Research on Stochastic Resonance Detection Method for Periodic Signals under Low SNR and α-Stable NoiseRead the full article
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Interactive Teaching System of Basketball Action in College Sports Based on Online to Offline Mixed Teaching Mode
The mixed teaching mode can be used to improve students’ academic performance. In this study, an interactive teaching system of basketball action in college sports based on online to offline mixed teaching mode is developed. The system is mainly comprised of teacher function module, student function module, and system analysis module. In the teacher function module, online to offline mixed teaching mode is introduced to realize the interactive teaching of basketball action in college sports in the form of organic combination of microclass, massive open online course, and traditional classroom teaching. The student function module is mainly used to manage the information related to students learning basketball actions. The system analysis module uses the data mining model based on multiant colony clustering combination algorithm to obtain the learners’ behavior data and then designs a targeted interactive teaching course for basketball action. After testing, it was concluded that the designed system can improve the students’ mastery of basketball movement in sports and can be applied to improve student’s academic performance.
Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities
In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities. Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior. In order to prove the effectiveness and efficiency of the proposed algorithm, the algorithm is compared with the other two network abnormal behavior detection methods, and the results prove that the proposed method can effectively detect the abnormal access behavior in ideological and political MOOCs.
The Bidirectional Information Fusion Using an Improved LSTM Model
The information fusion technology is of great significance in intelligent systems. At present, the modern coal-fired power plant has the fully functional sensor network. However, many data that are important for the operation of a power plant, such as the coal quality, cannot be directly obtained. Therefore, the information fusion technology needs to be introduced to obtain the implied information of the power plant. As a practical application, the soft measurement of coal quality is taken as the research object. This paper proposes an improved LSTM model combined with the bidirectional deep fusion, alertness mechanism, and parameter self-learning (DFAS-LSTM) to realize online soft computing for the coal quality analyses of industries and elements. First, a latent structure model is established to preprocess the noisy and redundant sensor network data. Second, an alertness mechanism is proposed and the self-learning method of the activation function parameters is used for the data feature extraction. Third, a deeply bidirectional fusion layer is added to the long short-term memory neural network model to solve the problem of the insufficient accuracy and the weak generalization. Using the historical data of the sensor network, the DFAS-LSTM model is established. Then, the online data of the sensor network is input to the DFAS-LSTM model to implement the online coal quality analyses. Experiment shows that the accuracy of the coal quality analyses is increased by 1%–2.42% compared to the traditionally bidirectional LSTM.
Deep Field-Aware Interaction Machine for Click-Through Rate Prediction
Modeling feature interactions is of crucial importance to predict click-through rate (CTR) in industrial recommender systems. Because of great performance and efficiency, the factorization machine (FM) has been a popular approach to learn feature interaction. Recently, several variants of FM are proposed to improve its performance, and they have proven the field information to play an important role. However, feature-length in a field is usually small; we observe that when there are multiple nonzero features within a field, the interaction between fields is not enough to represent the feature interaction between different fields due to the problem of short feature-length. In this work, we propose a novel neural CTR model named DeepFIM by introducing Field-aware Interaction Machine (FIM), which provides a layered structure form to describe intrafield and interfield feature interaction, to solve the short-expression problem caused by the short feature-length in the field. Experiments show that our model achieves comparable and even materially better results than the state-of-the-art methods.
Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning
The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.
Local Cultural IP Development and Cultural Creative Design Based on Big Data and Internet of Things
Relying on the development of cultural tourism resources and the development of cultural tourism industry to achieve regional industrial revitalization is an important way of implementing postdisaster reconstruction in areas suffering from major natural disasters. To this end, this article proposes a local cultural IP development and cultural creative design method based on big data and the Internet of Things to explore new ideas for postdisaster reconstruction in such areas. First, we collect traditional and modern cultural element data and carry out data cleaning and processing through the Internet of Things. Second, we use data mining to perform multilayer collaborative processing on regional cultural data based on ontology modeling and tensor decomposition. Based on our approach, local cultural categories can be effectively screened and filtered out. Finally, we establish a cultural IP development model based on the Internet of Things and verify the validity and applicability of the model through system testing and simulation analysis.