A Data-Oriented Method to Optimize Hydraulic Fracturing Parameters of Tight Sandstone Reservoirs

Based on the actual data collected from the tight sandstone development zone, correlation analysis using theSpearman method was conducted to determine the main factors influencing the gas production rate of tightsandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracturepropagation and production was completed. Based on data analysis, the hydraulic fracture parameters wereoptimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influenceof geological and engineering factors in the X1 and X2 development zones in the study area differs significantly.Therefore, it is challenging to adopt a uniform development strategy to achieve rapid production increase. Thedata analysis reveals that the variation in gas production rate is primarily affected by the reservoir thickness andpermeability parameters as geological factors. On the other hand, the amount of treatment fluid and proppantaddition significantly impact the gas production rate as engineering factors. Among these factors, the influence ofgeological factors is more pronounced in block X1. Therefore, the main focus should be on further optimizing thefracturing interval and adjusting the geological development well location. Given the existing well location, thereis limited potential for further optimizing fracture parameters to increase production. For block X2, the fracturingparameters should be optimized. Data screening was conducted to identify outliers in the entire dataset, and adata-driven fracturing parameter optimization method was employed to determine the basic adjustment directionfor reservoir stimulation in the target block. This approach provides insights into the influence of geological,stimulation, and completion parameters on gas production rate. Consequently, the subsequent fracturing parameteroptimization design can significantly reduce the modeling and simulation workload and guide field operations toimprove and optimize hydraulic fracturing efficiency.

Approximate Conditional Likelihood for Generalized Linear Models with General Missing Data Mechanism

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate.Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

REMUDA: A Practical Topology Control and Data Forwarding Mechanism for Wireless Sensor Networks

In wireless sensor networks, topology control plays an important role for data forwarding efficiency in the data gathering applications. In this paper, we present a novel topology control and data forwarding mechanism called REMUDA, which is designed for a practical indoor parking lot management system. REMUDA forms a tree-based hierarchical network topology which brings as many nodes as possible to be leaf nodes and constructs a virtual cluster structure. Meanwhile, it takes the reliability, stability and path length into account in the tree construction process. Through an experiment in a network of 30 real sensor nodes, we evaluate the performance of REMUDA and compare it with LEPS which is also a practical routing protocol in TinyOS. Experiment results show that REMUDA can achieve better performance than LEPS.

Analysis of drug use law and mechanism of prostate cancer based on data mining and network pharmacology

Objective: Excavate the medication rule of traditional Chinese medicine in the treatment of prostate cancer, and predicting the biomolecular level mechanism of high-frequency drug compatibility. Methods: Relevant documents in CNKI, Wanfang Medical Network and VIP Chinese Biomedical Periodical Database Pubmed, EMbase were collected and collated systematically. Frequency statistics, association rule analysis and new party mining were carried out using TCMISSV2.5. BATMAN-TCM was used to analyze the interaction relationship and related pathways between high-frequency drug targets. Results: Huangqi (Astragalus membranaceus) was the single drug most used of the 102prescriptions included in the standard. There are 6 pairs of combinations with high confidence in association rule analysis. System entropy cluster analysis resulted in 20 core drug combinations and 9 new prescriptions. Through KEGG pathway analysis of Huangqi, Fuling (Poria cocos), Gancao (Glycyrrhiza uralensis) and Dihuang (Rehmannia glutinosa), it was found that the number of potential targets of the neural active ligand receptor rented pathway and purine metabolism pathway was the largest. Conclusions: Prostate cancer is mainly treated with deficiency-tonifying drugs, which are combined with drugs for promoting blood circulation, removing blood stasis, clearing heat, promoting diuresis, detoxifying and resolving hard mass. The mechanism of action of high-frequency traditional Chinese medicine may be realized by interfering with the neuroactive ligand receptor interaction pathway and purine metabolism pathway.

Deformation and failure in nanomaterials via a data driven modelling approach

A data driven computational model that accounts for more than two material states has been presented in this work. Presented model can account for multiple state variables, such as stresses,strains, strain rates and failure stress, as compared to previously reported models with two states.Model is used to perform deformation and failure simulations of carbon nanotubes and carbon nanotube/epoxy nanocomposites. The model capability of capturing the strain rate dependent deformation and failure has been demonstrated through predictions against uniaxial test data taken from literature. The predicted results show a good agreement between data set taken from literature and simulations.

BFM:A Bus-Like Data Feedback Mechanism Between Graphics Processor and Host CPU

Graphics processors have received an increasing attention with the growing demand for gaming,video streaming,and many other applications.During the graphics rendering with OpenGL,host CPU needs the runtime attributes to move on to the next procedure of rendering,which covers almost all the function units of graphics pipeline.Current methods suffer from the memory capacity issues to hold the variables or huge amount of data parsing paths which can cause congestion on the interface between graphics processor and host CPU.This paper refers to the operation principle of commuting bus,and proposes a bus-like data feedback mechanism(BFM)to traverse all the pipeline stages and collect the run-time status data or execution error of graphics rendering,then send them back to the host CPU.BFM can work in parallel with the graphics rendering logic.This method can complete the data feedback ta.sk easily with only 0.6%increase of resource utilization and has no negative impact on performance,which also obtains 1.3 times speed enhancement compared with a traditional approach.

Video-based Person Re-identification Based on Distributed Cloud Computing

Person re-identification has been a hot research issues in the field of computer vision.In recent years,with the maturity of the theory,a large number of excellent methods have been proposed.However,large-scale data sets and huge networks make training a time-consuming process.At the same time,the parameters and their values generated during the training process also take up a lot of computer resources.Therefore,we apply distributed cloud computing method to perform person re-identification task.Using distributed data storage method,pedestrian data sets and parameters are stored in cloud nodes.To speed up operational efficiency and increase fault tolerance,we add data redundancy mechanism to copy and store data blocks to different nodes,and we propose a hash loop optimization algorithm to optimize the data distribution process.Moreover,we assign different layers of the re-identification network to different nodes to complete the training in the way of model parallelism.By comparing and analyzing the accuracy and operation speed of the distributed model on the video-based dataset MARS,the results show that our distributed model has a faster training speed.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm

A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.

Global road traffic injury statistics:Challenges,mechanisms and solutions

High-quality data are the foundation to monitor the progress and evaluate the effects of road traffic injury prevention measures.Unfortunately,official road traffic injury statistics delivered by governments worldwide,are often believed somewhat unreliable and invalid.We summarized the reported problems concerning the road traffic injury statistics through systematically searching and reviewing the literature.The problems include absence of regular data,under-reporting,low specificity,distorted cause spectrum of road traffic injury,inconsistency,inaccessibility,and delay of data release.We also explored the mechanisms behind the problematic data and proposed the solutions to the addressed challenges for road traffic statistics.

Mechanical movement data acquisition method based on the multilayer neural networks and machine vision in a digital twin environment[version 1;peer review:2 approved]

Background:Digital twin requires virtual reality mapping and optimization iteration between physical devices and virtual models.The mechanical movement data collection of physical equipment is essential for the implementation of accurate virtual and physical synchronization in a digital twin environment.However,the traditional approach relying on PLC(programmable logic control)fails to collect various mechanical motion state data.Additionally,few investigations have used machine visions for the virtual and physical synchronization of equipment.Thus,this paper presents a mechanical movement data acquisition method based on multilayer neural networks and machine vision.Methods:Firstly,various visual marks with different colors and shapes are designed for marking physical devices.Secondly,a recognition method based on the Hough transform and histogram feature is proposed to realize the recognition of shape and color features respectively.Then,the multilayer neural network model is introduced in the visual mark location.The neural network is trained by the dropout algorithm to realize the tracking and location of the visual mark.To test the proposed method,1000 samples were selected.Results:The experiment results shows that when the size of the visual mark is larger than 6mm,the recognition success rate of the recognition algorithm can reach more than 95%.In the actual operation environment with multiple cameras,the identification points can be located more accurately.Moreover,the camera calibration process of binocular and multi-eye vision can be simplified by the multilayer neural networks.Conclusions:This study proposes an effective method in the collection of mechanical motion data of physical equipment in a digital twin environment. Further studies are needed to perceive posture and shape data of physical entities under the multi-camera redundant shooting.