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  • Open Access

    REVIEW

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

    Yanjun Yan, Kai Chen*, Hang Geng, Wenqian Fan, Xinrui Zhou

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1345-1379, 2023, DOI:10.32604/cmes.2023.027252

    Abstract With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the… More > Graphic Abstract

    A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

  • Open Access

    ARTICLE

    Pure Detail Feature Extraction Network for Visible-Infrared Re-Identification

    Jiaao Cui1, Sixian Chan1,2,*, Pan Mu1, Tinglong Tang2, Xiaolong Zhou3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2263-2277, 2023, DOI:10.32604/iasc.2023.039894

    Abstract Cross-modality pedestrian re-identification has important applications in the field of surveillance. Due to variations in posture, camera perspective, and camera modality, some salient pedestrian features are difficult to provide effective retrieval cues. Therefore, it becomes a challenge to design an effective strategy to extract more discriminative pedestrian detail. Although many effective methods for detailed feature extraction are proposed, there are still some shortcomings in filtering background and modality noise. To further purify the features, a pure detail feature extraction network (PDFENet) is proposed for VI-ReID. PDFENet includes three modules, adaptive detail mask generation module (ADMG), inter-detail interaction module (IDI) and… More >

  • Open Access

    ARTICLE

    A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

    Ali Ahmed1,*, Alaa Omran Almagrabi2, Omar M. Barukab3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2355-2370, 2023, DOI:10.32604/iasc.2023.039847

    Abstract Content-based medical image retrieval (CBMIR) is a technique for retrieving medical images based on automatically derived image features. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. Several methods are applied to enhance the retrieval performance of CBMIR systems. Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems. This study proposes the relative difference-based similarity measure (RDBSM) for CBMIR. The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional… More >

  • Open Access

    ARTICLE

    Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

    Dong-Hoon Shin1, Seo-El Lee2, Byeong-Uk Jeon1, Kyungyong Chung3,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1925-1940, 2023, DOI:10.32604/iasc.2023.039317

    Abstract Recently, the importance of data analysis has increased significantly due to the rapid data increase. In particular, vehicle communication data, considered a significant challenge in Intelligent Transportation Systems (ITS), has spatiotemporal characteristics and many missing values. High missing values in data lead to the decreased predictive performance of models. Existing missing value imputation models ignore the topology of transportation networks due to the structural connection of road networks, although physical distances are close in spatiotemporal image data. Additionally, the learning process of missing value imputation models requires complete data, but there are limitations in securing complete vehicle communication data. This… More >

  • Open Access

    ARTICLE

    Melanoma Detection Based on Hybridization of Extended Feature Space

    Anuj Kumar, Shakti Kumar*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2175-2198, 2023, DOI:10.32604/iasc.2023.039093

    Abstract Melanoma is a perfidious form of skin cancer. The study offers a hybrid framework for the automatic classification of melanoma. An Automatic Melanoma Detection System (AMDS) is used for identifying melanoma from the infected area of the skin image using image processing techniques. A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved. The research problem is to identify the best preprocessing technique, feature extractor, and classifier for melanoma detection using publically available MED-NODE data set. AMDS goes through four stages. The preprocessing stage is for noise removal; the segmentation stage… More >

  • Open Access

    ARTICLE

    Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

    Jahanzaib Latif1, Shanshan Tu1,*, Chuangbai Xiao1, Anas Bilal2, Sadaqat Ur Rehman3, Zohaib Ahmad4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

    Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

  • Open Access

    ARTICLE

    Supervised Feature Learning for Offline Writer Identification Using VLAD and Double Power Normalization

    Dawei Liang1,2,4, Meng Wu1,*, Yan Hu3

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 279-293, 2023, DOI:10.32604/cmc.2023.035279

    Abstract As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and… More >

  • Open Access

    ARTICLE

    Improved Blending Attention Mechanism in Visual Question Answering

    Siyu Lu1, Yueming Ding1, Zhengtong Yin2, Mingzhe Liu3,*, Xuan Liu4, Wenfeng Zheng1,*, Lirong Yin5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1149-1161, 2023, DOI:10.32604/csse.2023.038598

    Abstract Visual question answering (VQA) has attracted more and more attention in computer vision and natural language processing. Scholars are committed to studying how to better integrate image features and text features to achieve better results in VQA tasks. Analysis of all features may cause information redundancy and heavy computational burden. Attention mechanism is a wise way to solve this problem. However, using single attention mechanism may cause incomplete concern of features. This paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism method. In the case… More >

  • Open Access

    ARTICLE

    Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

    Slim Ben Chaabane1,2,*, Rafika Harrabi1,2, Anas Bushnag1, Hassene Seddik2

    Journal on Artificial Intelligence, Vol.4, No.4, pp. 201-214, 2022, DOI:10.32604/jai.2022.032850

    Abstract Biometrics represents the technology for measuring the characteristics of the human body. Biometric authentication currently allows for secure, easy, and fast access by recognizing a person based on facial, voice, and fingerprint traits. Iris authentication is one of the essential biometric methods for identifying a person. This authentication type has become popular in research and practical applications. Unlike the face and hands, the iris is an internal organ, protected and therefore less likely to be damaged. However, the number of helpful information collected from the iris is much greater than the other biometric human organs. This work proposes a new… More >

  • Open Access

    ARTICLE

    A Fault Feature Extraction Model in Synchronous Generator under Stator Inter-Turn Short Circuit Based on ACMD and DEO3S

    Yuling He, Shuai Li, Chao Zhang*, Xiaolong Wang

    Structural Durability & Health Monitoring, Vol.17, No.2, pp. 115-130, 2023, DOI:10.32604/sdhm.2023.022317

    Abstract This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators. Different from the past methods focused on the current or voltage signals to diagnose the electrical fault, the stator vibration signal analysis based on ACMD (adaptive chirp mode decomposition) and DEO3S (demodulation energy operator of symmetrical differencing) was adopted to extract the fault feature. Firstly, FT (Fourier transform) is applied to the vibration signal to obtain the instantaneous frequency, and PE (permutation entropy) is calculated to select the proper weighting coefficients. Then, the signal is decomposed by ACMD, with the instantaneous frequency and… More >

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