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Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the slow transmission rate of high-frequency information in smart grid and improve the efficiency of information transmission, a research method of high-frequency information transmission in smart grids based on the CNN-LSTM model is proposed. It effectively combines the superiority of the CNN algorithm for high-frequency information feature extraction and the learning ability of the LSTM algorithm for global features of high-frequency information. Meanwhile, the client buffer is divided by the VLAN area division method, which avoids the buffer being too large due to line congestion. The intelligent control module is adopted to change the traditional control concept. In addition, the neural network optimization control module is used for intelligent control, which ensures the feedback speed of the control terminal and avoids the problem of increasing the buffer area caused by the feedback time difference. The experimental results show that via the method in this paper, the total efficiency of single-channel transmission reaches 96% and the transmission rate reaches 46 bit/s; the total efficiency of multiplex transmission is 89% and the transmission rate reaches 75 bit/s. It is verified that the method proposed in this paper has a fast transmission rate and high efficiency. [ABSTRACT FROM AUTHOR]

Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the CNN does not sufficiently mine the tool wear information contained in the multi-sensor data due to disregard of the differences in the contribution of different features when extracting features. In this paper, a tool wear prediction method based on a multi-scale convolutional neural network with attention fusion is proposed, which fuses the tool wear degradation information collected by different types of sensors. In the multi-scale convolution module, convolution kernels with different sizes are used to extract the degradation information of different scales in the wear information, and then the attention fusion module is constructed to fuse the multi-scale feature information. Finally, the mapping between tool wear and multi-sensor data is realized through the feature information obtained by residual connection and full connection layer. By comparing the multi-scale convolutional neural network with different attention mechanisms, the experiments demonstrated the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The weigh-in-motion (WIM) system weighs the entire vehicle by identifying the dynamic forces of each axle of the vehicle on the road. The load of each axle is very important to detect the total weight of the vehicle. Different drivers have different driving behaviors, and when large trucks pass through the weighing detection area, the driving state of the trucks may affect the weighing accuracy of the system. This paper proposes YOLOv3 network model as the basis for this algorithm, which uses the feature pyramid network (FPN) idea to achieve multi-scale prediction and the deep residual network (ResNet) idea to extract image features, so as to achieve a balance between detection speed and detection accuracy. In the paper, spatial pyramid pooling (SPP) network and cross stage partial (CSP) network are added to the original network model to improve the learning ability of the convolutional neural network and make the original network more lightweight. Then the detection-based target tracking method with Kalman filtering + RTS (rauch–tung–striebel) smoothing is used to extract the truck driving status information (vehicle trajectory and speed). Finally, the effective size of the vehicle in different driving states on the weighing accuracy is statistically analyzed. The experimental results show that the method has high accuracy and real-time performance in truck driving state extraction, can be used to analyze the influence of weighing accuracy, and provides theoretical support for personalized accuracy correction of WIM system. At the same time, it is beneficial for WIM system to assist the existing traffic system more accurately and provide a highway health management and effective decision making by providing reliable monitoring data. [ABSTRACT FROM AUTHOR]

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users' learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users' learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD). [ABSTRACT FROM AUTHOR]

Knowledge graph embedding (KGE) models have become popular means for making discoveries in knowledge graphs (e.g., RDF graphs) in an efficient and scalable manner. The key to success of these models is their ability to learn low-rank vector representations for knowledge graph entities and relations. Despite the rapid development of KGE models, state-of-the-art approaches have mostly focused on new ways to represent embeddings interaction functions (i.e., scoring functions). In this paper, we argue that the choice of other training components such as the loss function, hyperparameters and negative sampling strategies can also have substantial impact on the model efficiency. This area has been rather neglected by previous works so far and our contribution is towards closing this gap by a thorough analysis of possible choices of training loss functions, hyperparameters and negative sampling techniques. We finally investigate the effects of specific choices on the scalability and accuracy of knowledge graph embedding models. [ABSTRACT FROM AUTHOR]