In the face of complex network attacks, in order to ensure network security, an automatic identification method of computer network illegal intrusion based on big data feature mining is proposed. In this method, K-means clustering algorithm is used to cluster computer network data to obtain abnormal data of computer network and reconstruct the phase space of abnormal data. It is used as the input of the least squares support vector machine to establish an automatic identification classifier for illegal intrusion. By constructing a classification plane to automatically identify the types of illegal intrusion in computer networks, the purpose of accurately and automatically identifying illegal intrusion in computer networks is realized. The experimental results show that this method has a good clustering effect on abnormal data of computer network, and can accurately and automatically identify illegal intrusion of computer network. By fully considering the complexity and computational efficiency of the algorithm, redundant calculation and unnecessary memory access are reduced, and this method is feasible to automatically identify illegal intrusion of computer network.
Due to the differences between communication protocols of different video devices,the communication data transmission of device interaction is incomplete,so a communication protocol conversion system for mass video device interaction is proposed.DS80C320 CPU is used to realize communication specification conversion and serial communication,and SDRAM memory is used to convert virtual address into physical address to ensure data integrity and security.The reset circuit is designed with RCM5700 as the core to complete power-on reset,manual reset and periodic automatic reset.The classification model of deep convolution neural network is constructed,and the characteristics of video data are extracted and classified.Read that classified label,setting corresponding function codes,jud whether key information is randomly inserted or not,and realizing a data conversion protocol according to the function codes of the key information.The results of the example show that the maximum error between the accumulated data of the designed system and the actual data is 5 bits,and the communication data can be completely transmitted with high conversion efficiency.
Facial paralysis refers to the abnormal behavior of facial muscles caused by a disorder of the facial nerve, mainly manifested as facial asymmetry. In recent years, deep learning has found extensive applications in facial paralysis detection research. However, most existing methods are constrained to assessing the severity of facial paralysis, thereby concealing crucial symptoms within black-box models. Compared to the severity of facial paralysis, the symptoms of facial paralysis are of greater significance to both physicians and patients. To address this issue, this paper proposes a facial paralysis symptom detection model based on facial action units (AUs). To enhance the accuracy of AU intensity prediction, a novel Difference Ensemble Method (DEM) is introduced. This method leverages differential information between frames within the same video to improve the accuracy of predictions for the current frame. Building upon the predicted AU intensity sequences for keyframes in a video, an interpretable model for detecting facial paralysis symptoms is designed. This model employs an active means to describe the asymmetry in facial muscle strength and utilizes co-occurrence matrices to detect synkinesis. It is noteworthy that DEM is exclusively trained on a dataset of normal faces but exhibits excellent performance when transferred to a facial paralysis dataset. Additionally, DEM exhibits higher accuracy in predicting AU intensity compared to existing methods. The F1 scores for detecting facial muscle function in the eyebrow, eye, and mouth regions with our proposed model are 80.0%, 79.23%, and 90.91%, respectively. To demonstrate the model’s performance, a synkinesis detection experiment is conducted, further validating its applicability in facial paralysis detection.
Aiming at the problem of intelligent supervision and control of power engineering projects,this paper proposes a power engineering data processing and risk identification model based on FCM-IFA-SVM algorithm.The algorithm uses FCM algorithm to cluster and analyze the index data of environmental risk,technical risk,economic risk and management risk,and then uses IF A algorithm to optimize the penalty parameters and kernel function parameters of SVM model.The clustered data set is input into the SVM algorithm after parameter optimization,and the risk grade evaluation result of power engineering project is obtained.The simulation test results of a power engineering data set show that the proposed algorithm has higher accuracy in risk identification.Compared with SVM and FCM-SVM algorithm,the average accuracy is improved by 7.1％and 2.9％,it shows that the proposed algorithm can more accurately identify and evaluate the risk level of power engineering projects.
A set of accurate timing constraints for both top and blocks is an important guarantee of static timing analysis and timing closure in the Integrated Circuit(IC)design procedure.To achieve high efficiency,this paper proposed an automated timing constraints propagation approach,which includes timing constraints demotion and promotion.Comparing to traditional method,the automatic approach avoids the manual process for propagating timing constraints that caused by design and timing iteration.Therefore,it reduces the human effort for the timing constraint generation and speeds up the timing closure period.The experiment result shows that this approach performs high accuracy for both test cases and industrial projects,and significantly improves both efficiency and automaticity for timing constraint generation.
