Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.

Volumetric lattice Boltzmann method for pore-scale mass diffusionadvection process in geopolymer porous structures

Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the development of porous materials for nuclear waste treatment applications.

Application of Flipped Classroom Combined with PBL Teaching Method in the Teaching of Respiratory Intensive Care Unit Nursing

Objective:The aim is to investigate the application effect of flipped classroom combined with problem-based learning(PBL)teaching method in the teaching of respiratory intensive care unit nursing.Methods:100 fresh nursing students who were interned in the respiratory intensive care unit of our hospital from June 2020 to May 2022 were selected and randomly divided into 50 students in the control group and 50 students in the experimental group.The students in the control group were taught by PBL teaching method,and the students in the experimental group were taught by flipped classroom combined with PBL teaching method.After the completion of the teaching,the teachers combined the performance of the two groups of students,and scored them comprehensively in terms of their professional theoretical knowledge,clinical operation skills,independent learning ability,and teamwork ability,and carried out a survey of the experimental group’s students in terms of their satisfaction with the understanding of theoretical knowledge,clinical operation,independent learning ability,teamwork ability,and other dimensions.Results:There was no statistical significance in the specialized theoretical knowledge scores of the two groups of students(P>0.05).The scores of clinical operation,independent learning ability,and teamwork ability of the two groups of students were statistically significant(P<0.05),and all the scores of the students in the experimental group were higher than that of the control group.More than 90%of the students believed that the flipped classroom combined with PBL teaching method could assist in the comprehension of theoretical knowledge,improve the clinical operation skills,enhance the ability of independent learning and teamwork;there were 92%of the students supported the use of flipped classroom combined with PBL teaching in respiratory intensive care unit nursing teaching.Conclusion:In the teaching of respiratory intensive care unit nursing,the use of flipped classroom combined with PBL teaching method can improve the learning effect of students,and has certain value in teaching.

Critical care nurses and their clinical reasoning for customizing monitor alarms:a mixed-method study

Objective:To explore the clinical rationale of critical care nurses for personalizing monitor alarms.One of the most crucial jobs assigned to critical care nurses is monitoring patients'physiological indicators and carrying out the necessary associated interventions.Successful use of equipment in the nursing practice environment will be improved by a thorough understanding of the nurse's approach to alarm configuration.Methods:A mixed-method design integrating quantitative and qualitative components was used.The sample of this study recruited a convenience sample of 60 nurses who have worked in critical care areas.This study took place at Lebanese American University Medical Center Rizk Hospital,utilizing a semi-structured interview with participants.Results:The study demonstrated the high incidence of nuisance alarms and the desensitization of critical care nurses to vital ones.According to the nurses,frequent false alarms and a shortage of staff are the 2 main causes of alarm desensitization.Age was significantly associated with the perception of Smart alarms,according to the data(P=0.03).Four interconnected themes and subcategories that reflect the clinical reasoning process for alarm customization were developed as a result of the study's qualitative component:(1)unit alarm environment;(2)nursing style;(3)motivation to customize;and(4)clinical and technological customization.Conclusions:According to this study,nurses believe that alarms are valuable.However,a qualitative analysis of the experiences revealed that customization has been severely limited since the healthcare team depends on nurses to complete these tasks independently.Additionally,a staffing shortage and lack of technical training at the start of placement have also hindered customization.

Study on the evaluation index system and evaluation method for self-elevating drilling unit design

In the process of designing self-elevating drilling unit, it is important, yet complicated, to use comparison and filtering to select the optimum scheme from the feasible ones. In this research, an index system and methodology for the evaluation of self-elevating drilling unit was proposed. Based on this, a multi-objective combinatorial optimization model was developed, using the improved grey relation Analysis （GRA）, in which the analytic hierarchy process （AHP） was used to determine the weights of the evaluating indices. It considered the connections within the indices, reflecting the objective nature of things, and also considered the subjective interests of ship owners and the needs of designers. The evaluation index system and evaluation method can be used in the selection of an optimal scheme and advanced assessment. A case study shows the index system and evaluation method are scientific, reasonable, and easy to put into practice. At the same time, such an evaluation index system and evaluation method will be helpful for making decisions for other mobile platforms.

RELIABILITY ASSESSMENT OF ENTROPY METHOD FOR SYSTEM CONSISTED OF IDENTICAL EXPONENTIAL UNITS

The reliability assessment of unit-system near two levels is the mostimportant content in the reliability multi-level synthesis of complex systems. Introducing theinformation theory into system reliability assessment, using the addible characteristic ofinformation quantity and the principle of equivalence of information quantity, an entropy method ofdata information conversion is presented for the system consisted of identical exponential units.The basic conversion formulae of entropy method of unit test data are derived based on the principleof information quantity equivalence. The general models of entropy method synthesis assessment forsystem reliability approximate lower limits are established according to the fundamental principleof the unit reliability assessment. The applications of the entropy method are discussed by way ofpractical examples. Compared with the traditional methods, the entropy method is found to be validand practicable and the assessment results are very satisfactory.

The Conceptual Design Method for a Pumping Unit Based on QFD and TRIZ

Quality function deployment （QFD） is a quality system, that can help to design novel products that meet customers＇ needs. Theory of inventive problem solving （TRIZ） is a very powerful tool in helping to solve difficult technical problems encountered in the design process. Introducing QFD and TRIZ into the conceptual design of the pumping unit combines advantages of these two theories, therefore meeting different demands of different users. It can tell us “What should we do it” with QFD and “How should we do it” with TRIZ. The conceptual design method, which is based on QFD and TRIZ, is introduced andused to analyze and evaluate the conceptual design project of a pumping unit.

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.

Force Control Compensation Method with Variable Load Stiffness and Damping of the Hydraulic Drive Unit Force Control System

Each joint of hydraulic drive quadruped robot is driven by the hydraulic drive unit（HDU）, and the contacting between the robot foot end and the ground is complex and variable, which increases the difficulty of force control inevitably. In the recent years, although many scholars researched some control methods such as disturbance rejection control, parameter self-adaptive control, impedance control and so on, to improve the force control performance of HDU, the robustness of the force control still needs improving. Therefore, how to simulate the complex and variable load characteristics of the environment structure and how to ensure HDU having excellent force control performance with the complex and variable load characteristics are key issues to be solved in this paper. The force control system mathematic model of HDU is established by the mechanism modeling method, and the theoretical models of a novel force control compensation method and a load characteristics simulation method under different environment structures are derived, considering the dynamic characteristics of the load stiffness and the load damping under different environment structures. Then, simulation effects of the variable load stiffness and load damping under the step and sinusoidal load force are analyzed experimentally on the HDU force control performance test platform, which provides the foundation for the force control compensation experiment research. In addition, the optimized PID control parameters are designed to make the HDU have better force control performance with suitable load stiffness and load damping, under which the force control compensation method is introduced, and the robustness of the force control system with several constant load characteristics and the variable load characteristics respectively are comparatively analyzed by experiment. The research results indicate that if the load characteristics are known, the force control compensation method presented in this paper has positive compensation effects on the load characteristics variation, i.e., this method decreases the effects of the load characteristics variation on the force control performance and enhances the force control system robustness with the constant PID parameters, thereby, the online PID parameters tuning control method which is complex needs not be adopted. All the above research provides theoretical and experimental foundation for the force control method of the quadruped robot joints with high robustness.

A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system’s operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to large-scale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL);however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

A deep kernel method for lithofacies identification using conventional well logs

How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.

Simulation of fluid-structure interaction in a microchannel using the lattice Boltzmann method and size-dependent beam element on a graphics processing unit

Fluid-structure interaction （FSI） problems in microchannels play a prominent role in many engineering applications. The present study is an effort toward the simulation of flow in microchannel considering FSI. The bottom boundary of the microchannel is simulated by size-dependent beam elements for the finite element method （FEM） based on a modified cou- ple stress theory. The lattice Boltzmann method （LBM） using the D2Q13 LB model is coupled to the FEM in order to solve the fluid part of the FSI problem. Because of the fact that the LBM generally needs only nearest neighbor information, the algorithm is an ideal candidate for parallel computing. The simulations are carried out on graphics processing units （GPUs） using computed unified device architecture （CUDA）. In the present study, the governing equations are non-dimensionalized and the set of dimensionless groups is exhibited to show their effects on micro-beam displacement. The numerical results show that the displacements of the micro-beam predicted by the size-dependent beam element are smaller than those by the classical beam element.

Predication of Plastic Flow Characteristics in Ferrite/Pearlite Steel Using a Fern Unit Cell Method

The flow stress of ferrite/pearlite steel under uni-axial tension was simulated with finite element method (FEM) by applying commercial software MARC/MENTAT. Flow stress curves of ferrite/pearlite steels were calculated based on unit cell model. The effects of volume fraction, distribution and the aspect ratio of pearlite on tensile properties have been investigated.

Three-dimensional Extension of the Unit-Feature Spatial Classification Method for Cloud Type

We describe how the Unit-Feature Spatial Classification Method(UFSCM) can be used operationally to classify cloud types in satellite imagery efficiently and conveniently.By using a combination of Interactive Data Language(IDL) and Visual C++(VC) code in combination to extend the technique in three dimensions(3-D),this paper provides an efficient method to implement interactive computer visualization of the 3-D discrimination matrix modification,so as to deal with the bi-spectral limitations of traditional two dimensional(2-D) UFSCM.The case study of cloud-type classification based on FY-2C satellite data (0600 UTC 18 and 0000 UTC 10 September 2007) is conducted by comparison with ground station data, and indicates that 3-D UFSCM makes more use of the pattern recognition information in multi-spectral imagery,resulting in more reasonable results and an improvement over the 2-D method.

Analysis of Software Development and Enhancement Projects Work Effort per Unit Based on the COSMIC Method with Regard to Technological Factors—Case Study

This paper aims to present a case study that consists in the analysis of work effort per unit of software systems Development and Enhancement Projects (D&EP) depending on technological factors. That analysis was commissioned by one of the largest public institutions in Poland. This is the COSMIC (Common Software Measurement International Consortium) function points method that is chosen by this institution as a point of reference for size of software systems developed/enhanced for supporting its functions and therefore this method is the base for the analysis of D&EP work effort per unit with regard to technological factors.

Research on Test Data Distribution of Strapdown Inertial Measurement Unit Based on Bayesian Method

Aiming at that the successive test data set of the strapdown inertial measurement unit is always small,a Bayesian method is used to study its statistical characteristics.Its prior and posterior distributions are set up by the method and the pretest,sample and population information.Some statistical inferences can be made based on the posterior distribution.It can reduce the statistical analysis error in the case of small sample set.

基于DEMATEL-ISM方法的水电站机组检修作业安全事故致因链分析

针对水电站机组检修作业种类和危险因素多、安全事故频发等问题,通过文献挖掘与专家访谈方式,对水电站机组检修作业安全影响因素类别进行了划分。基于DEMATEL-ISM方法,构建了安全事故致因多层次递阶ISM模型来确定事故致因因素层级,通过剖析事故致因因素的中心度和权重,挖掘关键致因因素和推求事故致因链的路径权重,厘定关键致因链。采用该模型对某水电站致因链进行了分析,结果表明,安全检查不到位为关键致因因素,“安全检查不到位→违规作业→设备的问题→粉尘、油漆等有害物质”为关键致因链。

Physical Properties of the Function and Number of Empirical Macrohardness of the Material: Universal Physical Unit of Measurement of Macro Hardness (Part 2)

Results of analytical studies of the physical properties of the function and number of empirical macrohardness based on the standard experimental force diagram of kinetic macroindentation by a sphere.An analytical comparison method and a criterion for the similarity of the physical and empirical macrohardness of a material are proposed.The physical properties of the hardness measurement process using the Calvert-Johnson method are shown.The physical reasons for the size effect when measuring macrohardness are considered.The universal physical unit and standard of macrohardness of kinetic macroindentation by a sphere is substantiated.

利用熵权TOPSIS和遗传算法的焊接单元划分方法

在充分分析焊接工艺特性的基础上,以降低焊接序列规划和焊接工位分配难度为目标,综合考虑单元间的平衡问题和单元复杂度问题,提出一种利用熵权TOPSIS法和遗传算法实现焊接单元合理划分的方法。首先,根据最佳单元划分公式确定基础件数量范围;其次,以零件的质量、体积、表面积、零件连接数量、焊缝数量、焊缝体积为评价指标,通过熵权TOPSIS法确定基础件;然后,以单元稳定性、单元平衡性和单元复杂度为优化目标,利用遗传算法得到不同单元数量时的最佳单元划分方案,通过对比分析确定最佳单元划分方案,实现焊接单元的科学合理划分;最后,以某车型前副车架为例进行验证,结果表明该方法快速、合理、有效,能够满足实际的工程需求。