A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents

The relation triples extraction method based on table filling can address the issues of relation overlap and bias propagation. However, most of them only establish separate table features for each relationship, which ignores the implicit relationship between different entity pairs and different relationship features. Therefore, a feature reasoning relational triple extraction method based on table filling for technological patents is proposed to explore the integration of entity recognition and entity relationship, and to extract entity relationship triples from multi-source scientific and technological patents data. Compared with the previous methods, the method we proposed for relational triple extraction has the following advantages: 1) The table filling method that saves more running space enhances the speed and efficiency of the model. 2) Based on the features of existing token pairs and table relations, reasoning the implicit relationship features, and improve the accuracy of triple extraction. On five benchmark datasets, we evaluated the model we suggested. The result suggest that our model is advanced and effective, and it performed well on most of these datasets

Incremental feedforward collective pitch control method for wind turbines

In recent years, wind turbines are becoming larger, which will exacerbate the complexity of loads Complex load change affect the output power quality and wind turbine service life so that must be studied. Pitch control is usually used to reduce wind turbine load. In this paper, based on the Light Detection and Ranging (LiDAR) technology and incremental feedforward control theory, an incremental feedforward collective pitch controller is proposed. The controller can be directly superimposed on the traditional collective pitch controller so that the incremental pitch angle can fully compensate wind influence. The effectiveness of the controller is verified by multi-software platform joint simulation and hardware-in-the-loop experiment. The results show that the controller can effectively reduce the wind turbine power and load fluctuation when the variation trend of wind speed in the rotor plane estimate by LiDAR data is the same as the actual wind speed

Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering

Federated learning is a distributed machine learning technology, which realizes the balance between data privacy protection and data sharing computing. To protect data privacy, feder-ated learning learns shared models by locally executing distributed training on participating devices and aggregating local models into global models. There is a problem in federated learning, that is, the negative impact caused by the non-independent and identical distribu-tion of data across different user terminals. In order to alleviate this problem, this paper pro-poses a strengthened federation aggregation method based on adaptive OPTICS clustering. Specifically, this method perceives the clustering environment as a Markov decision process, and models the adjustment process of parameter search direction, so as to find the best clus-tering parameters to achieve the best federated aggregation method. The core contribution of this paper is to propose an adaptive OPTICS clustering algorithm for federated learning. The algorithm combines OPTICS clustering and adaptive learning technology, and can effective-ly deal with the problem of non-independent and identically distributed data across different user terminals. By perceiving the clustering environment as a Markov decision process, the goal is to find the best parameters of the OPTICS cluster without artificial assistance, so as to obtain the best federated aggregation method and achieve better performance. The reliability and practicability of this method have been verified on the experimental data, and its effec-tiveness and superiority have been proved

Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa

Text sentiment analysis, also known as opinion mining, is research on the calculation of people's views, evaluations, attitude and emotions expressed by entities. Text sentiment analysis can be divided into text-level sentiment analysis, sen-tence-level sentiment analysis and aspect-level sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis, which aims to predict the polarity of aspects. The research of pre-training neural model has significantly improved the performance of many natural language processing tasks. In recent years, pre training model (PTM) has been applied in ABSA. Therefore, there has been a question, which is whether PTMs contain sufficient syntactic information for ABSA. In this paper, we explored the recent DeBERTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem. DeBERTa is a kind of neural language model based on transformer, which uses self-supervised learning to pre-train on a large number of original text corpora. Based on the Local Context Focus (LCF) mechanism, by integrating DeBERTa model, we purpose a multi-task learning model for aspect-based sentiment analysis. The experiments result on the most commonly used the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset show that LCF mechanism with DeBERTa has significant improvement

The Combined Application of Urea and Fulvic Acid Solution Improved Maize Carbon and Nitrogen Metabolism

It has been reported that fulvic acid (FA) application improves soil structure and nutrient availability. However, the effects of combined application of urea (U) and FA solution on the photosynthesis and nitrogen metabolism in maize (Zea mays L.) have rarely been reported. In this study, pot experiments were conducted in 2017 and 2018, and the effects of combined application of urea and FA solution (U+FA) on soil available nutrient contents, maize endogenous hormone concentrations, carbon and nitrogen metabolism-related enzyme concentrations, maize yield, and nitrogen use efficiency (NUE) were researched. Compared with the U treatment, the maize yield and NUE in the U+FA treatment were significantly increased by 8.31% and 17.09 percentage points in 2017 and by 16.90% and 24.31 percentage points in 2018. At the jointing and 12-leaf (V12) stages of maize, soil NH4+ content increased by 139.32% and 12.08%, separately, in the U+FA treatment. At the V12 stage, the auxin, nitrate reductase, nitrite reductase, and glutamine synthetase concentrations in maize root were increased by 42.31%, 74.17%, 16.61%, and 45.60%, respectively, and the concentrations of pyruvate phosphate dikinase and phosphoenolpyruvate carboxylase in maize leave were increased by 29.40% and 42.96%, respectively, in the U+FA treatment. The combined application of urea and FA solution significantly improved soil nutrient availability, increased the concentrations of endogenous hormones in maize, stimulated the activities of enzymes related to nitrogen metabolism, promoted the photosynthetic carbon assimilation efficiency, and ultimately improved crop yield and NUE