Multi-dimensional and Multi-threshold Airframe Damage Region Division Method Based on Correlation Optimization

In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.

An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

Intrusion Detection System(IDS)is a network security mechanism that analyses all users’and applications’traffic and detectsmalicious activities in real-time.The existing IDSmethods suffer fromlower accuracy and lack the required level of security to prevent sophisticated attacks.This problem can result in the system being vulnerable to attacks,which can lead to the loss of sensitive data and potential system failure.Therefore,this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification(LTH-CNN).Here,the Correlation Coefficient based Mayfly Optimization(CC-MA)algorithm is used to extract the input characteristics for the IDS from the input data.Then,the optimized features are utilized by the LTH-CNN,which returns the attacked and non-attacked data.After that,the attacked data is stored in the log file and non-attacked data is mapped to the cyber security and data security phases.To prevent the system from cyber-attack,the Source and Destination IP address is converted into a complex binary format named 1’s Complement Reverse Shift Right(CRSR),where,in the data security phase the sensed data is converted into an encrypted format using Senders Public key Exclusive OR Receivers Public Key-Elliptic Curve Cryptography(PXORP-ECC)Algorithm to improve the data security.TheNetwork Security Laboratory-Knowledge Discovery inDatabases(NSLKDD)dataset and real-time sensor are used to train and evaluate the proposed LTH-CNN.The suggested model is evaluated based on accuracy,sensitivity,and specificity,which outperformed the existing IDS methods,according to the results of the experiments.

Real-time monitoring and fault detection of pulsed-spray fluid-bed granulation using near-infrared spectroscopy and multivariate process trajectories

Pulsed spray is a useful tool forgranule size control in fluid bed granulation.To improve the quality control of pulsed-spray fluid bed granulation,a combination of in-line near-infrared(NIR)spectroscopy and p「incipal component analysis was used to develop multivariate statistical process control(MSPC)charts.Different types of MSPC charts were developed,including principal component score charts,Hotelling's T2 control charts,and distance to model X control charts,to monitor the batch evolution throughout the granulation process.Correlation optimized warping was used as an alignment method to deal with the time variation in batches caused by the granulation mechanism in MSPC modeling.The control charts developed in this study were validated on normal batches and tested on four batches that deviated from normal processing conditions to achieve real-time fault analysis.The results indicated that the NIR spectroscopy-based MSPC model included the variability in the sample set constituting the model and could withstand external variability.This research demonstrated the application of synchronized NIR spectra in conjunction w让h multivariate batch modeling as an attractive tool for process monitoring and a fault diagnosis method for effective process control in pulsed-spray fluid bed granulation.