Traditional detection methods need to consume a lot of manpower and material resources, causing great difficulties in the research of malicious code. Aiming at the problem of malicious code family classification, this paper proposes an improved convolutional neural network model based on Xception (Simplified-Xception). The model uses the dataset from the 2015 Kaggle Microsoft Malicious Code Classification Contest. Firstly, the malicious code is transformed into the gray image as the model input, the number of modules of the original model is reduced, and a layer of depth-separable convolution with a step size of 2 is added to enhance the generated gray image. The convolutional network model is improved based on the Xception model. The model in this paper is compared with the CNN model, ResNet50, and Inception-related improvement models. The experimental results show that the accuracy of Simplified- Xception is 98%, which is better than that of other inception related models. Compared with the Xception model, the accuracy of the Simplified -Xception model is increased by 1.3%, and the number of parameters is reduced by half.
Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach.
The COVID-19 has been posing threats to peoples health around the world due to its simple transmission route, strong mutation ability, and high infection. Currently, the superb vaccine breakthrough ability of mutant strains puts people's lives and health further at risk. Many measures are taken to fight against the epidemic. Keeping a safe social distance to prevent the spread of COVID-19 in public places. In this paper, we propose a method to monitor social distancing from a bird's-eye view. It mainly includes three parts. Firstly, an objector based on YOLOv5m is trained to detect human beings. It shows good performance compared with other models such as Faster R-CNN and YOLOv3. Secondly, we propose to use SORT algorithm to track each human by assigning it an ID. It also ensures the accuracy of multiple-object tracking. More importantly, it makes for tracking those who violate social distance. Thirdly, the Euclidean distance between detected people is used to judge whether human beings keep social distance or not. It is implemented by computing the pairwise distances of the detected bounding box centroids. Experiments conducted on birds view dataset show that our method can monitor social distance well. Hopefully the proposed method and dataset can provide some help in the fighting against COVID-19.
Model predictive control is an effective way to achieve the control of wastewater treatment process (WWTP). However, it is a challenge to control multiple objectives due to the complexity of WWTP. To solve this problem, an eigenvector based multiobjective model predictive control (EMMPC) strategy is developed for WWTP to handle the conflicting objectives. First, a multiobjective control scheme is designed with adaptive fuzzy neural network prediction (AFNNP) model and gradient eigenvector optimization (GEO) algorithm. Then, AFNNP can be used to describe the nonlinear of WWTP to predict the controlled variables. Second, GEO is presented to obtain the control laws of the multiple control objectives. Specifically, GEO can reduce the computational burden by avoiding the determination of the control objective weights. Third, the stability of EMMPC is provided in theory. Finally, EMMPC is implemented on the benchmark simulation platform to demonstrate the effectiveness of the presented multiobjective control method.
Fuzzy transfer learning has the potential to enhance the performance of a task in the target scene by leveraging knowledge from the source scene. However, the existence of scene discrepancies between the two scenes poses two major problems for fuzzy transfer learning: 1) the mismatch between the distribution of source knowledge and the target task, and 2) the mismatch between the learning ability of the target task and the source knowledge. To address these challenges, we propose a fuzzy transfer learning algorithm called Knowledge-Task Matching Fuzzy Transfer Learning (KTM-FTL), which aims to adaptively match the source knowledge and the target task. Firstly, we develop a knowledge matching mechanism that balances matching accuracy and diversity to reconstruct the knowledge from the source scene. This process enables the source knowledge to align more accurately with the requirements of the target scene's task. Secondly, we design a task matching mechanism that allows for learning the source knowledge at different levels of granularity. This approach enables the learning ability of the target Feedforward Neural Network (FNN) to match the complexity of the source knowledge, thereby improving its generalization performance. Thirdly, we conduct theoretical analysis of the proposed KTM-FTL, including an examination of its computational complexity and error bound. These analyses provide valuable insights for the successful application of KTM-FTL. Finally, we evaluate the performance of the proposed KTM-FTL on several benchmark problems as well as real-world problems. The experimental results demonstrate the significant improvements achieved by KTM-FTL when compared to state-of-the-art algorithms.
