Casing life prediction using Borda and support vector machine methods

Eight casing failure modes and 32 risk factors in oil and gas wells are given in this paper. According to the quantitative analysis of the influence degree and occurrence probability of risk factors, the Borda counts for failure modes are obtained with the Borda method. The risk indexes of failure modes are derived from the Borda matrix. Based on the support vector machine （SVM）, a casing life prediction model is established. In the prediction model, eight risk indexes are defined as input vectors and casing life is defined as the output vector. The ideal model parameters are determined with the training set from 19 wells with casing failure. The casing life prediction software is developed with the SVM model as a predictor. The residual life of 60 wells with casing failure is predicted with the software, and then compared with the actual casing life. The comparison results show that the casing life prediction software with the SVM model has high accuracy.

Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Remote sensing study of wetlands in the Pearl River Delta during 1995-2015 with the support vector machine method

In recent years, experienced rapid economic the Pearl River Delta has growth which may create a substantial burden to its ecology. In this study, the wetlands of the Pearl River Delta are investigated. Through the use of remote sensing methods, we analyze spatial and temporal variations of wetlands in this area over the past twenty years. The support vector machine （SVM） method is proven to be an effective approach for classifying the wetlands of the Pearl River Delta, and the total classifica- tion resolution reaches 94.94% with a Kappa coefficient exceeding 0.94, higher than other comparable analysis methods. Our results show that wetland areas were reduced by 36.9% during the past twenty years. The change detection analysis method shows that there was a 95.58% intertidal zone change to other land-use types, most of which （57.12%） was converted to construction land. In addition, farmland was reduced by 54.89% during the past twenty years, 47.19% of which was changed to construction land use. The inland water area increased 19.02%, but most of this growth （18.77%） was converted from the intertidal zone.

Enhancing the Linearity Characteristics of Photoelectric Displacement Sensor Based on Extreme Learning Machine Method

Photoelectric displacement sensors rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. If the sensor output is nonlinear, it will produce a whole assortment of problems. This paper presents a method to compensate the nonlinearity of the photoelectric displacement sensor based on the extreme learning machine （ELM） method which significantly reduces the amount of time needed to train a neural network with the output voltage of the optical displacement sensor and the measured input displacement to eliminate the nonlinear errors in the training process. The use of this proposed method was demonstrated through computer simulation with the experimental data of the sensor. The results revealed that the proposed method compensated the presence of nonlinearity in the sensor with very low training time, lowest mean squared error （MSE） value, and better linearity. This research work involved less computational complexity, and it behaved a good performance for nonlinearity compensation for the photoelectric displacement sensor and has a good application prospect.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long distances.In this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method.The differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),respectively.Moreover,the automatic method is capable of recognizing higher harmonic tweek sferics.The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method

To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method(SRSM) is proposed. SRSM integrates the advantages of support vector machine(SVM) and traditional response surface method(RSM), and utilizes experimental samples to construct a suitable response surface function(RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method(MCM); while SRSM(17.296 s) needs far less running time than MCM(10958 s) and RSM(9840 s). Therefore,under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.

Design of N-11-Azaartemisinins Potentially Active against Plasmodium falciparum by Combined Molecular Electrostatic Potential, Ligand-Receptor Interaction and Models Built with Supervised Machine Learning Methods

N-11-azaartemisinins potentially active against Plasmodium falciparum are designed by combining molecular electrostatic potential (MEP), ligand-receptor interaction, and models built with supervised machine learning methods (PCA, HCA, KNN, SIMCA, and SDA). The optimization of molecular structures was performed using the B3LYP/6-31G* approach. MEP maps and ligand-receptor interactions were used to investigate key structural features required for biological activities and likely interactions between N-11-azaartemisinins and heme, respectively. The supervised machine learning methods allowed the separation of the investigated compounds into two classes: cha and cla, with the properties εLUMO+1 (one level above lowest unoccupied molecular orbital energy), d(C6-C5) (distance between C6 and C5 atoms in ligands), and TSA (total surface area) responsible for the classification. The insights extracted from the investigation developed and the chemical intuition enabled the design of sixteen new N-11-azaartemisinins (prediction set), moreover, models built with supervised machine learning methods were applied to this prediction set. The result of this application showed twelve new promising N-11-azaartemisinins for synthesis and biological evaluation.

A REVIEW OF THE HISTORY OF CHINA'S MACHINE DESIGN METHODS AND THE PROSPECT

This paper examines the history of China’s machine design methods and its status quo. First of all, it discusses machine design methods in ancient China. (1)The design idea of creation by the intelligent and exposition by the ingenious. (2)The design principle of the dependence of workmanship on criteria. (3)A macroscopic view of the object of design. (4)The design method of manufacture emphasizing shape. (5)Technological requirements on excellent material and consummate skill. Second, it discusses machine design ideas in ancient China. (1)The system idea in machine design, (2)The idea of machine design.(3)The idea of standardization in machine design. (4)The idea of automation in machine design. Finally, it discusses the status quo and a look ahead of the theory of machine design in China.

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5＇ Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim＇s and Shekari＇s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5＇ model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5＇ approach.

Combined method of plastic work piece machining based on a pretreatment mechanical down

An analysis of polymer materials behavior under cutting forces load was presented.The analysis was accomplished taking into account the existence and interaction of micro cracks in material.On the basis of modeling representations of a polymeric material behavior at cutting the method of preliminary mechanical destruction a superficial layer of polymeric material was developed.The essence of the method consists in producing micro-cracks in form of blind holes on the upper layer of blanks before turning.The aim of this method is a plastic deformation zones creation under stresses interaction occurring at the adjacent crack apexes.Results of experimental researches of fabric-based laminate turning processing according to the offered method were submitted.The analysis of the received results confirms expediency of application of the given combined method and the decrease of a roughness on the processed surface of fabric-based laminate is testifying about it.

Development of a methodology for assessing the adequacy of electric power systems

This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies,which complicates their analysis.The methodology development is aimed at solving two main problems:(1)increase the adequacy of modeling the processes that occur in the electric power system and (2)enhance the computational efficiency of the adequacy assessment methodology.This study proposes a new mathematical model to minimize the power shortage and enhance the adequacy of modeling the processes.The model considers quadratic power transmission losses and network coefficients.The computational efficiency of the adequacy assessment methodology is enhanced using efficient random-number generators to form the calculated states of electric power systems and machine learning methods to assess power shortages and other reliability characteristics in the calculated states.

The Acoustic Attenuation Prediction for Seafloor Sediment Based on in-situ Data and Machine Learning Methods

Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has been extensively studied,there is still no consensus on the correlation between acoustic attenuation coefficient and sediment physical properties.Predicting the acoustic attenuation coefficient remains a challenging issue in sedimentary acoustic research.In this study,we propose a prediction method for the acoustic attenuation coefficient using machine learning algorithms,specifically the random forest(RF),support vector machine(SVR),and convolutional neural network(CNN)algorithms.We utilized the acoustic attenuation coefficient and sediment particle size data from 52 stations as training parameters,with the particle size parameters as the input feature matrix,and measured acoustic attenuation as the training label to validate the attenuation prediction model.Our results indicate that the error of the attenuation prediction model is small.Among the three models,the RF model exhibited the lowest prediction error,with a mean squared error of 0.8232,mean absolute error of 0.6613,and root mean squared error of 0.9073.Additionally,when we applied the models to predict the data collected at different times in the same region,we found that the models developed in this study also demonstrated a certain level of reliability in real prediction scenarios.Our approach demonstrates that constructing a sediment acoustic characteristics model based on machine learning is feasible to a certain extent and offers a novel perspective for studying sediment acoustic properties.

Scientific Successes of Machine Design Ideasin Ancient China

With its great achievements in science and technology, China's mechanical engineeringholds an extremely important standing in the history of world civilization. In the long years ofhistory, the Chinese nation has worked innumerable wonders of machines. This paper investigates thehistory of China's Inachine design methods and discusses the machine design ideas in ancient China:1). The system idea in machine design: 2) The idea of machine design: 3) The idea ofstandardization in machine design: 4) The idea of automation in machine design.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

Floods are widespread and dangerous natural hazards worldwide.It is essential to grasp the causes of floods to mitigate their severe effects on people and society.The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation.This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters,and used machine learning methods to assess flood susceptibility.The core urban area of the Yangtze River Delta served as a case study.Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods,to measure the spatial variability in flood susceptibility.The findings demonstrate that the extreme gradient boosting model outperformed the decision tree,support vector machine,and stacked models in evaluating flood susceptibility.Both climate change and human activity were found to act as catalysts for flooding in the region.Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake.Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity,indicating that climate change was the dominant factor influencing flood susceptibility in the region.By comparing the relationship between the indicators and flood susceptibility,the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region.This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.

Numerical modeling of SiC by low-pressure chemical vapor deposition from methyltrichlorosilane

The development of functional relationships between the observed deposition rate and the experimental conditions is an important step toward understanding and optimizing low-pressure chemical vapor deposition(LPCVD)or low-pressure chemical vapor infiltration(LPCVI).In the field of ceramic matrix composites(CMCs),methyltrichlorosilane(CH3 SiCl3,MTS)is the most widely used source gas system for SiC,because stoichiometric SiC deposit can be facilitated at 900°C–1300°C.However,the reliability and accuracy of existing numerical models for these processing conditions are rarely reported.In this study,a comprehensive transport model was coupled with gas-phase and surface kinetics.The resulting gas-phase kinetics was confirmed via the measured concentration of gaseous species.The relationship between deposition rate and 24 gaseous species has been effectively evaluated by combining the special superiority of the novel extreme machine learning method and the conventional sticking coefficient method.Surface kinetics were then proposed and shown to reproduce the experimental results.The proposed simulation strategy can be used for different material systems.

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

CFR辛烷值机常见故障原因及处理方法

通过深入学习CFR辛烷值机的操作规程、检测方法和操作经验积累,将长期在操作中遇到的故障、产生故障的原因、处理方法及日常维护进行了归纳和总结,希望从事辛烷值检测的人员有所启迪和收获。从而来延长设备的寿命、确保设备安全可靠性、辛烷值准确无误性。