A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Simulation Modeling and Analysis of Operator-Machine Ratio

Based on a simulation model of a semiconductor manufacturer, operator-machine ratio （OMR） analysis is made using work study and time study. Through sensitivity analysis, it is found that labor utilization decreases with the increase of lot size. Meanwhile, it is able to identify that the OMR for this company should be improved from 1 ： 3 to 1 ： 5. An application result shows that the proposed model can effectively improve the OMR by 33%.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

Control of seismic and operational vibrations of rotating machines using semi-active mounts

A dual isolation problem for rotating machines consists of isolation of housing structures from the machine vibrations and protection of machines during an earthquake to maintain their functionality. Desirable characteristics of machine mounts for the above two purposes can differ significantly due to difference in nature of the excitation and performance criteria in the two situations. In this paper, relevant response quantities are identified that may be used to quantify performance and simplified models of rotating machines are presented using which these relevant response quantities may be calculated. Using random vibration approach with a stationary excitation, it is shown that significant improvement in seismic performance is achievable by proper mount design. Results of shaking table experiments performed with a realistic setup using a centrifugal pump are presented. It is concluded that a solution to this dual isolation problem lies in a semi-active mount capable switching its properties from ‘operation-optimum’ to ‘seismic-optimum’ at the onset of a seismic event.

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Metal-Halide Perovskite Submicrometer-Thick Films for Ultra-Stable Self-Powered Direct X-Ray Detectors

Metal-halide perovskites are revolutionizing the world of X-ray detectors,due to the development of sensitive,fast,and cost-effective devices.Self-powered operation,ensuring portability and low power consumption,has also been recently demonstrated in both bulk materials and thin films.However,the signal stability and repeatability under continuous X-ray exposure has only been tested up to a few hours,often reporting degradation of the detection performance.Here it is shown that self-powered direct X-ray detectors,fabricated starting from a FAPbBr_(3)submicrometer-thick film deposition onto a mesoporous TiO_(2)scaffold,can withstand a 26-day uninterrupted X-ray exposure with negligible signal loss,demonstrating ultra-high operational stability and excellent repeatability.No structural modification is observed after irradiation with a total ionizing dose of almost 200 Gy,revealing an unexpectedly high radiation hardness for a metal-halide perovskite thin film.In addition,trap-assisted photoconductive gain enabled the device to achieve a record bulk sensitivity of 7.28 C Gy^(−1)cm^(−3)at 0 V,an unprecedented value in the field of thin-film-based photoconductors and photodiodes for“hard”X-rays.Finally,prototypal validation under the X-ray beam produced by a medical linear accelerator for cancer treatment is also introduced.

WEB-BASED VIRTUAL CNC MACHINE MODELING AND OPERATION

A CNC simulation system based on intemet for operation training of manufacturing facility and manufacturing process simulation is proposed. Firstly, the system framework and a rapid modeling method of CNC machine tool are studied under the virtual environment based on PolyTrans and CAD software. Then, a new method is proposed to enhance and expand the interactive ability of virtual reality modeling language（VRML） by attaining communication among VRML, JavaApplet, JavaScript and Html so as to realize the virtual operation for CNC machine tool. Moreover, the algorithm of material removed simulation based on VRML Z-map is presented. The advantages of this algorithm include less memory requirement and much higher computation. Lastly, the CNC milling machine is taken as an illustrative example for the prototype development in order to validate the feasibility of the proposed approach.

Support vector machine for prediction of siRNA silencing efficacy

In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （RNAi） effect is analyzed by a support vector machine （SVM） based algorithm relied on a basebase correlation （BBC） feature. The results show that the proposed algorithm has the highest area under curve （AUC） value （0. 73） of the receive operating characteristic （ROC） curve and the greatest r value （0. 43） of the Pearson＇s correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.

Methods for Integrating Energy Consumption and Environmental Impact Considerations into the Production Operation of Machining Processes

Energy consumption and environmental impact considerations of machining processes are viewed as important issues for the global trends towards sustainable manufacturing. The existing research of reducing energy consumption and environmental impacts of machining processes greatly focuses on design and planning activities, but is reasonably sparse for production operation activities. This paper explores a systematic methodology that incorporates energy consumption and environmental impact considerations into the production operation of machining processes. Firstly, the framework of the methodology is proposed to establish the generic procedures for integrating the above considerations in production operation activities. As the two key issues of the framework, the profile index value matrix is determined by valuing the individual quantity of energy consumption and environmental impacts of machining each job on each machine, and the multi-criteria models are constructed by the operational methods. Furthermore, with the guideline of the framework, the specific formulations are modeled by two sub-models for the parallel machine scheduling problem, in which makespan and energy consumption are the optimizing objectives as well as the constraints of environmental impact considerations. The specific formulations provide a practical method to integrate energy consumption and environmental impact considerations into the scheduling activity, and also can serve as a reference to other activities in the production operation. The case study for a batch of jobs, including seven kinds of gears in the machining shop floor, is presented to demonstrate the application of the specific formulations of the methodology. The proposed methodology provides potential opportunities for reducing energy consumption and environmental impacts in machining processes, and helps production managers in decision-making on the issues of energy consumption and environmental impacts in the production operation.

Homing Strategy for a 4RRR Parallel Kinematic Machine

Returning home is the most important process of a parallel kinematic machine （PKM） with incremental encoders.Currently,most corresponding articles focus on the accuracy of homing process,and there lacks the investigation of the operation＇s safety.For a 4RRR PKM,all servoaxes would be independently driven to their zero positions at the same time based on the traditional homing mode,and that can bring serious interfere of the kinematic chains.This paper systemically investigates this 4RRR PKM＇s safety of homing process.A homing strategy usually contains three parts which are the home switches＇ locations,the platform＇s initial moving space,and each links＇ homing direction,and all of them can influence the safety of homing operation.For the purpose of evaluating and describing the safety of the homing strategy,some important parameters are introduced as follows：Safely homing ratio （SHR） is used to evaluate the probability of a machine＇s successfully returning home from an initial moving space;Synchronal rotational angle （SRA） is the four links＇ largest synchronal rotational angle with given directions from a given pose.Whether a machine can safely return home from a given pose can be judged by comparing the SRA with all four home switches＇ mounting angles.By meshing the initial moving space and checking the safeties of returning home from all the initial poses on the nodes,the SHR of this initial moving space can be calculate.For the sake of convenience,the platform＇s initial moving space should be as large as possible,and in this 4RRR PKM,a square zone in the center of the workspace with a giving initial rotation range is selected as the platform＇s initial moving space.The forward direction is selected as each link＇s homing direction according to custom,and the platform＇s initial rotational angle is selected as larger than 0° based on this 4RRR PKM＇s kinematic characteristics.The platform＇s initial moving space can be defined only by the side length of the initial moving square.By setting a probable searching step and calculating the SHR of the initial moving square,an optimal procedure of searching for the largest side length of the platform＇s initial moving square is proposed.The homing strategy proposed is based on a systemic research on the safety of homing process for PKM,and the two new indexes SHR and SRA can clearly describe the safety of homing operation.The homing operation based on this strategy is fast and safe,and the method can also be used in other PKMs with the situation of serious components＇ interference.

ASSESSMENT OF ANIMAL SKIDDING AND GROUND MACHINE SKIDDING UNDER MOUNTAIN CONDITIONS

The Proportion of animal skidding in forest operations in Heilongjiang forest region increased signiticantly in recent years. This paper at first demonstrated and analyzed the development of the animal skidding and machine skidding, then, evaluated these two means of ground.skidding currently runing in this region both individually and comprehensively under the following criteria: operation efficiency or operation cost, degree of damage to soil and residual stands, accident rate, and natural regeneration. Finally, according to the results of synthetic assessments, cIassitications of operation conditions suitable to each of skidding measures were recommended quqntitatively with considerations of multiple evaluation criteria.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

Four-quadrant Force Control with Minimal Ripple for Linear Switched Reluctance Machines

Linear switch reluctance machine(LSRM)has been tried to act as an alternative generator for direct drive linear wave energy converter(WEC).Many researchers have proposed new topologies of LSRM to improve the power density,efficiency and reliability.However,the control methods for LSRM applied in direct drive WEC have been paid little attention,especially control methods considering the wave energy generator operating characteristics.In this paper,according to the generator control requirements of the direct drive WEC,force control algorithm for LSRM operating in four quadrants without a speed closed loop is put forward.The force ripple of LSRM is suppressed using force sharing function method.The four-quadrant control is easy to realize requiring only phase currents information.Simulation results validate the proposed method and indicate that LSRM is able to be used as the generator for direct drive WEC.

Quantum Operator Model for Data Analysis and Forecast

A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.

USE OF GENETIC ALGORITHMS TO SEQUENCE THE MACHINING OPERATIONS OF PARTS

ＵＳＥＯＦＧＥＮＥＴＩＣＡＬＧＯＲＩＴＨＭＳＴＯＳＥＱＵＥＮＣＥＴＨＥＭＡＣＨＩＮＩＮＧＯＰＥＲＡＴＩＯＮＳＯＦＰＡＲＴＳＷＡＮＧＸｉｙａｎｇ（王细洋）（ＮａｎｃｈａｎｇＩｎｓｔｉｔｕｔｅｏｆＡｅｒｏｎａｕｔｉｃｓＴｅｃｈｎｏｌｏｇｙ，３３００３４，...