Prediction and Analysis of PDC Bit Wear in Conglomerate Layer with Machine Learning and Finite-Element Method

Polycrystalline diamond compact (PDC) bits experience a serious wear problem in drilling tight gravel layers. To achieve efficient drilling and prolong the bit service life, a simplified model of a PDC bit with double cutting teeth was established by using finite-element numerical simulation technology, and the rock-breaking process of PDC bit cutting teeth was simulated using the Archard wear principle. The numerical simulation results of the wear loss of the PDC bit cutting teeth, such as the caster angle, temperature, linear velocity, and bit pressure, as well as previous experimental research results, were combined into a training dataset. Then, machine learning methods for equal-probability gene expression programming (EP-GEP) were used. Based on the accuracy of the training set, the effectiveness of this method in predicting the wear of PDC bits was demonstrated by verifying the dataset. Finally, a prediction dataset was established by a Latin hypercube experiment and finite-element numerical simulation. Through comparison with the EP-GEP prediction results, it was verified that the prediction accuracy of this method meets actual engineering needs. The results of the sensitivity analysis method for the gray correlation degree show that the degree of influence of bit wear is in the order of temperature, back dip angle of the PDC cutter, linear speed, and bit pressure. These results demonstrate that when an actual PDC bit is drilling hard strata such as a conglomerate layer, after the local high temperature is generated in the formation cut by the bit, appropriate cooling measures should be taken to increase the bit pressure and reduce the rotating speed appropriately. Doing so can effectively reduce the wear of the bit and prolong its service life. This study provides guidance for predicting the wear of a PDC bit when drilling in conglomerate, adjusting drilling parameters reasonably, and prolonging the service life of the bit.

Evaluation Algorithm of Ecological Energy-Saving Effect of Green Buildings Based on Gray Correlation Degree

The environmental protection attribute and energy-saving level of green buildings cannot be described by the traditional evaluation model. In order to solve the above problems, a new ecological energy-saving effect evaluation algorithm of green buildings based on gray correlation degree is designed. Based on the framework of building energy-saving index system, the environmental protection evaluation standards are divided and the results are used to screen the energy-saving indexes, so as to complete the establishment of green building ecological energy-saving index system and standards. Then, the evaluation set is established, and the evaluation scale of each layer of indicators is accurately located according to the weight value of each index. On this basis, the membership matrix is constructed. By calculating the index weight and determining the fuzzy synthesis operator, the rating process of the algorithm is improved and the analysis of the evaluation algorithm of environmental protection and energy conservation indicators of green building materials based on gray correlation degree is realized. The experimental results show that the designed algorithm has good stability of the fitting curve, can save energy, and has low cost.

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

Neural Network: An Improved FCM for Multimodal Cultural Data Analysis

With the development of cloud computing, big data, and artificial intelligence (AI) technology, there is a growing interest in “cultural analysis.” Cultural analysis requires different types of data such as texts, pictures, and videos. The richness and differences of resources in the cultural field lead to diverse modalities of cultural data. Traditional text analysis methods can no longer meet the data analysis needs of current multimedia cultural resources. This article starts from cultural data’s feature information to solve the heterogeneity problem faced by massive multimodal cultural data analysis. It analyzes it from geography, time, art, and thematic character, classified and aggregated to form a multimodal cultural feature information matrix. The corresponding correlation measurement methods for different matrices from the above dimensions are proposed, solved in turn, and substituted into the optimized training back propagation (BP) neural network to obtain the final correlation degree. The improved fuzzy C-means (FCM) clustering algorithm is used to aggregate the high correlation cultural data based on the degree. The algorithm proposed in this study is compared with the existing algorithm. The experimental results show that the optimized BP neural network is at least 58% more accurate than the current method for calculating different matrices’ correlation degrees. In terms of accuracy, the improved fuzzy C-means algorithm effectively reduces the random interference in the selection of the initial clustering center, which is significantly higher than other clustering algorithms.

Big Data Analysis of e-Commerce Efficiency and Its Influencing Factors of Agricultural Products in China

At present, the e-commerce industry of agricultural products plays a pivotal role in promoting income growth and helping rural revitalization. This paper collected relevant data in the recent 8 years (2012 to 2019) and used the DEA model and Tobit model to analyze the correlation degree between the efficiency and various influencing factors in China. DEA analysis results show that, in recent years, three efficiencies are quite different: the comprehensive efficiency and scale efficiency show an upward trend, while the pure technical efficiency remains at a high level. Tobit model results show that the number of urban Internet users, rural Internet users, logistics practitioners, the development of national economy are negatively correlated with e-commerce efficiency; the length of traffic construction has no significant correlation; the level of agricultural mechanization has a significant positive correlation. Hence, the paper puts forward four suggestions.

Operation analysis of C3-MR process cold box by grey system theory

In this paper, the problems of high refrigerant line differential pressure and uneven distribution of cold energy in cold box regulation under C3-MR process are studied. Five reasons are predicted by engineering performance. Using gas chromatography experiment and grey system pure mathematics analysis, it is determined that the main causes of the problem are unreasonable distribution ratio of each group of mixed refrigerants and disordered latent heat of vaporization of refrigerants. Furthermore, the grey system model is used to study: 1. grey relation analysis model shows that the correlation degree of T3 temperature measuring point is 0.8552, which is the only main factor. The abnormal working condition is determined by the project to be caused by incorrect proportion of N2 components. 2. According to GM(1,N) model, the driving term of T3 temperature measuring point is 3.8304, which needs to be supplemented with N2 component to eliminate the problem. 3. After adding N2 to 10% (mol component), abnormal working conditions disappeared. The GM(1,N) model is used again to verify that the difference of driving results is small, the average relative error is 24.91%, and the accuracy of the model is in compliance.

Synergy and Correlation Optimization Analysis of Innovation System and Institutional Governance System from the Perspective of Cluster Ecosystem

Innovation and institutional governance are the key enabling factors of cluster ecosystem development. Its synergistic effects play an important role in enhancing ecosystem competitiveness. In this paper, pseudocode language is applied to cluster ecosystem cooperative model reasoning. The coordination and optimization of the innovation system and institutional governance system were studied in a biomedical cluster. Besides, Pearson algorithm was used to test the correlation degree of elements in three Chinese biomedical clusters. The results show that, in Zhangjiang and Nanchang biomedical clusters, the synergistic correlation coefficient between the innovation system and the institutional governance system fluctuates around 0.8. However, in Tonghua biomedical cluster, the synergy correlation coefficient fluctuated around -0.2. The fluctuation range between the two clusters was large. After adjusting the range of order parameters, the rank of synergy trend was Zhangjiang > Nanchang > Tonghua. Finally, further analysis shows that Zhangjiang and Nanchang biomedical clusters can achieve the optimal synergy state by adjusting innovation and institutional governance, but Tonghua cannot. Therefore, the collaboration between the innovation system and institutional governance system provides some reference for the high-quality development of the cluster ecosystem.

Water Quality Evaluation of Wenyu River in Beijing by Matter Element Model

River water quality is an important indicator for identifying river changes and analyzing river health, and has an important impact on the ecological environment of the river basin. In this paper, the matter-element analysis method based on the coupling weight method is used to evaluate the water environment of the water quality measured data of Wenyu River in 2019, which provides a reference for water quality management and protection. Through the establishment of the object element to be evaluated, the classical domain, the section domain, the normalization of the evaluation standard, and the measured data, three representative indicators such as DO, NH3-N, and CODcr are selected as the object element to be evaluated. The standard value corresponding to the water quality standards of Grade I to V is the classic domain. The weight of river indicators is determined by the coupling of the ordinary objective weighting method and the multiple super-scale weighting method. After the weight is determined, the correlation degree is calculated and the matter-element analysis model for water quality evaluation is established. The results showed that the water quality of the Wenyu River in May 2019 was still mainly Grade V water, which was in line with the actual water quality situation. It shows that the method meets the feasibility and practicability in water quality evaluation and is relatively reliable.

A Manufacturing SCOS Model (MSCOS) Based on the Similarity of Parameter Sequences Between Tasks and Service Composition

Service composition and optimal selection (SCOS) is a core issue in cloud manufacturing (CMfg) when integrating distributed manufacturing services for complex manufacturing tasks. Generally, a set of recommended task parameter sequences (Tps) will be given when publishing manufacturing tasks. The similarity between the service composition parameter sequence (SCps) and Tps also reflects the rationality of the service composition. However, various evaluation models based on QoS have been proposed, ignoring the rationality between the Tps and SCps. Considering the similarity of the Tps and SCps in an evaluation model, we propose a manufacturing SCOS framework called MSCOS. The framework includes two parts: an evaluation model and an algorithm for both optimization and selection. In the evaluation model, based on the numerical proximity and geometric similarity between the Tps and SCps, improving the technique for order preference by similarity to an ideal solution (TOPSIS) with the grey correlation degree (GC), we propose the GC&TOPSIS (GTOPSIS). In the optimization and selection algorithm, an improved flower pollination algorithm (IFPA) is proposed to achieve optimization and selection based on polyline characteristics between the fitness values in the population. Experiments show that the MSCOS evaluation effect and optimal selection offer better performance than commonly used algorithms.