194 publications from this institution
In the present paper, we introduce a new axiomatic definition of the inclusion measure for intuitionistic fuzzy sets (IFSs, for short).The close relationships among entropy, similarity measure, and inclusion measure of IFSs are then discussed in detail.Also, we obtain some important theorems by which the entropy, similarity measure and inclusion measure of IFSs can be transformed into each other based on their axiomatic definitions.Moreover, some formulae for calculating the entropy, similarity measure and inclusion measure of IFSs are put forward.Finally, we compare the proposed new entropy, similarity and distance measures with the existing ones.
Lateral roots play essential roles in drought tolerance in maize (Zea mays L.). However, the genetic basis for the variation in the number of lateral roots in maize remains elusive. Here, we identified a major quantitative trait locus (QTL), qLRT5-1, controlling lateral root number using a recombinant inbred population from a cross between the maize lines Zong3 (with many lateral roots) and 87-1 (with few lateral roots). Fine-mapping and functional analysis determined that the candidate gene for qLRT5-1, ZmLRT, expresses the primary transcript for the microRNA miR166a. ZmLRT was highly expressed in root tips and lateral root primordia, and knockout and overexpression of ZmLRT increased and decreased lateral root number, respectively. Compared with 87-1, the ZmLRT gene model of Zong3 lacked the second and third exons and contained a 14 bp deletion at the junction between the first exon and intron, which altered the splicing site. In addition, ZmLRT expression was significantly lower in Zong3 than in 87-1, which might be attributed to the insertions of a transposon and over large DNA fragments in the Zong3 ZmLRT promoter region. These mutations decreased the abundance of mature miR166a in Zong3, resulting in increased lateral roots at the seedling stage. Furthermore, miR166a post-transcriptionally repressed five development-related class-III homeodomain-leucine zipper genes. Moreover, knockout of ZmLRT enhanced drought tolerance of maize seedlings. Our study furthers our understanding of the genetic basis of lateral root number variation in maize and highlights ZmLRT as a target for improving drought tolerance in maize.
A new concept of the inclusion measure for intuitionistic fuzzy sets is proposed by the axiomatic definition. Also, the distance measures, similarity measures and information entropy are recalled and summarized. Most of important, some relationships among distance measure, information entropy, and inclusion measure of IFSs are then investigated. Finally, we obtain some important theorems by which the distance measure, information entropy and inclusion measure of IFSs and interval-valued intuitionistic fuzzy set can be deduced based on their corresponding axiomatic definitions. Simultaneously, some new formulae to calculate distance measure, information entropy and inclusion measure of IFSs are presented.
As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.
In order to obtain the position of thunderstorm cloud in real time and make it possible to track the thunderstorm cloud motion, a method is proposed for tracking the moving path of thunderstorm cloud, with the aid of the three‐dimensional atmospheric electric field apparatus (AEFA). According to the method of images, we establish a spatial model for tracking the moving path. Based on the model, we define the dynamic parameters of thunderstorm cloud position. Subsequently, to realize the moving path tracking of thunderstorm cloud, its coordinates are associated with the time points. Besides, we use the relationship between electric field component measurement error, horizontal angle, elevation angle, and the tracking accuracy to analyze the tracking performance. Finally, a fusion system combining an electric field measurement unit, electric field calibration unit, and permittivity measurement unit is designed to meet the actual needs. The results show that the method can accurately track the thunderstorm cloud moving path and has a better effect. In addition, the method can also be combined with a radar map, thus better predicting the development of the thunderstorm cloud.
By using integral operator, some oscillation criteria for second order elliptic differential equation$$ \sum^d _{i,j=1} D_i[A_{ij}(x)D_jy]+ q(x)f(y)=0, \;x \in \Omega\qquad \eqno{(E)} $$are established. The results obtained here can be regarded as the extension of the well-known Kamenev theorem to Eq.$(E)$.
For the serious problems of lighting waste in current universities and other public places,this paper designed an intelligent lighting control system.Taking AT89S52 microcontroller integrated circuit as master control,it used light intensity measurement module,sound intensity measurement module,stepping motor rotationpyroelectric infrared sensor modules to constitute a multi-way detection system.The innovative use of multi-sensor complementary probes,complemented by stepper motor rotation realization pyroelectric infrared sensor dynamic detection.After debugged,it certifies that the programme is possible,and it has high practical value.
The relationship between smart devices and human beings is one of the research hotspots of the Fourth Industrial Revolution (4IR). In this regard, we explored the practical relationship between the 3D electric field components measured by the smart 3D atmospheric electric field apparatus (AEFA) and the thunderstorm activity from the perspective of the observer. Especially, in the application of AEFA, a smart calibration method is proposed to solve the problem of inconvenient thunderstorm data acquisition. Firstly, in order to obtain the thunderstorm charge position from the observation angle of the apparatus, this paper establishes a 3D electric field measurement model. According to the mirror method theory, we further obtain the charge potential distribution at AEFA. Then, the electric field components are derived by using the potential distribution formula with permittivity. In addition, based on the vector relation of the model, the thunderstorm charge azimuth and elevation angles are obtained. Finally, after the establishment of a new coordinate system, the calibration of charge localization is carried out, based on the observation point. Meanwhile, a preliminary solution is given to the problem that the elevation of the apparatus position affects the localization performance. Results show that the method matches the data of radar map and microphone array, which reflects the advantages of the method. Besides, this method can be used not only in sound source localization but also in AI thunderstorm monitoring system to realize a big data net observation.
To get source azimuth from microphone observation angle of view in a complex real environment, this article, on the basis of the analysis of geometric positioning method, established a seven‐element microphone array model and proposed a sound source omnidirectional positioning calibration method based on microphone observation angle. By using a seven‐element array to invert the position and angle of a sound source, the relative time delay value of a pair of microphones on the vertical axis of the coordinate system is used to determine the elevation angle polarity and realize the omnidirectional sound source positioning. The array parameters, sound velocity, array size, horizontal deflection angle, elevation angle, and sound source are analyzed, and the error method is proposed. The sound source data was measured using the microphone array perspective, and a new Cartesian coordinate system was established based on the observation angle of view for omnidirectional positioning calibration of the sound source. The simulation results show that the position error of the method is about 0.01% and the angle error is about 0.005%, with high calibration accuracy. The actual measurement results show that this method can effectively calibrate the sound source azimuth, the error rate of the source coordinates is around 10%, the horizontal declination angle error is less than 5%, and the elevation angle error is less than 8%. Appropriately increasing the spacing of the array will have a better calibration effect in an actual complex experimental environment.
A method of detecting weak signals embedded in chaotic noise by selective support vector machine ensemble based on the theory of phase space reconstruction of the complicated nonlinear system is presented. For improving the generalization ability of support vector machine ensemble, K-means algorithm is used to select the most accurate individual support vector machine from every cluster for ensembling It is established a one-step predictive model that detects the weak signal, including transient signal and period is signals, from the predictive error in the chaotic sequences. It is illustrated in the experiment which is conducted to detect weak signals from Lorenz chaotic background and IPIX Sea Clutter, that the proposed method is highly effective to detect weak signal from a chaotic background and to minimize the influence of noise on weak signals, Compared wich the RBF neural network and SVM model, the new method presents great value in predicting accuracy and detection threshold.
In order to detect low‐flying small targets in complex sea condition effectively, we study the chaotic characteristic of sea clutter, use joint algorithm combined complete ensemble empirical mode decomposition (CEEMD) with wavelet transform to de‐noise, and put forward a detection method for low‐flying target under the sea clutter background based on Volterra filter. By CEEMD method, sea clutter signal which contains small target can be decomposed into a series of intrinsic mode function (IMF) components, pick out high‐frequency components which contain more noise by autocorrelation function, and perform wavelet transform on them. The de‐noised components and remaining components are used to reconstruct clear signal. In view of the chaotic characteristics of sea clutter, we use Volterra filter to establish adaptive prediction model, detect low‐flying small target hiding in sea clutter background from the prediction error, and compare the root mean square error (RMSE) before and after de‐noising to evaluate de‐noising effect. Experimental results show that the joint algorithm can effectively remove noise and reduce the RMSE by 40% at least. Volterra prediction model can directly detect low‐flying small target under sea clutter background from the prediction error in the cases of high signal‐to‐noise ratio (SNR). In the cases of low SNR, after de‐noised by joint algorithm, Volterra prediction model can also detect the low‐flying small target clearly.
Aiming at the problem of harrowing target feature extraction for one-dimensional radar signals in the strong sea clutter background, this paper proposes a weak target detection method based on the combination of multi-modal time-frequency map fusion and deep learning in the sea clutter background. The one-dimensional signal is converted into three gray-scale maps with complementary characteristics by three signal processing methods: normalized continuous wavelet transform, Normalized Smooth Pseudo Wigner-Ville Distribution, and recurrence plot; the resulting two-dimensional grayscale maps are adaptively mapped to the R, G, and B channels through an adaptive weighting matrix for feature fusion, ultimately generating a fused color image. Subsequently, an improved multi-modal EfficientNetV2s classification framework was constructed, wherein the decision threshold of the Softmax layer was optimized to achieve controllable false alarm rates for weak signal detection. Experiments are carried out on the IPIX dataset and the China Yantai dataset, and the proposed method achieves certain improvement in detection performance compared with existing detection methods.
Industrial surface defect detection is critical for ensuring product quality and manufacturing efficiency across steel, electronics, and semiconductor sectors. However, practical deployment remains challenging due to the diversity of defect types, scale variations, and complex background noise. To address these issues, we propose YOLO-DCF (YOLO with Dual Distillation and Context-Aware Fusion), a novel and lightweight detection framework built upon YOLO11. The Context-Guided Dynamic Fusion FPN decomposes global context into orthogonal directional components, enabling precise localization of fine-grained defects while suppressing background noise, the C3k2-Dilated Multiscale Contextual Residual module leverages hierarchical receptive fields with parallel multi-dilation design to capture both local textures and global dependencies, and the Dual Block-Channel Knowledge Distillation module enhances model compression via a self-distillation mechanism by decoupling spatial and semantic knowledge flows, preserving essential representations during lightweight deployment. These modules enhance detection precision while maintaining real-time inference capability. Extensive experiments on NEU-DET and PKU-Market-PCB datasets validate the effectiveness of YOLO-DCF, which achieves mAP50 scores of 79.3% and 96.5%, respectively, representing significant improvements of 1.6% and 0.8% over baseline methods. Notably, YOLO-DCF demonstrates stronger recall and robustness in detecting fine-grained and low-contrast defects, while maintaining competitive real-time inference capability despite the increased model complexity. This work offers a practical and deployable solution for industrial quality inspection, and sets a new direction for efficient, distribution-aware visual recognition in manufacturing contexts.
In this paper we have studied the accuracy of field-to-current conversion factors (FCCFs) presented by Baba and Rakov for currents inferred from electromagnetic field produced by lightning strike to tall objects, considering the perfectly and finitely conducting ground, respectively. For the perfectly conducting ground, the different FCCFs for the peak currents have different accuracy ranging from about underestimation of 18% to overestimation of 10% for the reflection coefficients at the two ends of object ρ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> =−0.5 and π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> =1.0, and from about underestimation of 25% to overestimation of 10% for π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> =−0.5 and π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> =0.7, and their accuracy decreases with the increase of current risetime RT. For the finite conductivity with 0.01 S/m and 0.001 S/m, FCCFs will cause many errors if we do not take into account the propagation effect along the finitely conducting ground, and their errors obviously increase with the decrease of the conductivity. For example, for π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> =−0.5 and π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> =1.0, the errors are about 20% when the conductivity is 0.01 S/m while the errors are about 55% when the conductivity is 0.001 S/m for lightning strike to the 168-m-high object. Therefore, we revised FCCFs by considering the propagation effect of finite conductivity on the electromagnetic field radiated by lightning strike to tall objects, and found that our revised FCCFs have much better accuracy for the lossy ground than that presented by Baba and Rakov.
Intelligent polishing equipment comprising industrial robots and sensors is increasingly used to polish ship components, which has traditionally been done manually. The core component of such equipment is the visual recognition system, which is responsible for workpiece recognition and positioning, and polishing point recognition and extraction. Current systems are mostly based on CCD or CMOS cameras with 2D image processing. However, recognition efficacy is limited by the auxiliary lighting conditions and image resolution, and depth information cannot be obtained. An algorithm module is designed for object recognition in point cloud from a depth camera in a device used to polish small ship components. The module's architecture and the functions of the visual recognition system are described. The module's functions are based on the OpenCV and printer control language (PCL) libraries and are implemented in the C++ programming language. An algorithm test platform was built to verify the algorithm module its functions, including camera pose estimation and coordinate transformation, point cloud segmentation, centroid extraction, and edge extraction. The limitations of on-site auxiliary light conditions and image resolution were overcome. A workpiece recognition and positioning success rate of 98% was achieved, and the polishing point detection and recognition accuracy are sufficient for real applications. This allows accurate and comprehensive visual data to be provided to the automated polishing device, ensuring smooth operation and effective workpiece handling and polishing.
In order to improve the detection accuracy of sea clutter wavelet prediction model further, a sea clutter hybrid denosing algorithm based on variational modal decomposition (VMD) is proposed. The VMD is adopted to decompose the sea clutter signal into a finite number of intrinsic modal functions (IMF) with limited bandwidths of different center frequencies. Then, we analyze the auto-correlation property of the decomposed signal and perform wavelet hard threshold filtering on the modal component with noise characteristics. Reconstructing the filtered component and the residual component to obtain a denosied signal. The sea clutter prediction model based on LSSVM is adopted to verify the denosing result, and the denosing result is evaluated by comparing the predicted root mean square error (RMSE) before and after denosing. Comparing the prediction results of the two groups of experiments, it is not difficult to find that the predicted RMSE after denosing is 0.00055, which is two orders of magnitude lower than the predicted RMS error before denosing (0.0125).
