No abstract

The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

No abstract

No abstract

With the development of 5G, the advancement of basic infrastructure has led to considerable development in related research and technology. It also promotes the development of various smart devices and social platforms. More and more people are now using smart devices to post their reviews right after something happens. In order to keep pace with this trend, we propose a method to analyze users’ sentiment by using their text data. When analyzing users’ text data, it is noted that a user’s review may contain many aspects. Traditional text classification methods used by smart devices, however, usually ignore the importance of multiple aspects of a review. Additionally, most algorithms usually ignore the network structure information between the words in a sentence and the sentence itself. To address these issues, we propose a novel dual-level attention-based heterogeneous graph convolutional network for aspect-based sentiment classification which minds more context information through information propagation along with graphs. Particularly, we first propose a flexible HIN (heterogeneous information network) framework to model the user-generated reviews. This framework can integrate various types of additional information and capture their relationships to alleviate semantic sparsity of some labeled data. This framework can also leverage the full advantage of the hidden network structure information through information propagation along with graphs. Then, we propose a dual-level attention-based heterogeneous graph convolutional network (DAHGCN), which includes node-level and type-level attentions. The attention mechanisms can analyze the importance of different adjacent nodes and the importance of different types of nodes for the current node. The experimental results on three real-world datasets demonstrated the effectiveness and reliability of our model.

The risk assessment of environmental conflicts has been an integral part of measuring the operability of projects as well as the happiness of the affected population. With the current situation of environmental crises, environmental conflict has an effect on society. In order to maintain the stable development of society, we used the fuzzy comprehensive evaluation method and the analytic hierarchy process to study the risk of a population being affected by a project producing environmental pollution. Furthermore, we provided an approach that had the potential to quantify as well as give a risk assessment of the environmental impact. The results classified 0.397, 0.202, and 0.295 as medium, relatively high, and highest risk, respectively. We deemed that the conflict mode between people, government, and enterprise is actually the confrontation between benefit gainers and benefit losers to a great extent. In subtle ways, this conflict mode has several roles. The solution of environmental conflicts lies in how the roles of government and enterprises change and how the interests of the public are considered. Environmental risks can be safely mitigated without violence by managing relationships between people, governments and businesses.

No abstract

Clustering is an important data analysis technique and it widely used in many field such as data mining, machine learning and pattern recognition. Ant colony optimization clus tering is one of the popular partition algorithm. However, in mutidimensional search space, its results is usually ordinary as the disturbing of redundant information. To address the problem, this paper presents MD-ACO clustering algorithm which improves the ant structure to implement attribute reduction. Four real data sets from VCI machine learning repository are used to evaluate MD-ACO with ACO. The results show that MD-ACO is more competitive.