, Omer Riaz1, Hafiz Muhammad Hasnain2, Waqas Sharif2, Tenvir Ali2, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2923-2943, 2024, DOI:10.32604/cmc.2024.049139

    Abstract In today’s rapidly evolving landscape of communication technologies, ensuring the secure delivery of sensitive data has become an essential priority. To overcome these difficulties, different steganography and data encryption methods have been proposed by researchers to secure communications. Most of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB substitution. In this work, we have an approach that utilizes a combination of Most Significant Bit (MSB) matching and Least Significant Bit (LSB) substitution. The proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of… More >

  • Open Access

    ARTICLE

    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

  • Open Access

    ARTICLE

    FusionNN: A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

    Li Wang1,2,3,*, Mingshan Xia1,2,*, Hao Hu1, Jianfang Li1,2, Fengyao Hou1,2, Gang Chen1,2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2991-3006, 2024, DOI:10.32604/cmc.2024.048637

    Abstract With the rapid development of the mobile communication and the Internet, the previous web anomaly detection and identification models were built relying on security experts’ empirical knowledge and attack features. Although this approach can achieve higher detection performance, it requires huge human labor and resources to maintain the feature library. In contrast, semantic feature engineering can dynamically discover new semantic features and optimize feature selection by automatically analyzing the semantic information contained in the data itself, thus reducing dependence on prior knowledge. However, current semantic features still have the problem of semantic expression singularity, as… More >

  • Open Access

    ARTICLE

    Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

    Fei Wang, Zhen Dong*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2315-2329, 2024, DOI:10.32604/cmc.2024.048443

    Abstract Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models, SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model. The dataset is first preprocessed using solo thermal encoding and normalization functions. Then the spiral convolution-Long Short-Term Memory Network model is constructed, which consists of spiral convolution, a two-layer long short-term memory network, and a classifier. It is shown through experiments that the model is characterized by high accuracy, small model computation, and fast convergence speed relative to previous deep learning models. The model More >

  • Open Access

    ARTICLE

    Enhancing Deep Learning Semantics: The Diffusion Sampling and Label-Driven Co-Attention Approach

    Chunhua Wang1,2, Wenqian Shang1,2,*, Tong Yi3,*, Haibin Zhu4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1939-1956, 2024, DOI:10.32604/cmc.2024.048135

    Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and More >

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