Search

Summary: During horizontal drilling operations, real‐time prediction of drilling pressure during the drilling process can help the drilling team cope with the complex and changing working environment downhole, adjust the parameters of the drilling rig promptly, make correct decisions, reduce the probability of drilling accidents, and avoid affecting the duration and cost of the project. This study provides a method for real‐time prediction of the drilling pressure of horizontal drilling rigs. A deep learning model based on a convolutional Transformer is trained for accurate real‐time prediction by extracting real‐time operating data of the horizontal drilling rig from the data acquisition system. The method proposed in this study can be a useful tool to improve the performance of horizontal drilling rigs and can assist the drilling team in operating horizontal drilling rigs. The results of the case study show that: (1) the proposed convolutional Transformer model provides reliable real‐time prediction with an MAE of 0.304 MPa and an RMSE of 0.508 MPa; (2) the proposed method can quickly and accurately predict the trend of drilling pressure change in the next period based on the current change of drilling pressure, and grasp the dynamics of drilling pressure of horizontal drilling rigs in advance. Further research could focus on assisted decision‐making and intelligent optimization to provide solutions for preventing drilling accidents and improving horizontal rig performance based on the prediction. [ABSTRACT FROM AUTHOR]

An operational scheme for predicting the symmetric R30 and R50 of tropical cyclones (TCs) in the western North Pacific was developed using a statistical regression method and track pattern clustering (four clusters). The statistical–dynamical model employs multiple linear regressions of two to eight variables at each cluster and forecast lead time. The dependent variable for prediction was the change in the 5‐kt wind radius (R5)—a proxy of TC size—relative to the initial time. The performance of the model was compared for the training (2008–2016) and testing (2017–2018) periods. The effect of clustering on TC size prediction was evaluated by comparing the performance of the non‐clustering and clustering models. The clustering model improved the prediction of TC size by 3%–24% at all lead times during the training period, especially with a significant improvement of up to 43% in Cluster 2. In Cluster 2, because most TCs tend to develop strongly and continue to increase in size, it greatly reduced the variability in TC size through clustering, allowed for smarter predictor selection, and ultimately improved TC size prediction. In the real‐time R30 and R50 predictions for the 2017 and 2018 TCs, the error of the clustered model was 18%–19% less than that of the non‐clustered model. The analysis results revealed that the real‐time prediction errors of the current model increase when the TC tracks are difficult to classify into specific clusters, the predicted environments and TC tracks are inaccurate, and the size and intensity of a TC rapidly increase. [ABSTRACT FROM AUTHOR]

Long non‐coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA–mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real‐time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML‐based lncRNA–mRNA target prediction model‐ 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non‐interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease‐specific lncRNA–mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA–mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred. [ABSTRACT FROM AUTHOR]

The comprehensive effect of multiple factors that include the geometric characteristics, load status, service characteristics, and failure mechanism will affect the safety of bridge crane structure. To evaluate the security of the bridge crane structure, the real‐time prediction method of fatigue life of the bridge structure based on digital twin is proposed. The specific type of general bridge crane is selected as the physical entity of the research object, and the information acquisition system is utilized to get the current service status information about the physical entity. On this basis, combined with the historical service information and inherent information of physical entity, the fuzzy database is established. Meanwhile, twin data are formed by the clear quantification of fuzzy information and data processing technology. In accordance with structural characteristic and work cycle process of bridge crane, the analytical models of load, strength, defect, and fatigue life are established, respectively. The multi‐theoretical calculation model is completed by encapsulating the analysis models and transmitting information, and then the main factors affecting the fatigue life of bridge crane structure are determined. With that, the comprehensive evaluation coefficient is calculated by the fuzzy comprehensive evaluation theory. The response relationship between the information data and the fatigue life of bridge crane structure is described by the Kriging surrogate model constructed with experimental design. Real‐time prediction of fatigue life of bridge crane structure is realized in a virtual space to depict the life cycle process. Taking QD20/10 t × 43 m × 12 m general bridge crane as an example, the feasibility and applicability of the proposed method are verified, which provides a strong theoretical basis for dependable service and timely scrapping of cranes. [ABSTRACT FROM AUTHOR]

In this study, by using generator electrical output power and rotor speed, a new algorithm for out‐of‐step prediction is proposed. In this online method, after fault clearance, the trend of generator output power will be predicted using sinusoidal variable frequency function curve fitting. Finally, time‐domain equal area criterion (TDEAC) is used to evaluate the transient stability. The mathematical proofing of TDEAC and its validity in large‐scale systems are presented too. Real‐time prediction, fast calculation, using only local parameters and estimation of the remaining time until instability are some of the proposed method's advantages. The performance of this algorithm is examined on IEEE 39‐bus New England and Iran electrical power network systems. The results show that the proposed method represents a valuable alternative for out‐of‐step protection possessing fast and high‐efficiency performance. The simulations are carried out using the Power Factory® software and the proposed algorithm has been developed in MATLAB. [ABSTRACT FROM AUTHOR]

By means of a novel time-dependent cumulated variation penalty function, a new class of real-time prediction methods is developed to improve the prediction accuracy of time series exhibiting irregular periodic patterns: in particular, the breathing motion data of the patients during robotic radiation therapy. It is illustrated that for both simulated and empirical data involving changes in mean, trend, and amplitude, the proposed methods outperform existing forecastingmethods based on support vector machines and artificial neural network in terms of prediction accuracy. Moreover, the proposed methods are designed so that real-time updates can be done efficiently with O(1) computational complexity upon the arrival of a new signalwithout scanning the old data repeatedly. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study proposes an algorithm that combines ensemble numerical weather‐prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble‐weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium‐Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper‐parameters of SIS on forecast performance are also presented in this study. [ABSTRACT FROM AUTHOR]

Abstract: Based on the partial least squares (PLS) method, an adaptive predictive PLS (AP‐PLS) method was developed for sensitive adaptation to process changes using large‐scale data (Big Data) from chemical processes. Utilizing data sets of fluidized catalytic cracking (FCC) and residue FCC (RFCC) processes as the basis, the AP‐PLS method was developed and its prediction ability was compared with the simple PLS method. The required parameters for the prediction model of the FCC and RFCC processes are readily available from reported process data using time‐varying model updating by the AP‐PLS method. The prediction results were compared with the simple PLS method in terms of average deviations from the real process data. This approach can be used for reasonably accurate prediction of product variables and can adapt to process changes in large‐scale chemical processes such as FCC and RFCC. [ABSTRACT FROM AUTHOR]