Features of A New 500 kVAR Static VAR Generator

The paper briefly describes the main features of a new 500 kVAR static VAR generator designed and manufactured by NAm for industrial test and trial

Applying Wide & Deep Learning Model for Android Malware Classification

Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile market.With the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android malware.Deep learning technology is designed to handle a large amount of raw and continuous data,such as image content data.However,it is incompatible with discrete features,i.e.,features gathered from multiple sources.Furthermore,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine learning.On the other hand,a wide learning model can expand the feature set to enhance the classification accuracy.To maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning method.The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component.The proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw image.The accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.

Concurrent Engineering oriented Integrated Product Model Based on STEP

In this paper, the generalized feature concept is put forward according to concurrent engineering. An integrated product model is established based on the generalized feature according to STEP in order to provide enrichment information for product concurrent development process. The integration of the information and function of CAD/CAPP can be realized based on the integrated product model that supports concurrent engineering. IPM has been used successfully in product concurrent development.

Improved YOLOv7 Algorithm for Floating Waste Detection Based on GFPN and Long-Range Attention Mechanism

Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection.