Enhancing the resolution of sparse rock property measurements using machine learning and random field theory

The travel time of rock compressional waves is an essential parameter used for estimating important rock properties,such as porosity,permeability,and lithology.Current methods,like wireline logging tests,provide broad measurements but lack finer resolution.Laboratory-based rock core measurements offer higher resolution but are resource-intensive.Conventionally,wireline logging and rock core measurements have been used independently.This study introduces a novel approach that integrates both data sources.The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data.By combining machine learning with random field theory,the method allows for probabilistic predictions in regions with sparse data sampling.In this framework,12 parameters from wireline tests are used to predict trends in rock core data.The residuals are modeled using random field theory.The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual.By utilizing unconditional and conditional random field theories,this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates.This integrated approach optimizes the use of existing core and logging data.Its applicability is confirmed in an oil project in West China.

“Learning by Doing”教学模式的探索

"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。

Learning-by-doing教学模式在安全系统工程教学中的应用

安全系统工程课程是安全工程专业的专业基础课,其教学效果的好坏对后续课程的学习以及日后所从事的工作至关重要。因而该课程教学方法的运用选择值得深思。笔者分析了安全工程专业中安全系统工程课程的特点和教学中存在的问题,引用learning-by-doing的教学模式并在教学中加以应用,提出"通过授课得到答案——学会一个解,通过案例讨论得到方法——学会一个方法,通过实践模拟学会学习——学会找到个方法,通过总结学会融会贯通"的安全系统工程教学模式,收到了较好的教学效果。

“Flash动画设计”课程“Learning by doing”教学法探索

本文针对"Flash动画设计"的课程特点引入"Learningbydoing"教学理念,提出"简单实例掌握基本技能→案例讨论学会分析方法→模拟实践学会找到方法→总结经验融会贯通同时实现创新提高"的教学模式,在教学实践中取得了良好的效果。

基于Learning-by-doing的计算机系统结构课程改革

在计算机系统结构的课程教学中,引入由卡内基.梅隆大学提出的"Learning-by-doing"这一适用于工程教学的行之有效的先进教学理念是新教学模式的一种积极探索。文章围绕基于"Learning-by-doing"教学法的计算机系统结构课程改革实施的前期准备、遇到的问题,具体解决方案等环节进行了讨论和分析。

具有不完全技术外部性的随机Learning-by-Doing模型及解法

提出适用于随机Learning-by-Doing模型的"附加效用"值函数解法,并用此方法求解具有不完全技术外部性的随机learning-by-doing模型,得到了均衡时的经济增长路径、消费—资本比和值函数,讨论了技术外部性对私人资本回报率、消费倾向、均值经济增长率和个体福利的影响.

“嵌入式系统程序设计实习”教学改革——探索“Learning by Doing”教学模式

"Learning by Doing"是一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。

基于Learning-by-doing的不确定经济增长与财政政策研究

本文研究基于learning-by-doing的随机增长模型,得到了均衡时的经济增长路径、债券回报率、资产组合份额和消费-资本比,分析财政政策对长期经济增长、资产组合选择、个体消费倾向和个体福利的影响,探讨最优的财政政策。

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

一个多资本的Learning-by-doing模型

考虑一个多资本投入的一般Learning by doing模型 .该模型技术进步的增长率由经济发展过程内生地决定 .当技术对资本为递减规模回报时 ,该经济仅有唯一正的均衡点 .运用单调动力系统理论论证了此模型的稳定性问题 。

Learning by doing方法在移动平台应用开发课中的应用

"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。

Network Defense Decision-Making Based on Deep Reinforcement Learning and Dynamic Game Theory

Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.

A Summary of Some Teaching Methodologies as well as Some Linguistic and Learning Theories

This paper tries to summarize some main schools 0f teaching methodologies abroad and some main learning theories abroad. From this paper, we can know the main learning theories, the basic theories of them and the leading figures. It can help us understand the characteristics of each school of the teaching methodologies and learning theories.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

The Application of Reinforcement Theory in the Review Stage of English Teaching and Learning in Chinese Higher Vocational and Technical Colleges

Reinforcement theory is a behavioral psychology theory proposed by Skinner,which has been widely applied in various fields such as management and education.Positive reinforcement and negative reinforcement are the two types of reinforcement.By adopting these two different reinforcement methods appropriately,human behavior can develop in a positive direction.In the review stage of English teaching and learning in Chinese higher vocational and technical colleges,the use of different reinforcement methods based on various classes,individuals,conditions,and environments can effectively promote or change the behavior of teachers and students,thereby improving the effectiveness of the review.

Educational Practices in the Model of Music Learning Theory of E. Edwin Gordon： An Observational Research

This paper analyzes the supervision activity, to which educators and teachers enrolled with AIGAM （Gordon Italian Association for the Musical Learning） are subject to every year and intends to verify the application of those principles expressed in the learning model of the MLT （Music Learning Theory） developed by educational psychologist E. Edwin Gordon （1989, 1999, 2000, 2001, 2007） and promoted internationally by various institutions and organizations specifically accredited. It describes the influence of the videotaped supervision on the process, functions of monitoring, and evaluation of educational practices, starting with an empirical model that has guided the interventions in a study of supervision on training aimed at consolidating and developing professional skills in music education in early childhood. This paper sought to understand： the kind of practices, interactions, communications developing during an educational actions, the existence of a consistent relationship between the principles expressed in the MLT and their application, the type and benefits of supervision performed by of video recording on stakeholders in terms of change in professional behavior, and finally whether the active supervision could be comparable with other kinds of approaches.

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Learning by Doing Effect in North South Trade under the Global Value Chains： An Empirical Analysis of Various Industries in the U.S.

This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more output and increasing trade value in several industries in the U.S., because the U. S. is at the leading position in the division of labor by global value chains. The empirical evidence reveals that more international outsourcing, there will be more detailed division of labor, and the industry unit production time and production cost will show more declining trend year by year. This is consistent with that the global value chains and the outsourcing play more and more important roles in the international division of labor and economic growth in both developed and developing countries, and helps explain the integration of workforce across countries in the global value chains.