Feature Extraction of Weak Fault for Rolling Bearing based on Spectral Kurtosis and MOMEDA by Fuwang Liang, Huer Sun, Kexin Liu

“…Aiming at the problem that the early periodic transient impulse of rolling bearings is not obvious and the spectral kurtosis is poorly analyzed under low signal-to-noise ratio， a method of extracting the weak fault features of rolling bearing based on the combination of multipoint optimal minimum entropy deconvolution adjusted （MOMEDA） and spectral kurtosis is proposed. …”
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Feature Extraction of Weak Fault of Rolling Bearing based on TVF-EMD and TEO by Kexin Liu, Huer Sun, Fuwang Liang

“…Aiming at the problem that the vibration signals for rotating machinery rotors are usually accompanied by strong noise， it is difficult to extract its effective information. …”
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Feature Extraction of Weak Fault for Rolling Bearing based on Improved SSD Denoising by Xupeng Wang, Huer Sun

“…Aiming at the problem of early weak fault features of rolling bearings are difficult to be extracted under strong background noise and the components decomposed by the singular spectral decomposition method still contain noise，a method of extracting the weak fault features of rolling bearing based on the combination of singular spectrum decomposition （SSD） and maximum cyclostationarity blind deconvolution （CYCBD） is proposed. …”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing by Yutao Liu, Huer Sun

“…The blind deconvolution based on cyclostationarity maximization （CYCBD） is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise. …”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis by Agnieszka Wosiak, Danuta Zakrzewska

“…Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. …”
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RESEARCH ON ROLLING BEARING FAULT FEATURE EXTRACTION METHOD WITH SGMD-MOMEDA (MT) by CAO YaLei, DU YingJun, WEI Guang, DONG XinMin, GAO LiPeng, LIU YuXi

“…Aiming at the problem that the vibration signal of rolling bearing is difficult to extract due to the characteristics of non-linear, non-stationary and low signal-to-noise ratio, a new fault extraction method based on symplectic geometry mode decomposition（SGMD） and multipoint optimal minimum entropy deconvolution adjusted（MOMEDA） theory is proposed. …”
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Prediction Model of Material Properties Based on Feature Fusion and Convolutional Neural Network by SHI Jingchen, LIU Feining, WANG Wenjie, ZHAO Rui

“…Aiming at the problem that most machine learning models need a lot of prior knowledge and manual selection of feature vectors in the prediction of material properties, a convolutional neural network model OPCNN (Orbital of Electron and Periodic table CNN) is established by feature fusion based on two descriptors, electronic orbit matrix and periodic table method. …”
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A feature-based intelligent deduplication compression system with extreme resemblance detection by Xiaotong Wu, Jiaquan Gao, Genlin Ji, Taotao Wu, Yuan Tian, Najla Al-Nabhan

“…In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. …”
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Deconstructability prediction for building using machine learning and ensemble feature selection techniques by Habeeb Balogun, Hafiz Alaka, Eren Demir, Christian Nnaemeka Egwim, Godoyon Ebenezer Wusu, Wasiu Yusuf, Muideen Adegoke, Iqbal Qasim

“…A deconstructability predictive model using a machine learning-based model and ensemble feature selection techniques was developed to tackle this problem. …”
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Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection by Haojie Lou, Yuanlin Zheng, Wenqian Chen, Haiwen Liu

“…It is always a challenging task in the industry to detect the small printing defects under complex background. To address this problem, a defect detection algorithm based on multi-scale feature similarity evaluation and small object defect detection is proposed. …”
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Ship detection optimization method in SAR imagery based on multi-feature weighting by Quanhua ZHAO, Xiao WANG, Yu LI, Guanghui WANG

“…Aiming at the problem that the accuracy of traditional ship detection algorithms is not satisfying in complex scene with many false alarm targets,a ship detection optimization method in SAR imagery based on multi-feature weighting was proposed.Firstly,the marker-based watershed algorithm was employed to remove land from SAR amplitude image.Then,the CFAR algorithm based on log-normal distribution was used to obtain candidate targets from no land image.Furthermore,the length to width ratio,the ship area and the contrast ratio of the candidate targets were extracted.Finally,a variance coefficient method was proposed to distribute the weight of the three features,and the confidence levels were calculated by combining the normalized feature vectors of the candidate targets with the feature weight.By determining the best confidence level,false alarm targets among the candidate targets were removed to optimize ship detection results.In order to verify the proposed method,experiments were carried on with the GF-3 SAR images of different complex scenes.The experimental results show that the proposed method is feasible and effective.…”
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Multilevel Feature Fusion-Based GCN for Rumor Detection with Topic Relevance Mining by Shenyu Chen, Meng Li, Weifeng Yang

“…This paper addresses the problem of detecting internet rumors in social media. …”
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Fault diagnosis algorithm based on multi-channel neighbor feature convolutional network by Huang Xiao, Hanqing Jian

“…First, to mitigate the covariate shift problem in the data, inverted mel-scale frequency cepstral coefficients are introduced to obtain domain-invariant features with high recognition accuracy. …”
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Water quality features of the Podpolnaya small river at the collector-drainage water discharge by Natalia N. Krasovskaya, Mikhail V. Vlasov

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Smart train control and monitoring system with predictive maintenance and secure communications features by Alfian Akbar Gozali, Muhammad Faris Ruriawan, Andry Alamsyah, Yudha Purwanto, Ade Romadhony, Febry Pandu Wijaya, Fifin Nugroho, Dewi Nala Husna, Agri Kridanto, Anang Fakhrudin, Mu’ammar Itqon, Sri Widiyanesti

“…First, the data analytics features predict the product age and deliver real-time notifications. …”
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A composite Feature Selection Method to improve Classifying Imbalanced Big Data by Shaymaa Razoqi, Ghayda Al-Talib

“…Therefore, this research proposed a composed feature selection method using the filter feature selection technique and permutation-based important features with the ensemble learning method. …”
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Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment by Yulan Xu, Qiaowei Li

“…In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. …”
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Quantitative evaluation of college music teaching pronunciation based on nonlinear feature extraction by Wang Aiping

“…The complexity and diversity of different music styles created by different composers make music evaluation a very difficult problem. To quantitatively evaluate the quality of music pronunciation, this work proposes a method for extracting nonlinear features of music signals. …”
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