A Method for Finding LR Fuzzy Eigenvectors of Real Symmetric Matrix by Xinyi Duan, Hangru Lin, Xiaobin Guo

“…In this paper, the calculation methods of the real eigenvalues and LR fuzzy eigenvectors of clear real symmetry matrices are deeply considered. The original fuzzy feature problem is extended by using the arithmetic algorithm of LR fuzzy numbers into a simple feature problem with a high-order clear real symmetry matrix. …”
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IoT intrusion detection method for unbalanced samples by ANTONG P, Wen CHEN, Lifa WU

“…In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users.…”
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Projectile Explosion Weak Firelight Image Recognition Method Using Multi-Scale Adaptive Enhancement Network by Xuebin Liu, Hanshan Li, Shaopeng Liang

“…The traditional VGG16 backbone network is used as the feature extraction network, and the firelight feature extraction network structure of the projectile explosion image is designed by adding the bidirectional aggregation feature pyramid. …”
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An information-DNA based method of information dissemination and evolution on Internet by Panpan LI, Zhengxia XIE, Zengkai WANG, Rui JIN

“…To solve the problem of how to trace the information content dissemination and evolution process on the Internet, an information DNA-based method of information dissemination and evolution on Internet was proposed.Firstly, semantic extraction of Internet information content was performed based on domain knowledge to form a key feature set of information content.Then, using the key feature set of information content, an information DNA construction method based on locally sensitive hashing was proposed.Finally, the usability and effectiveness of the proposed method were verified by public dataset.The problem of traceability of Internet homologous information dissemination and evolution process was solved by using information DNA as the core identifier, which was of great practical significance for the study of Internet information content dissemination, evolution tracing and the governance and guidance of Internet public opinion events.…”
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An information-DNA based method of information dissemination and evolution on Internet by Panpan LI, Zhengxia XIE, Zengkai WANG, Rui JIN

“…To solve the problem of how to trace the information content dissemination and evolution process on the Internet, an information DNA-based method of information dissemination and evolution on Internet was proposed.Firstly, semantic extraction of Internet information content was performed based on domain knowledge to form a key feature set of information content.Then, using the key feature set of information content, an information DNA construction method based on locally sensitive hashing was proposed.Finally, the usability and effectiveness of the proposed method were verified by public dataset.The problem of traceability of Internet homologous information dissemination and evolution process was solved by using information DNA as the core identifier, which was of great practical significance for the study of Internet information content dissemination, evolution tracing and the governance and guidance of Internet public opinion events.…”
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Android collusion attack detection model by Hongyu YANG, Zaiming WANG

“…In order to solve the problem of poor efficiency and low accuracy of Android collusion detection,an Android collusion attack model based on component communication was proposed.Firstly,the feature vector set was extracted from the known applications and the feature vector set was generated.Secondly,the security policy rule set was generated through training and classifying the privilege feature set.Then,the component communication finite state machine according to the component and communication mode feature vector set was generated,and security policy rule set was optimized.Finally,a new state machine was generated by extracting the unknown application’s feature vector set,and the optimized security policy rule set was matched to detect privilege collusion attacks.The experimental results show that the proposed model has better detective efficiency and higher accuracy.…”
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Research of the Method of Gear Fault Classification based on Contourlet Transform and Local Binary Pattern by Zhang Fangdong

“…For the problem that the gear fault feature is difficult to extract in actual working condition,a fault feature extraction method based on vibration signal time-frequency image is proposed,which is based on the global texture of the contourlet transform and the local texture of the local binary pattern. …”
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GENERALIZED ALGORITHM OF COMBINE PROCESS ADJUSTMENT BASED ON FUZZY KNOWLEDGE MODELS by Valery Petrovich Dimitrov, Lyudmila Victorovna Borisova

“…The specific feature of the proposed problem-solving algorithm is the hypothesis testing of emerging combining process non-conformances under the machine parameter variations. …”
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Research on Target-detection and Tracking of Intelligent Mine Trucks in Open-pit Mines by LI Yuanzhengyu, HU Yunqing, LONG Teng, HUANG Wenyu, PAN Wenbo, LI Peijie

“…Among them, feature extraction module uses point cloud gradient and distance feature extraction methods to solve the point cloud segmentation problem of small and irregular-shaped obstacles in changing scenes; target tracking module constructs track information for multi-target tracking, which improves the stability of target tracking; an asynchronous fusion strategy of multi-source sensors is designed for heterogeneous sensor fusion module, which overcomes the problem of heterogeneous sensor fusion, and improves the detection and tracking capabilities of small and various targets in unpaved road and dusty environment of open-pit mines. …”
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多尺度主元坐标变换在轴承故障声发射信号增强检测中的应用 by 张晓涛, 唐力伟, 王平, 邓士杰

“…Aiming at the problem of weak feature extraction and recognition in the bearing acoustic emission signal processing,multi-scale principal component coordinate transformation is proposed to enhance fault feature.Firstly,the signal is decomposed by wavelet package,and the sub-band reconstruction component is converted to a new linear space by principal component analysis,and the signal fault feature is enhanced.Finally,the method performance is verified by simulation signal and testing signal,the results show that multi-scale principal component coordinate transformation has obvious enhancement effect in bearing fault detection.…”
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Large Language Model-Assisted Deep Reinforcement Learning from Human Feedback for Job Shop Scheduling by Yuhang Zeng, Ping Lou, Jianmin Hu, Chuannian Fan, Quan Liu, Jiwei Hu

“…Deep reinforcement learning has shown great potential in solving this problem. However, it still has challenges in reward function design and state feature representation, which makes it suffer from slow policy convergence and low learning efficiency in complex production environments. …”
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Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics by Yong He, Jiangtao Yu, Xiaochuan He

“…And a new fractional statistical model is proposed to increase the number of correct matching pairs, So as to improve the fastness and accuracy of characteristic matching. Aiming at the problem of mismatch caused by local similarity of images, a data set is proposed to determine the data set by using geometric relationship between feature points, which analyzes the similarity between the data by the Person correlation coefficient, and sets the threshold to remove the feature matching pairs with low confidence, so as to improve the accuracy of feature matching. …”
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A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks by Yuhong Wang

“…In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. …”
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Recognition method based on hesitant fuzzy set for unequal length sequences and its application by Shuangming LI, Xin GUAN, Guidong SUN

“…Aiming at the problem that unequal length sequences were difficult to recognize, a recognition method based on hesitant fuzzy distance measure was proposed.Firstly, the problem was described from the perspective of fuzzy value, and the hesitant fuzzy information recognition model of unequal length sequence was established by lattice closeness degree.Secondly, the mean value, variance, relative range and hesitancy degree of hesitant fuzzy values were defined.Combined with membership difference of the shorter part, the generalized integrated feature distance measure and the generalized weighted integrated feature distance measure were defined to meet relevant properties of metric space, and the strict mathematical proof process was given.Finally, entropy measure and support measure were proposed to determine the weight, and the VIKOR recognition method based on hesitant distance measure was given.The simulation results verify the effectiveness and feasibility of proposed method from numerical examples, energy strategy selection and target recognition respectively.…”
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Research on transformer operation state prediction based on comprehensive weights and BO-CNN-GRU by Ying Liu, Wenbin Cao, Xiaoming Zhang, Yuhang Sun, Xu Sun

“…Firstly, 11 kinds of monitoring data in three categories including oil chromatography gas content, temperature, and electrical quantity are selected as feature parameters; Then, the game theory method is used to integrate the weight values of the three methods of G1 method, entropy weight method and CRITIC method to get the comprehensive weight value of each feature parameter, and the transformer operation state index is constructed based on the comprehensive weight; Finally, the BO-CNN-GRU combination prediction model is built, which solves the problem of difficulty in determining the hyperparameters of the model. …”
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Generalized Intuitionistic Fuzzy Normalized Weighted Optimized Geometric Bonferroni Mean and Their Application to MADM by Jin-Bo Zhang, Tian-Le Sun, Ming-Hua Shi

“…Long-term research has proved that information aggregation operator is an effective tool to solve this kind of problem. Bonferroni mean (BM) is an important information aggregation tool which has the main feature of capturing the interrelationships among aggregated arguments. …”
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Binary Transformer Based on the Alignment and Correction of Distribution by Kaili Wang, Mingtao Wang, Zixin Wan, Tao Shen

“…To tackle this problem, the feature distribution alignment operation in binarization is investigated. …”
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Planetary Gearbox Fault Diagnosis based on LMD Sample Entropy and ELM by Zhang Ning, Wei Xiuye, Xu Jinhong

“…In order to solve the difficult problem of early fault feature extraction of planetary gearbox and consider that the planetary gearbox vibration signal is coupling and nonlinear，and the signal has multiple transmission paths，a planetary gearbox fault diagnosis method based on Local Mean Decomposition（LMD） and Sample Entropy and Extreme Learning Machine（ELM） is proposed.Firstly，the vibration signal is adaptively decomposed into a plurality of PF components by LMD，and the first four PF components including the main fault information are selected in combination with the correlation coefficient and the variance contribution rate.Secondly，the Sample Entropy of the signal is calculated to form a feature vector.Finally，the feature vector is input into ELM for fault classification.Experiments are carried out on the planetary gearbox test bench，compared with the probabilistic neural network classification algorithm，and compared with the feature vector based on Singular Value Decomposition （SVD）.The results verify the effectiveness of the proposed method.…”
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Facial Expression Recognition-You Only Look Once-Neighborhood Coordinate Attention Mamba: Facial Expression Detection and Classification Based on Neighbor and Coordinates Attention... by Cheng Peng, Mingqi Sun, Kun Zou, Bowen Zhang, Genan Dai, Ah Chung Tsoi

“…In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). …”
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Fault Diagnosis Research on Bearingof Motor Based on LMD And Support Vector Machine by YIN Zhao-jie, XU Tong-le, ZHENG Dian-kun

“…Secondly，the new kernel function is used to improve the SVM to complete the adaptive training，and the“one to many”method is used to solve the large sample data and multi classification problem. Finally，the energy feature vector is used as the training sample and test sample of SVM，and the fault information of motor bearing is trained and predicted. …”
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