Applicability Of Proposed Method Research Articles

DIC measurement for large-scale structures based on adaptive warping image stitching.

As a representative method of optical non-interference measurement, digital image correlation (DIC) technology is a non-contact optical mechanics method that can measure the displacement and deformation of the whole field. However, when the measurement range of the field is too large, the existing DIC method cannot measure the full-field strain accurately, which limits the application of the DIC measurement in the case of a large size and wide-field view. To address this issue, a DIC measurement method for large-scale structures based on adaptive warping image stitching is proposed in this paper. First, multiple adjacent high-resolution images are collected at different locations of large-scale structures. Secondly, the collected images are stitched by applying the adaptive warping image stitching algorithm to obtain a panoramic image. Finally, the DIC algorithm is applied to solve the whole deformation field. In the experiments, we first verify the feasibility of the proposed method for image matching and fusion through the numerical simulation of a rigid body translation experiment. Then the accuracy and robustness of the proposed method in practical application are verified by rigid body translation and a three-point bending experiment. The experimental results demonstrate that the measurement range of DIC is improved significantly with the adaptive warping image stitching algorithm.

A method for improving the performance of centering rod surveying based on two-position correction

To address the problem of a decrease in the measurement accuracy of centering rod surveying as a result of accelerometer bias and scale factor error, a performance improving method for pitch measurement is proposed. The error model of centering rod surveying is built so that the relationship between pitch measurement error and the accelerometer’s bias and scale factor error are clear. Numerical simulations are conducted and the error variation is revealed. A two-position correction method is developed and 12 verification experiments are carried out. After correction, the measurement error is reduced from a few centimeters to submillimeter scale. The experimental results illustrate that the accuracy of the pitch measurement is improved effectively, which meets the requirements for use. Three repeated experiments are carried out; the calculated uncertainties are 0.053%, 0.035% and 0.027% separately, which indicates the feasibility and effectiveness of the proposed method in practical applications. This method can provide some guidance for improving the performance of centering rod surveying.

Data-Driven Power Flow Calculation Method: A Lifting Dimension Linear Regression Approach

The high-precision parameters in distribution networks are difficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integration of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distribution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accuracy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effectiveness of the proposed method in practical applications.

A scaled framework for analysis of crack propagation in teeth using equivalent inorganic materials

Experimental investigations of teeth are significantly impeded with the main impediments being small dimensions involved and ethical issues associated with in vivo testing. On the other hand, numerical simulations which can be used as an alternative method have their own inherent limitations which are being reliant on constitutive and boundary information, and also numerical predictions should be validated with experimental findings. Hence, it is necessarily required that experimental tests are conducted on equivalent models which should inevitably be made out of different materials. However, there is no any known method to predict the crack propagation and fracture behavior of structures using equivalent models made from different materials. The present paper aims to propose a scaling method based on the finite similitude theory which has recently been appeared in open literatures to investigate the crack propagation and fracture behavior of teeth in which different materials can even be used for scaled models. The effectiveness of the developed scaling method is examined using the Abaqus finite element software by employing the J-integral, cohesive zone model (CZM), and extended finite element method (XFEM). By conducting the compressive numerical experiments, it is revealed that although the exact prediction of the behavior of a tooth using equivalent models made out of inorganic materials is impossible, it can be anticipated to a good accuracy. Also, the response characteristics of the tooth subjected to a cyclic loading which can be imposed during the act of chewing are anticipated to a good accuracy using the scaled up trial model made out of zirconia which demonstrates the high capability of the proposed method in practical applications.

Evaluating construction delays using productivity-based resource reallocation for economic feasibility

PurposeWork crew productivity and the application of limited resources are necessary elements in construction duration delay analysis. This study thus proposes a method to analyze construction delays and resource reallocation based on work crew productivity and resource constraints. The study also presents an economic feasibility analysis that maximizes economic effect by reducing construction duration, the cost of resource reallocation, delay liquidated damages (DLDs) and incentives for reducing contractual duration.Design/methodology/approachThe proposed method involved three steps. First, work crew characteristics such as productivity, unit price and workload helped analyze delay information, including delay duration, reducible duration and daily reduced cost. Next, a goal programming method assessed resource reallocation based on the priority (as determined by decision-makers) of each constraint condition, such as the available number of workers, cost, goal workload and statutory working hours. Lastly, the level of reallocation was analyzed based on the results of the economic feasibility analysis and decision-makers’ delay attitudes.FindingsA case study was performed to test the proposed method's applicability. Its involved sensitivity analysis indicated proposing to decision-makers a scenario based on the prioritization of economic feasibility. The proposed method's applicability proved high for decision-makers, as they can determine whether to reduce construction duration per the proposed data.Originality/valueThe proposed method's main contribution is the reallocation of resources to reduce construction duration based on work crew productivity and the prioritization of limited resources. The proposed method can analyze the differences in productivity between the plan and actual progress, as well as calculate the necessary number of workers. Decision-makers can then reduce the appropriate level of contractual duration based on their own delay attitude, constraint condition prioritization and results from daily economic feasibility analyses.

Power Curve-Based Fault Detection Method for Wind Turbines

A normal behavior model based on a boosted stacked regressor, trained in a k-fold crossvalidation with optimal power curve data, is proposed for wind turbine fault detection. In order to obtain the optimal power curve data, a signal processing scheme based on density-based spatial clustering of applications with noise, along with a robust estimation algorithm are employed, from which an upper-lower bound envelop is established. The experimental results based on real wind turbine data from supervisory control and data acquisition system indicate the effectiveness and impact of the proposed method in practical applications

A SIMPLE NONPARAMETRIC APPROACH FOR ESTIMATION AND INFERENCE OF CONDITIONAL QUANTILE FUNCTIONS

In this paper, we present a new nonparametric method for estimating a conditional quantile function and develop its weak convergence theory. The proposed estimator is computationally easy to implement and automatically ensures quantile monotonicity by construction. For inference, we propose to use a residual bootstrap method. Our Monte Carlo simulations show that this new estimator compares well with the check-function-based estimator in terms of estimation mean squared error. The bootstrap confidence bands yield adequate coverage probabilities. An empirical example uses a dataset of Canadian high school graduate earnings, illustrating the usefulness of the proposed method in applications.

Autoregressive spectrum-guided variational mode decomposition for time-varying modal identification under nonstationary conditions

Modal identification is an important research subject in structural health monitoring. It is a mature subject for modal identification of time-invariant and stationary conditions. Because time-varying properties are valuable for damage detection and time-varying system identification, and because the environmental loads do not always satisfy the assumption of stationarity, it is necessary to develop accurate identification methods for time-varying structures, structures under nonstationary excitations, or the coupling of the two conditions. The variational mode decomposition (VMD) method, which adaptively decomposes the signal into narrow-band intrinsic modes, shows the potential for time-varying and nonstationary conditions. However, parameter selection greatly affects the accuracy of mode decomposition. In this study, a new modal identification method based on an autoregressive spectrum-guided variational mode decomposition method is proposed for the application of time-varying and nonstationary conditions. First, the determination of the initial center frequencies for VMD is proposed using an autoregressive spectrum-guided method to greatly improve the accuracy. Second, a method to select the optimal balancing factor is developed to improve the decomposition accuracy by controlling the proper bandwidth. Subsequently, an instantaneous frequency identification method is presented by detecting the ridge of the time–frequency distribution. Finally, studies of numerical and actual structures demonstrate the capacity of the proposed method for practical applications of time-varying cases and nonstationary conditions.

Cell‐based hybrid adaptive mesh refinement algorithm for immersed boundary method

AbstractThis article proposes a hybrid adaptive mesh refinement (AMR) algorithm for the immersed boundary method (IBM) combination, and the AMR code is split into mesh and physics codes to optimize each part individually. The uniform parent grid solver is used for AMR grids by constructing hanging cells for the high‐order extension. The restrictive spatial refinement in a fully threaded tree (FTT) data structure is explored, and a simplified stencil search algorithm for hanging cells construction is introduced, including current and following child AMR level cells, if any. The proposed AMR method was applied to IBM, which offers flexibility in treating complex geometries in the Cartesian grid, leading to algorithmic simplicity and computational efficiency. Local near immersed boundary refinement is proposed to avoid complex and computationally expensive IBM and AMR algorithms near the solid bodies. Finally, a high‐order flux scheme extension at AMR level transition cells and the proposed method's applicability for steady and transient flows are demonstrated. Besides state and flux variables storage, the proposed hybrid AMR method's additional cost is 4.5 words/cell instead of 3.5 words/cell for 2D and 3.75 words/cell instead of 2.75 words/cell for 3D compared to the conventional FTT‐based AMR method. By comparing the uniform grid, the AMR final grid distribution shows that the optimal cell distribution based on flow physics reduces the cells required by more than 40% to resolve the complex flow features in less computational time. The enhanced performance and accuracy of the proposed AMR method in resolving different scale flow features are validated through benchmark problems.

A private sustainable partner selection model for green public-private partnerships and regional economic development

Many cities are struggling to keep pace with limited budgets and rapid growth. Economic development models involving public-private partnerships (P3s) can help drive economic revitalization. The choice of partners plays a vital role in the success or failure of sustainable P3 initiatives. In this study, we propose a novel integrated sustainable private partner selection framework in P3s. The proposed model is composed of the best-worst method (BWM), the weighted influence non-linear gauge system (WINGS), and the technique of order preference similarity to the ideal solution (TOPSIS). The BWM is used to identify the importance weights of the economic, environmental, social, and technological criteria. The WINGS method uses ideographic causal maps to analyze the intertwined criteria and their causal relations. TOPSIS is used to rank and select the private partners that will bring the “best value” to the partnership. We demonstrate the proposed method's applicability in a P3 initiative for sustainability, gentrification and neighborhood revitalization, and economic development in a northeastern US city. In this initiative, low density, low cost, and biodegradable agricultural waste and mushroom fibers are grown in vacant buildings to be used as a Styrofoam packaging replacement.

A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

Bioluminescence Tomography Based on One-Dimensional Convolutional Neural Networks.

Bioluminescent tomography (BLT) has increasingly important applications in preclinical studies. However, the simplified photon propagation model and the inherent ill-posedness of the inverse problem limit the quality of BLT reconstruction. In order to improve the reconstruction accuracy of positioning and reconstruction efficiency, this paper presents a deep-learning optical reconstruction method based on one-dimensional convolutional neural networks (1DCNN). The nonlinear mapping relationship between the surface photon flux density and the distribution of the internal bioluminescence sources is directly established, which fundamentally avoids solving the ill-posed inverse problem iteratively. Compared with the previous reconstruction method based on multilayer perceptron, the training parameters in the 1DCNN are greatly reduced and the learning efficiency of the model is improved. Simulations verify the superiority and stability of the 1DCNN method, and the in vivo experimental results further show the potential of the proposed method in practical applications.

A modeling framework and analysis of challenges faced by the Indian petroleum supply chain

This paper aims to determine the petroleum industry's supply chain challenges and develop Total Interpretive Structural Models (TISM) of the challenges faced by the companies and the consumers' perspective. From the companies' perspective, nine supply chain challenges were identified through rigorous literature review and experts' opinions. From the consumers' perspective, five challenges variables were identified by the literature and the north Indian retail consumers. A novel modeling technique, TISM methodology, describes the leading and lagging variables, which helped them convert the unstructured model to a well-articulated model. The MICMAC analysis is also performed to classify the contributory variables into different categories on the basis of their driving and dependence power. The study reveals that government policies, adverse weather conditions, and environmental regulation are the most influencing challenges, and resource availability is the least influencing by companies' perspectives. The quality and the price of products are the most influencing challenges, and social welfare is the least influencing by consumers' perspectives. This study uses the Indian petroleum industry to describe the proposed method's applicability. This research intended to guide the practitioners, policymakers, and academicians to emphasize their effort towards effective supply chain management in the petroleum industry.

Variable selection in nonparametric functional concurrent regression

We develop a new method for variable selection in nonparametric functional concurrent regression. The commonly used functional linear concurrent model (FLCM) is far too restrictive in assuming linearity of the covariate effects, which is not necessarily true in many real‐world applications. The nonparametric functional concurrent model (NPFCM), on the other hand, is much more flexible and can capture complex dynamic relationships present between the response and the covariates. We extend the classically used variable selection methods, e.g., group LASSO, group SCAD and group MCP, to perform variable selection in NPFCM. We show via numerical simulations that the proposed variable selection method with the non‐convex penalties can identify the true functional predictors with minimal false‐positive rate and negligible false‐negative rate. The proposed method also provides better out‐of‐sample prediction accuracy compared to the FLCM in the presence of nonlinear effects of the functional predictors. The proposed method's application is demonstrated by identifying the influential predictor variables in two real data studies: a dietary calcium absorption study, and some bike‐sharing data.

Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine

As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH) variance (ARMA-GARCH) time series model. Autocorrelation structure in data with changing volatility makes pattern recognition difficult. As such, some features are selected to extract different properties of the considered patterns. Moreover, a new feature named the maximum degree between the horizontal line and the consecutive observations line is used for the better distinction of the step and trend shift patterns. To evaluate the performance of the proposed method, we performed a comprehensive simulation study. Moreover, to illustrate the proposed method's application, it was applied to classify patterns in the OPEC crude oil basket return and the Tehran stock exchange index.

Discussion on the existing methodology of entropy-weights in water quality indexing and proposal for a modification of the expected conflicts.

The present research focuses on addressing various ambiguities in the existing method of integrating information entropy and water quality, thereby presenting a novel approach for an entropy-weighted water quality index. A three-dimensional water quality dataset is considered in the proposed method, the third dimension being the sampling frequency factor. The probability of observed values adhering to desirable limits prescribed by a standard code is estimated, leading to the computation of information entropy and, eventually, entropy weights. These weights are then used for the computation of the Modified Entropy-weight Water Quality Index (MEWQI) values. To verify the proposed method's applicability, the water quality dataset of Deepor Beel, India, was considered. IS 10500: 2012 was used for estimating MEWQI values. Results showed an excellent correlation with the observed dataset and their uncertainties of occurrence. The reliability and correctness of the proposed methodology were finally confirmed through both cluster analysis and sensitivity analysis. The cluster analysis showed remarkable associations with the computed MEWQI values, while the sensitivity analysis proved that no particular parameter was accountable for the contribution of MEWQI values; instead, all parameters exhibited equal contributions. The proposed methodology was thus found to be the most reasonable and reliable as it considered both factors, i.e., measured values concerning standard limits and the uncertainty, necessary for a consistent water quality monitoring program.

Simplified modeling and analysis method for skyscrapers with outrigger system

In this paper, a simplified modeling and analysis method for skyscrapers with an outrigger system is introduced. The simplified method is mainly based on the superelements and dominant degree of freedom methods, and it is codified by using MATLAB software. The simplified method results are verified with analysis results of the ETABS detailed model, where static, modal, and dynamic analyses are conducted. The proposed method's applicability on different configurations of tall buildings with outriggers systems is also verified through a parametric study. A comparative study with some previous studies is conducted to prove the efficiency of the proposed method. It was found that the adopted simplified modeling method has good accuracy in predicting the lateral displacements and inter-story drifts in the linear and nonlinear analysis. Furthermore, the simplified model has good accuracy in predicting the first modal shape and its corresponding period, while it has less accuracy in predicting the higher modal shapes and periods. Also, it was found that the simplified model underestimates the Max and Min story acceleration in comparison with the accurate model. The parametric study confirmed the proposed method's applicability on different configurations and topologies of tall buildings with an outrigger system. As a result, the simplified modeling method gives a deep understanding of the outrigger system behavior and its parameters. Therefore, it is an effective tool for modeling and analysis in the preliminary design stage. However, further improvements on the simplified modeling method should be conducted in future studies to increase the accuracy and quality of the obtained results and avoid its disadvantages.

A New Algorithm for the Design of Site Utility for Combined Production of Power, Freshwater, and Steam in Process Industries

Abstract Site utility, without a doubt, is one of the major units in process industries that consumed a lot of fossil fuels and significantly emitted emission pollution. In this paper, a systematic procedure was proposed to optimize the utility system's design and integration based on a targeting approach as process integration technique, exergetic, exergoeconomic, exergoenvironmental analysis associated with life cycle assessment (LCA), and multi-objective optimization through water cycle and genetic algorithms. Total site analysis was performed to provide an essential understanding of the equipment's characteristics and interactions in the site utility system. It also aims for power production and the temperature of the boiler and each steam level with acceptable accuracy. Furthermore, the exergetic, exergoeconomic, and exergoenvironmental analyses were presented to declare the effects of irreversibility, economic, and environmental impacts on the system. In this paper, to show the proposed method's applicability, the optimal design of a site utility associated with a petrochemical complex has been considered. First, the optimal design is performed based on minimization of total annualized costs (TAC) as one-objective function through star software, genetic algorithm (GA), water cycle algorithm (WCA), proposed GA, and proposed WCA method. In the next phase, the optimal design is done based on MOGA, MOWCA, proposed MOGA, and proposed MOWCA methods based on maximum exergetic efficiency, and freshwater production, and minimization of exergetic cost, and total environmental exergetic impact. Results show by using the new procedure, the optimum solution has been achieved by a significant reduction of computational time.

Robust and Reliable Output Feedback Control for Uncertain Networked Control Systems Against Actuator Faults

In this study, first, a comprehensive model is introduced to model actuator faults, and then a novel fault-tolerant control (FTC) strategy is proposed to compensate the loss of actuator’s effectiveness in networked control systems (NCSs). A Markov chain is exploited to represent networked-induced random delays, and data packet dropouts as well as disorders to address the stochastic characteristic of the network issues. Accordingly, the resulting closed-loop system lies in the framework of Markovian jump systems (MJSs). Moreover, partly unknown transition probabilities are considered in the current study since the identification of the exact value of transition probabilities of the Markov chain is difficult or even impractical due to the complex structure of the network. Sufficient conditions for the stochastic stability are derived by means of the solutions of a finite set of linear matrix inequalities (LMIs) to design a novel robust FTC through the output feedback technique, which requires only the outputs. A numerical example and an engineering benchmark system are presented to verify the capability of the proposed method in practical applications.

Improved controlled source audio‐frequency magnetotelluric method apparent resistivity pseudo‐sections based on the frequency and frequency–spatial gradients of electromagnetic fields

ABSTRACTAlthough most electromagnetic data can be inverted to actual resistivity, ways of quickly getting a real‐time interpretation of a data set are still valuable. Such methods are useful when we are testing instrumentation or assessing data quality during a survey, or when we need to get a general understanding of the geological structure during a field survey. Apparent resistivity is a good way to satisfy these desires. However, one of the disadvantages of apparent resistivities is that the traditional apparent resistivity formulations are poor at recognizing boundaries, mainly because abnormal responses get stretched into deeper parts of the image (a shadow effect). In order to improve the recognition ability of boundaries, we propose improved apparent resistivity pseudo‐sections based on the formulae for the frequency and frequency–spatial gradients of the fields in the far‐field region of frequency‐domain controlled‐source audio‐frequency magnetotelluric surveys. The new pseudo‐sections are found to be better than those produced from a traditional method when applied to a number of 3‐D examples. The performance of this apparent resistivity method is closely related to using an appropriate transmitter–receiver distance: when a proper value is used, good results can be obtained in which the horizontal locations of vertical boundaries and the positions of top and bottom boundaries can be identified clearly. Finally, the usefulness of the proposed method for practical applications is evaluated with a field‐data example, for which the results of the proposed apparent resistivity imaging method are compared with traditional apparent resistivities, as well as with the results from a 2‐D inversion of DC resistivity data from the same survey line and with what is known about the geology of the area. This comparison demonstrates the improved capabilities of the new apparent resistivities over traditional approaches, including an improved capability to accurately reveal the bottoms of targets.