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.

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.

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.

Feedback on a shared big dataset for intelligent TBM PartⅠ:Feature extraction and machine learning methods

This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.

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.

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.

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.

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.

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Previous studies have revealed that patients with hypertrophic cardiomyopathy(HCM)exhibit differences in symptom severity and prognosis,indicating potential HCM subtypes among these patients.Here,793 patients with HCM were recruited at an average follow-up of 32.78±27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features.Furthermore,we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data.Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings.Consequently,two subtypes characterized by different clinical outcomes were identified in HCM.Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course,while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression.Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities.Furthermore,the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction.By employing echocardiography and genetic screening for the 46 genes,HCM can be classified into two subtypes with distinct clinical outcomes.

The Coordinated Influence of Indian Ocean Sea Surface Temperature and Arctic Sea Ice on Anomalous Northeast China Cold Vortex Activities with Different Paths during Late Summer

The Northeast China cold vortex(NCCV)during late summer(from July to August)is identified and classified into three types in terms of its movement path using machine learning.The relationships of the three types of NCCV intensity with atmospheric circulations in late summer,the sea surface temperature(SST),and Arctic sea ice concentration(SIC)in the preceding months,are analyzed.The sensitivity tests by the Community Atmosphere Model version 5.3(CAM5.3)are used to verify the statistical results.The results show that the coordination pattern of East Asia-Pacific(EAP)and Lake Baikal high pressure forced by SST anomalies in the North Indian Ocean dipole mode(NIOD)during the preceding April and SIC anomalies in the Nansen Basin during the preceding June results in an intensity anomaly for the first type of NCCV.While the pattern of high pressure over the Urals and Okhotsk Sea and low pressure over Lake Baikal during late summer-which is forced by SST anomalies in the South Indian Ocean dipole mode(SIOD)in the preceding June and SIC anomalies in the Barents Sea in the preceding April-causes the intensity anomaly of the second type.The third type is atypical and is not analyzed in detail.Sensitivity tests,jointly forced by the SST and SIC in the preceding period,can well reproduce the observations.In contrast,the results forced separately by the SST and SIC are poor,indicating that the NCCV during late summer is likely influenced by the coordinated effects of both SST and SIC in the preceding months.

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.

Classification of Northeast China Cold Vortex Activity Paths in Early Summer Based on K-means Clustering and Their Climate Impact

The classification of the Northeast China Cold Vortex(NCCV)activity paths is an important way to analyze its characteristics in detail.Based on the daily precipitation data of the northeastern China(NEC)region,and the atmospheric circulation field and temperature field data of ERA-Interim for every six hours,the NCCV processes during the early summer(June)seasons from 1979 to 2018 were objectively identified.Then,the NCCV processes were classified using a machine learning method(k-means)according to the characteristic parameters of the activity path information.The rationality of the classification results was verified from two aspects,as follows:(1)the atmospheric circulation configuration of the NCCV on various paths;and(2)its influences on the climate conditions in the NEC.The obtained results showed that the activity paths of the NCCV could be divided into four types according to such characteristics as the generation origin,movement direction,and movement velocity of the NCCV.These included the generation-eastward movement type in the east of the Mongolia Plateau(eastward movement type or type A);generation-southeast longdistance movement type in the upstream of the Lena River(southeast long-distance movement type or type B);generationeastward less-movement type near Lake Baikal(eastward less-movement type or type C);and the generation-southward less-movement type in eastern Siberia(southward less-movement type or type D).There were obvious differences observed in the atmospheric circulation configuration and the climate impact of the NCCV on the four above-mentioned types of paths,which indicated that the classification results were reasonable.

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.

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.

Performance prediction of magnetorheological fluid‐based liquid gating membrane by kriging machine learning method

Smart liquid gating membrane is a responsive structural material as a pressure-driven system that consists of solid membrane and dynamic liquid,responding to the external field.An accurate prediction of rheological and mechanical properties is important for the designs of liquid gating membranes for various applications.However,high predicted accuracy by the traditional sequential method requires a large amount of experimental data,which is not practical in some situations.To conquer these problems,artificial intelligence has promoted the rapid development of material science in recent years,bringing hope to solve these challenges.Here we propose a Kriging machine learning model with an active candidate region,which can be smartly updated by an expected improvement probability method to increase the local accuracy near the most sensitive search region,to predict the mechanical and rheolo-gical performance of liquid gating system with an active minimal size of ex-perimental data.Besides this,this new machine learning model can instruct our experiments with optimal size.The methods are then verified by liquid gating membrane with magnetorheological fluids,which would be of wide interest for the design of potential liquid gating applications in drug release,microfluidic logic,dynamic fluid control,and beyond.

System reliability-based robust design of deep foundation pit considering multiple failure modes

Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering.However,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main challenges.To address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,China.The proposed method included two parts:system reliability model and robust design method.Back Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability analysis.The common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system reliability.Furthermore,based on the system reliability model,a robust design method is developed.This method aims to find cost-effective design points.To solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is adopted.The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm.The proposed method has a good performance in locating the balanced design point between safety and construction cost.Moreover,the proposed method can provide design points with reasonable stiffness distribution.

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

Agricultural robots are flexible to obtain ambient information across large areas of farmland. However, it needs to face two major challenges: data compression and filtering noise. To address these challenges, an encoder for ambient data compression, named Tiny-Encoder, was presented to compress and filter raw ambient information, which can be applied to agricultural robots. Tiny-Encoder is based on the operation of convolutions and pooling, and it has a small number of layers and filters. With the aim of evaluating the performance of Tiny-Encoder, different three types of ambient information (including temperature, humidity, and light) were selected to show the performance of compressing raw data and filtering noise. In the task of compressing raw data, Tiny-Encoder obtained higher accuracy (less than the maximum error of sensors ±0.5°C or ±3.5% RH) and more appropriate size (the largest size is 205 KB) than the other two auto-encoders based convolutional operations with different compressed features (including 20, 60, and 200 features). As for filtering noise, Tiny-Encoder has comparable performance with three conventional filtering approaches (including median filtering, Gaussian filtering, and Savitzky-Golay filtering). With large kernel size (i.e., 5), Tiny-Encoder has the best performance among these four filtering approaches: the coefficients of variation with the large kernel (i.e., 5) were 8.6189% (temperature), 10.2684% (humidity), 57.3576% (light), respectively. Overall, Tiny-Encoder can be used for ambient information compression applied to microcontrollers in agricultural information acquisition robots.