Research on Abnormal Detection Method of Data Flow Trend in Oil Drilling Engineering Early Warning

In this paper, by collecting and analysing the domestic and foreign oil drilling accident data and early warning technology, combined with the oil drilling process prediction and forecasting process deficiencies. Based on theoretical analysis and analysis of five typical accident modes in actual drilling rig engineering, the early warning method and train of thought of oil drilling engineering are established, and the index of early warning of accident is given. The sensitivity of various accident prediction indexes to the corresponding accidents is studied. On the basis of analysing the forecast signal and processing, the comprehensive model of accident early warning under multi-objective condition is established.

Research on Nonlinear Systems Modeling Methods Based on Neural Networks

Nonlinear systems widely exist in all fields of industrial production and are difficult to model because of complex non-linearity. Neural network is widely used in process prediction, fault detection and fault diagnosis of modern industry because of the nonlinear fitting ability. Due to various structures, there exists diversity in the performance of neural networks. However, only the appropriate network can improve the efficiency and safety in modelling nonlinear industrial process, which requires full consideration of the structure of neural network. In this study, several typical structures of neural networks are compared and analysed, and the performance differences caused by these structures are presented in detail. Finally, performance differences of neural networks with inconsistent structures are verified on several experiments. The results showed that neural networks with inconsistent structures were good at dealing with different types of nonlinear systems. Our work will provide a theoretical basis in accurately modeling the industrial production process, which is beneficial to nonlinear system control.

Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

Text-Aware Predictive Monitoring of Business Processes

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.