Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

Time-Expanded Sampling for Ensemble-Based Filters:Assimilation Experiments with Real Radar Observations

By sampling perturbed state vectors from each ensemble prediction run at properly selected time levels in the vicinity of the analysis time, the recently proposed time-expanded sampling approach can enlarge the ensemble size without increasing the number of prediction runs and, hence, can reduce the computational cost of an ensemble-based filter. In this study, this approach is tested for the first time with real radar data from a tornadic thunderstorm. In particular, four assimilation experiments were performed to test the time-expanded sampling method against the conventional ensemble sampling method used by ensemble- based filters. In these experiments, the ensemble square-root filter （EnSRF） was used with 45 ensemble members generated by the time-expanded sampling and conventional sampling from 15 and 45 prediction runs, respectively, and quality-controlled radar data were compressed into super-observations with properly reduced spatial resolutions to improve the EnSRF performances. The results show that the time-expanded sampling approach not only can reduce the computational cost but also can improve the accuracy of the analysis, especially when the ensemble size is severely limited due to computational constraints for real-radar data assimilation. These potential merits are consistent with those previously demonstrated by assimilation experiments with simulated data.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

Adaptive Radial Based Sampling for Reliability and Reliability Sensitivity Analysis Involving Truncated Correlated Normal Variables

The reliability and reliability sensitivity （ RS ） models are presented for the engineering problem involving truncated correlated normal variables （CNV）, and in the case an adaptive radial based sampling is used to analyze the reliability and the RS. In the presented models, the truncated CNV is transformed to general CNV, and the value domains of the truncated CNV are treated as multiple failure modes, then the reliability and the RS with the truncated CNV are transformed to the general cases, on which an e^cient radial based sampling is used to analyze the trans- formed reliability and RS. An adaptive strategy is employed to search for the optimal radial in the sampling, by which the robustness of the method is improved. After the model concepts and the detailed implementation are given, several examples are presented to demonstrate the feasibility of the model and the efficiency of the solutions.

Peak displacement patterns for the performance-based seismic design of steel eccentrically braced frames

Performance-based seismic design(PBSD) aims to assess structures at different damage states. Since damage can be directly associated to displacements, seismic design with consideration of displacement seems to be logical. In this study, simple formulae to estimate the peak floor displacement patterns of eccentrically braced frames(EBFs) at different performance levels subjected to earthquake ground motions are proposed. These formulae are applicable in a PBSD and especially in direct displacement-based design(DDBD). Parametric study is conducted on a group of 30 EBFs under a set of 15 far field and near field accelerograms which they scaled to different amplitudes to adapt various performance levels. The results of thousands of nonlinear dynamic analyses of EBFs have been post-processed by nonlinear regression analysis in order to recognize the major parameters that influence the peak displacement pattern of these frames. Results show that suggested displacement patterns have relatively good agreement with those acquired by an exact nonlinear dynamic analysis.

A new methodology for energy-based seismic design of steel moment frames

A procedure is proposed whereby input and hysteretic energy spectra developed for single-degree-of-freedom （SDOF） systems are applied to multi-degree-of-freedom （MDOF） steel moment resisting frames. The proposed procedure is verified using four frames, viz., frame with three-, five-, seven- and nine-stories, each of which is subjected to the fault- normal and fault-parallel components of three actual earthquakes. A very good estimate for the three- and five-story frames, and a reasonably acceptable estimate for the seven-, and nine-story frames, have been obtained. A method for distributing the hysteretic energy over the frame height is also proposed. This distribution scheme allows for the determination of the energy demand component of a proposed energy-based seismic design （EBSD） procedure for each story. To address the capacity component of EBSD, a story-wise optimization design procedure is developed by utilizing the energy dissipating capacity from plastic hinge formation/rotation for these moment frames. The proposed EBSD procedure is demonstrated in the design of a three-story one-bay steel moment frame.

Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control

This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.

Benchmark Dose Estimation for Cadmium-Induced Renal Effects Based on a Large Sample Population from Five Chinese Provinces

A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D-glucosaminidase （UNAG） was chosen as an effect biomarker. The UCd BMDLs for UNAG ranged from 2.18μg/g creatinine （cr） to 4.26μg/g cr in the populations of different provinces. The selection of the sample population and area affect the evaluation of the BMDL. The reference level of UCd for renal effects was further evaluated based on the data of all 6103 subjects. With benchmark responses （BMR） of 10%/5%, the overall UCd BMDLs for males in the total population were 3.73/2.08 μg/g cr. The BMD was slightly lower in females, thereby indicating that females may be relatively more sensitive to Cd exposure than are males.

A Framework for Web-Based Mechanical Design and Analysis

In this paper, a Web-based Mechanical Design and A na lysis Framework (WMDAF) is proposed. This WMADF allows designers to develop web -based computer aided programs in a systematic way during the collaborative mec hanical system design and analysis process. This system is based on an emerg ing web-based Content Management System (CMS) called eXtended Object Oriented P ortal System (XOOPS). Due to the Open Source Status of the XOOPS CMS, programs d eveloped with this framework can be further customized to satisfy the demands of the user. To introduce the use of this framework, this paper exams three differ ent types of mechanical design and analysis problems. First, a repetitive design consideration and calculation process is transferred into WMADF programs to gai n efficiency for wired collaborative team. Second, the considered product solid model is created directly through the use of XOOPS program and Microsoft Compone nt Object Model (COM) instances. To the end of the paper, an example linked with ANSYS is used to indicate the possible application of this framework.

Compressive sensing based multiuser detector for massive MBM MIMO uplink

Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.

Use of models in large-area forest surveys： comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role： model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design- based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data.We review studies on large-area forest surveys based on model-assisted, model- based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Sequential RBF Surrogate-based Efficient Optimization Method for Engineering Design Problems with Expensive Black-Box Functions

As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.

Over-sampling algorithm for imbalanced data classification

For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.

A Calibration Method of Robot Base Frame with Procrustes Analysis

In order to realize high precision industrial operations,based on the POE formula,an effective approach to calibrate the robot's actual base frame(ABF) is proposed. Due to the existence of manufacturing errors,the ABF deviates slightly from nominal base frame( NBF). Using external precision measurement,the ABF can be established only through the three dimension(3D) position of the robot's end-effector.To ensure the orthonormal constraints of rotation matrix as well as the precise solutions,Procrustes Analysis is introduced,where an optimal orthogonal matrix is solved out by the Lagrange Multiplier method and Singular Value Decomposition(SVD). Furthermore,calibration experiment on a serial 6-DOF robot is performed,where a FARO laser tracker is utilized to measure the 3D position. Finally, calibration result indicates that the positioning accuracy has been significantly improved after calibration. The calibration method is also applicable to other similar problems,such as multi-robot coordination and robot hand-eye system calibration.

Separation of Inorganic Anions Using Methacrylate-Based Monolithic Column Modified with Trimethylamine in Ion Chromatography Capillary System

Methacrylate-based monolithic column was prepared in fused-silica capillary (80 ′ 0.32 mm i.d.) by in situ polymerizetion reaction using glycidyl methacrylate as monomer;ethylene dimethacrylate as crosslinker;1-propanol, 1,4-butanediol, and water as porogenic solvents. The monolith matrix was modified with trimethylamine to create strong anion exchanger via ring opening reaction of epoxy groups. The morphology of the monolithic column was studied by using Scanning Electron Microscope (SEM). This column had good mechanical stability and permeability. The effects of various mobile phases for separation of inorganic anions were investigated. Iodate, bromate, nitrite, bromide, and nitrate were separated within 11 min using100 mMpotassium chloride as mobile phase and detected at 210 nm. This method showed good precision of retention time, acceptable linearity and good sensitivity. Under the optimum condition, the RSD of the retention time was in the range of 1.09%-1.75% (n = 6). The calibration curve showed linear relationships between the peak area and the concentration. The limits of detection (LOD) and the limits of quantitation (LOQ) were between 0.08-0.18 mM and 0.26-0.61 mM, respectively. This method was applied to the determination of inorganic anions in tap water and ground water samples.

Asymmetric Multi-channel Sampling in a Series of Shift Invariant Spaces

We show asymmetric multi-channel sampling on a series of a shift invariant spaces ∑a^m=1v（φ（ta）） with a series of Riesz generators ∑a^m=1φ（ta） in L2（R）, where each channeled signal is assigned a uniform but distinct sampling rate. We use Fourier duality between ∑a^m=1v（φ（ta））and L2[0, 2π] to find conditions under which there is a stable asymmetric multi-channel sampling formula on ∑a^m=1v（φ（ta））.

Effect of Terrestrial LiDAR Point Sampling Density in Ephemeral Gully Characterization

Gully erosion can account for significant volumes of sediment exiting agricultural landscapes, but is difficult to monitor and quantify its evolution with traditional surveying technology. Scientific investigations of gullies depend on accurate and detailed topographic information to understand and evaluate the complex interactions between field topography and gully evolution. Detailed terrain representations can be produced by new technologies such as terrestrial LiDAR systems. These systems are capable of collecting information with a wide range of ground point sampling densities as a result of operator controlled factors. Increasing point density results in richer datasets at a cost of increased time needed to complete field surveys. In large research watersheds, with hundreds of sites being monitored, data collection can become costly and time consuming. In this study, the effect of point sampling density on the capability to collect topographic information was investigated at individual gully scale. This was performed through the utilization of semi-variograms to produce overall guiding principles for multi-temporal gully surveys based on various levels of laser sampling points and relief variation (low, moderate, and high). Results indicated the existence of a point sampling density threshold that produces little or no additional topographic information when exceeded. A reduced dataset was created using the density thresholds and compared to the original dataset with no major discrepancy. Although variations in relief and soil roughness can lead to different point sampling density requirements, the outcome of this study serves as practical guidance for future field surveys of gully evolution and erosion.

基于二阶价值梯度模型强化学习的工业过程控制方法

为了实现对高延时、非线性和强耦合的复杂工业过程稳定准确的连续控制,提出了一种基于二阶价值梯度模型强化学习的控制方法。首先,该方法在模型训练过程中加入了状态价值函数的二阶梯度信息,具备更精确的函数逼近能力和更高的鲁棒性,学习迭代效率更高;其次,通过采用新的状态采样策略,可以更高效地利用模型进行策略学习。最后,通过在OpenAI的Gym公共实验环境和两个工业场景的仿真环境的实验表明:基于二阶价值梯度模型对比传统的基于最大似然估计模型,环境模型预测误差显著降低;基于二阶价值梯度模型的强化学习方法学习效率优于现有的基于模型的策略优化方法,具备更好的控制性能,并减小了控制过程中的振荡现象。可见该方法能有效地提升训练效率,同时提高工业过程控制的稳定性和准确性。