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As infrastructure-as-a-service clouds quickly grow, an increasing number of businesses and people are moving their application development to the cloud. The purpose of the research is to solve the problem of identifying memory anomalies in cloud virtual machines and improve the accuracy of the model in detecting abnormal situations. This paper presents a model for detecting virtual machine anomalies in IaaS cloud platform. The model considers the unique properties of monitoring metrics as time-series data and proposes an approach based on four important virtual machine monitoring metrics. The study also develops an adaptive anomaly detection system based on deep Q-network algorithms and migration learning principles for the variety of VM monitoring data in the cloud. The testing findings reveal that utilizing a Zoom layer with a 2-kernel size can increase detection accuracy to 96.7%. This demonstrates that a portion of the experimental data can extract the temporal features using the Zoom layer and different kernel sizes. The research model for anomaly detection had a classification accuracy of 99.8%. The deep Q-network model's final anomaly detection accuracy varies from 96.7 to 98.6%. The outcomes of the research improved the system's security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance. [ABSTRACT FROM AUTHOR]

Drilling is one of the most classical machining operations. Real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on image processing and deep learning methods, which works effectively for small sample datasets and for low-quality images. The normal areas and worn areas of the drill bits are extracted using the U-Net network and traditional image processing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than previous methods using manual measurements. These results can be applied to real-time drill wear monitoring, thus reducing part damage caused by tool wear. [ABSTRACT FROM AUTHOR]

To address the problem that the ant colony algorithm in solving Traveling Salesman Problem (TSP) has a slow convergence speed and easily falls into local optimum, an Ant Colony Algorithm with n- α -measure and Migration Learning is proposed. Firstly, the n- α -measure is proposed to map the least squares to the distance matrix of TSP nodes for improving the connection between nodes; second, multiple parameters in the ant colony algorithm are extended in multiple dimensions to improve the control of the algorithm over the pheromone; finally, a communication channel is established between multiple populations, and a transfer learning mechanism is proposed to exchange information. The simulation experiments of multiple cases in TSPLIB show that the improved algorithm balances diversity and the convergence speed, and effectively improves the quality of the solution. [ABSTRACT FROM AUTHOR]

Traditional detection methods are not sensitive to small-sized tomato organs (flowers and fruits), because the immature green tomatoes are highly similar to the background color. The overlap among fruits and the occlusion of stems and leaves on tomato organs can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a tomato organ recognition method based on improved Feature Pyramid Network was proposed in this paper. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized tomato organs to improve recognition rate. And then repulsion loss was used to take place of the original smooth L1 loss function. Besides, Soft-NMS (Soft non-maximum suppression) was adopted to replace non-maximum suppression to screen the bounding boxes of tomato organs to construct a recognition model of tomato key organ. Finally, the network was trained and verified on the collected image data set. The results showed that compared with the traditional Faster R-CNN model, the performance was greatly improved (mean average precision was improved from 90.7 to 99.5%). Subsequently, the training model can be compressed so that it can be embedded into the microcontroller to develop further precise pesticide targeting application system of tomato organs and the automatic picking device. [ABSTRACT FROM AUTHOR]