The traditional automatic driving behavior decision algorithm needs to manually set complex rules, resulting in long vehicle decision‐making time, poor decision‐making effect, and no adaptability to the new environment. As one of the main methods in the field of machine learning and intelligent control in recent years, reinforcement learning can learn reasonable and effective policies only by interacting with the environment. Firstly, this paper introduces the current research status of automatic driving technology and the current mainstream automatic driving control methods. Then, it analyzes the characteristics of convolutional neural network, reinforcement learning method (Q‐learning), and deep Q network (DQN) and deep deterministic policy gradient (DDPG). Compared with the DQN algorithm based on value function, the DDPG algorithm based on action policy can well solve the continuity problem of action space. Finally, the DDPG algorithm is used to solve the control problem of automatic driving. By designing a reasonable reward function, deep convolution network, and exploration policy, the intelligent vehicle can avoid obstacles and, finally, achieve the purpose of avoiding obstacles and running the whole process in a 2D environment.

In order to improve the segmentation effect of brain tumor images and address the issue of feature information loss during convolutional neural network (CNN) training, we present an MRI brain tumor segmentation method that leverages an enhanced U-Net architecture. First, the ResNet50 network was used as the backbone network of the improved U-Net, the deeper CNN can improve the feature extraction effect. Next, the Residual Module was enhanced by incorporating the Convolutional Block Attention Module (CBAM). To increase characterization capabilities, focus on important features and suppress unnecessary features. Finally, the cross-entropy loss function and the Dice similarity coefficient are mixed to compose the loss function of the network. To solve the class unbalance problem of the data and enhance the tumor area segmentation outcome. The method's segmentation performance was evaluated using the test set. In this test set, the enhanced U-Net achieved an average Intersection over Union (IoU) of 86.64% and a Dice evaluation score of 87.47%. These values were 3.13% and 2.06% higher, respectively, compared to the original U-Net and R-Unet models. Consequently, the proposed enhanced U-Net in this study significantly improves the brain tumor segmentation efficacy, offering valuable technical support for MRI diagnosis and treatment.

Aiming at the demand of industrial instrument reading, this study proposes a method of industrial instrument classification and reading recognition based on YOLOv3. Given that industrial meters can be divided into pointer meters and digital meters according to the dial type, this method conducts a reading study for each of the two types of meters. Firstly, the YOLOv3 model is trained to recognize and detect the meter types and classify the meters according to the values of the obtained classes. The pointer meter uses a Hough circle to detect the dial, extracts the scale and the pointer, calculates the angle between the 0 scale line and the pointer, and obtains the reading of the pointer meter. The digital meter extracts the digits by finding the contours of the dial and the digit area and then uses a support vector machine (SVM) to identify the extracted digits and output the readings of the digital meter. Through the test, the mean average precision (mAP) of the recognition model in this study is 93.73%. The absolute error of pointer meter reading is less than 0.1 in general, and the maximum relative error is 0.35%. The accuracy of the digital meter reading is 99.7%. The proposed method can accurately read the value of the instrument and meet the needs of industrial production.

Servo motors play an important role in automation equipment and have been used in several manufacturing fields. However, the commonly used control methods need their parameters to be set manually, which is rather difficult, and this means that these methods generally cannot adapt to changes in operation conditions. Therefore, in this study, we propose an intelligent control method for a servo motor based on reinforcement learning and that can train an agent to produce a duty cycle according to the servo error between the current state and the target speed or torque. The proposed method can adjust its control strategy online to reduce the servo error caused by a change in operation conditions. We verify its performance on three different servo motors and control tasks. The experimental results show that the proposed method can achieve smaller servo errors than others in most cases.

Although the traditional recurrent neural network (RNN) model can cover the time information of the whole sentence theoretically, the gradient is dominated by the short-term gradient, and the long-term gradient is very small, which makes it difficult for the model to learn the long-distance information, and thus the effect of RNN on long text sentence recognition is poor. The long short-term memory network (LSTM) introduces the gate mechanism, especially the forgetting gate, which improves the disappearance of the gradient of RNN. Therefore, LSTM can store long text information and remove or increase the ability of information interaction by adding gate structure, which has natural advantages for long text processing. Based on the word vector matrix of GloVe model, on the open-source comment sentiment140 data set, we use the TensorFlow framework to construct the LSTM neural network and divide the data into the training set and test set based on the ratio of 4:1, design and implement the sentiment analysis published by Twitter users based on LSTM model, and then propose the bidirectional LSTM (Bi-LSTM) sentiment analysis method. The experimental results show that the accuracy of bidirectional LSTM is higher than that of unidirectional LSTM in sentiment analysis.