With the continuous development of educational informatization, more and more emerging technologies are applied in teaching activities. These technologies provide massive and multidimensional information for teaching research, but at the same time, the information obtained by teachers and students presents an explosive increase. Extracting the core content of the class record text through text summarization technology to generate concise class minutes can significantly improve the efficiency of teachers and students to obtain information. This article proposes a hybrid-view class minutes automatic generation model (HVCMM). The HVCMM model uses a multilevel encoding strategy to encode the long text of the input class records to avoid memory overflow in the calculation after the long text is input into the single-level encoder. The HVCMM model uses the method of coreference resolution and adds role vectors to solve the problem that the excessive number of participants in the class may lead to confusion about the referential logic. Machine learning algorithms are used to analyze the topic and Section of the sentence to capture structural information. We test the HVCMM model on the Chinese class minutes dataset (CCM) and the augmented multiparty interaction (AMI) dataset, and the results show that the HVCMM model outperforms other baseline models on the ROUGE metric. With the help of the HVCMM model, teachers can improve the efficiency of reflection after class and improve the teaching level. Students can review the key content to strengthen their understanding of what they have learned with the help of the class minutes automatically generated by the model.

With the rapid development of technology, the scale of traffics in industrial control networks is increasing day by day. More malicious traffics brought terrible impacts on industrial areas. Modbus plays a momentous role in the communications of Industrial Control Systems (ICS), but it’s vulnerable to Denial of Service attacks(DoS). Traditional methods cannot perform well on fine-grained detection tasks which could contribute to locating targets of DoS and preventing the destruction. Considering the temporal locality and high dimension of malicious traffic, we proposed a Neural Network architecture named MODLSTM, which consists of three parts: input preprocessing, feature recoding, and traffic classification. By virtue of the design, MODLSTM can perform high-precision identification and fine-grained classification of DOS attacks in the Modbus/TCP-based system. To test our model’s performances, we conducted experiments on our traffic dataset collected from the industrial control network, and the models achieved excellent performances in comparison with previous work(accuracy increased by 0.74%). The results show that our proposed method has satisfactory abilities to detect DoS attacks related to Modbus, it could help to build a reliable firewall to address the DoS traffic in industrial environments.

In this paper, we present an innovative design of a solder paste inspection device which can be practically integrated into existing solder paste printing machines. Since solder paste inspection systems usually occupy a large space in vertical direction, we designed a mirror box that can re-direct the transmission of fringe pattern. In this way, a new parallel solder paste inspection device with a significant reduction in the vertical constraint is developed. We also developed a hybrid weighting algorithm that applied the distance and fringe contrast to acquire the height of solder pastes. Furthermore, we developed an algorithm that generates the 2-D image from the fringe pattern images during the 4-steps algorithm. It gives benefit (time for solder paste inspection) to traditional approach that uses some special lighting systems to create the 2-D image. Experimental results show our device can inspect the 20mm times 20mm PCB area within 2 seconds and the maximum standard deviation for the average height is 3 mum.

The goal of this paper is to monitor in-process on-board data as a means of indicating product quality and to be able to respond quickly to unexpected process disturbances. Due to pending environmental legislation and market requirements, lead-free soldering is widely used by the electronics industry. As the margin between the higher melting temperatures of lead-free solders and the heat-resistant temperatures of electronic components becomes narrower than lead solders, a more precise control of the temperature is required. Traditionally, the control processes of the on-board temperature are open loop because it is difficult to monitor the temperature on a PCB board. In this paper, we establish a method to determine the real process temperatures at any point on a PCB board in the furnace. We develop the method based on support vector machines (SVM) with multiple-input single-output strategies to learn relationship between the temperatures near the PCB board and the on-board temperature. The method is the only one which has been commercially utilized to predict the on-board temperature because of its low cost and high accuracy