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The properties of fiber-reinforced composite materials greatly depend on the morphology of reinforcing fibers within the base materials, i.e., spatial uniformity, orientation, and length distribution. Accurately extracting this information in an automated manner from SEM images is essential for quality assessment, quality control, and process optimization. However, due to overlapping or cross-linking issue, morphological fiber extraction is very challenging and has not been well addressed in the existing literature. This paper takes into account this research gap and proposes five different methods, namely, the opening method, simple Hough transform (HT), partitioning HT, gradient-based HT, and break-merge method to automatically extract the straight fibers from SEM images to facilitate the morphological analysis. The robustness and accuracy of the proposed methods are evaluated and compared through both simulation and real case studies.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT + RBFNN + PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

Initialization bias truncation is critically important for system performance assessment and warm-up length estimation in discrete-event simulations. Most of the existing methods are for univariate...

Prognostics play an increasingly important role in modern engineering systems for smart maintenance decision-making. In parametric regression-based approaches, the parametric models are often too rigid to model degradation signals in many applications. In this paper, we propose a Bayesian multiple-change-point (CP) modeling framework to better capture the degradation path and improve the prognostics. At the offline modeling stage, a novel stochastic process is proposed to model the joint prior of CPs and positions. All hyperparameters are estimated through an empirical two-stage process. At the online monitoring and remaining useful life (RUL) prediction stage, a recursive updating algorithm is developed to exactly calculate the posterior distribution and RUL prediction sequentially. To control the computational cost, a fixed-support-size strategy in the online model updating and a partial Monte Carlo strategy in the RUL prediction are proposed. The effectiveness and advantages of the proposed method are demonstrated through thorough simulation and real case studies. Note to Practitioners —Degradation signals have been widely used in determining the current health condition and estimate the remaining useful life (RUL) of a component or a system. Most of the existing prognostics utilize a parametric regression model to describe the evolution path of degradation signals for RUL prediction. The common functional forms of these models include simple linear, quadratic, and exponential functions. However, in many applications, the degradation signals show multiple-segment characteristics and the existing parametric forms are inadequate to capture the degradation trend. Motivated by such issue, this paper presents a multiple-change-point (CP) modeling approach, where the degradation signal is divided into several consecutive segments by CPs, and each segment is modeled by a unique parametric model. To capture the heterogeneity across different units, all the parameters, including the number and locations of CPs and model parameters of each segment, are assumed to be random variables following certain distributions. Then, we develop a statistical method to estimate these distributions using historical data. At the online monitoring stage, we develop an innovative updating algorithm to exactly calculate the closed forms of the posterior distributions of the latest CP, the current segment, and model parameters of the current segment. We also derive a closed form for the RUL distribution estimation. Later, several efficient approximation strategies are proposed to reduce the computational burden. Simulation studies and real case studies have shown that the proposed methodology has much better performance than those of existing approaches in handling degradation signals of multiple-segment characteristics.

To date, the field of telemedicine is at a critical standpoint and faces a wide variety of challenges. Voluminous data are generated through the interaction among the telemedicine stakeholders, which are ever increasing. It is well conjectured that the successful implementation of telemedicine largely depends on the effective and efficient knowledge extraction from this available data cloud. However, due to lack of proper integration of the data mining techniques, the stakeholders are not getting the full-fledged benefit from this promising platform. Considering the aforementioned fact, this book chapter provides a contrivance to integrate data mining techniques into telemedicine connecting all the stakeholders into a single podium using data engine. It illustrates the prospects of different data mining techniques and their integration for telemedicine. These techniques combine all the basic classification and clustering method including the state-of-the-art artificial neural network (ANN) and deep learning procedure for disease prediction. Two case studies, heart diseases, and breast cancer prediction have been demonstrated applications of the integrated data mining engine.

Degradation modeling is critical for health condition monitoring and remaining useful life prediction (RUL). The prognostic accuracy highly depends on the capability of modeling the evolution of degradation signals. In many practical applications, however, the degradation signals show multiple phases, where the conventional degradation models are often inadequate. To better characterize the degradation signals of multiple-phase characteristics, we propose a multiple change-point Wiener process as a degradation model. To take into account the between-unit heterogeneity, a fully Bayesian approach is developed where all model parameters are assumed random. At the offline stage, an empirical two-stage process is proposed for model estimation, and a cross-validation approach is adopted for model selection. At the online stage, an exact recursive model updating algorithm is developed for online individual model estimation, and an effective Monte Carlo simulation approach is proposed for RUL prediction. The effectiveness of the proposed method is demonstrated through thorough simulation studies and real case study.

Information theory, introduced by Shannon in the context of information transfer in communication channels, is used as a foundation for research in many diverse fields. The concept Entropy in terms of information theory is seen as the average amount of information or the rate of information produced when forming a message, element by element. Entropy has found broad application in many research fields and can also be applied in software engineering for quantifying the uncertainty associated with a software code. In this paper, information entropy and its application towards measuring software complexity are explored, along with the formulation of an information entropy based complexity measure that considers logical decision-making, processes, and software statement interaction patterns in control flow graphs mapped from actual software code. To broaden the application of the proposed metric, the execution times of nodes in the control flow graphs are also incorporated. Further, the metric is evaluated against eight different axioms that a software complexity measure should satisfy.

In addition to the high acquisition cost of large-scale equipment, firms may also have to pay for costly repairs and maintenance for subsequent conditions of inadequate reliability and malfunctions. Therefore, instead of purchasing, many firms have considered obtaining large-scale equipment through leasing. Considering customization for heterogeneous consumers, which allows consumers to choose different leasing programs, firms can meet customer demands by enhancing their degree of satisfaction. This study considers customized leasing services and develops a pricing model for the leasing industry, in which the heterogeneity of customers, two-dimensional warranties, and preventive maintenance are taken into account for the purpose of establishing a customized leasing contract. Customers are categorized into three types according to their levels of usage of the leasing product: high, moderate, and low, and the lessor respectively provides the three corresponding leasing services. The various leasing services consist of different lease fees and preventive maintenance policies. The objective of this study is to assist the lessors in developing an optimal lease contract for heterogeneous lessees with different usage types. The results of the study show that when the penalties for higher usage customers who purchase lower-usage services are managed, offering a customized lease contract will elevate lessee willingness toward purchasing lease services, and therefore increase the total profit of the lessor.

This paper presents an efficient strategy to optimally allocate renewable energy sources (RES), primarily wind and solar photovoltaic (PV), and energy storage system (ESS) in electrical distributio...

Product functionality and physical structure are easily influenced by long storage times, which result in degradation, erosion, and deterioration. This study considers a two-echelon supply chain with one supplier and one retailer, in which products deteriorate over time and the retailer may encounter product shortages. This study contributes to current literature by developing a quantity discount-coordination mechanism that can efficiently determine an optimal long-term ordering policy, which should maximize the entire supply chain’s benefits. The results demonstrate that coordinating quantity discounts can reduce demand uncertainty, which can decrease the number of orders placed by the retailer and effectively increase the supply chain’s overall benefits. The retailer’s benefits increase due to the significant decrease in ordering and purchasing costs. However, the increased sales volume from offering a discount may not offset related expenses, which may decrease the supplier’s benefits. Therefore, the retailer must share its revenue with the supplier to enhance the latter’s willingness to coordinate.

The automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes. Highlights This work uses innovative methods in e-quality with the application of SVM. Many potential benefits of e-quality control have been presented and instantiated. The classifier equations are built on the data obtained from the experiments. A detailed analysis is presented for six different case studies. The results indicate the robustness of proposed SVM classification.

Demand to generate and analyze business decision rules based on dynamical data sets is desired.The proposed approach could efficiently generate updated rules without re-computation.Identification of features based on the customer's preference is summarized in the case study of service sector. In service industry application, there is vague and qualitative information required to be processed properly, for example, to identify customer preferences in order to provide adequate services. From literature, Rough Set Theory (RST) has been indicated to be one of promising approaches to cope with vagueness in a large scale database. Basically, the rough set approach integrates learning-from-example techniques, extracts rules from a data set of interest, and discovers data regularities. Most of the existing RS based approaches are able to implement rule induction but it is very time consuming from computation perspective particularly from a large database. To date, there is a demand to generate and analyze business decision rules based on dynamical data sets and conclude such rules on the daily basis in the service industry. Therefore, in this study, an Incremental Weight Incorporated Rule Identification (IWIRI) algorithm is proposed to fulfill such demand. The proposed approach is proficient to efficiently process in-coming data (objetcs) and generate updated decision rules without re-computation efforts in the database. Identification of features based on the customer's preference and implementation of the proposed algorithm are summarized in the case study. This paper forms the basis for solving many other similar problems that occur in service industries.

The increase of consumer needs for quality metal cutting related products with more precise tolerances and better product surface roughness has driven the metal cutting industry to continuously improve quality control of metal cutting processes. In this paper, two different approaches are discussed. First, design of experiments (DOE) is used to determine the significant factors and then fuzzy logic approach is presented for the prediction of surface roughness. The data used for the training and checking the fuzzy logic performance is derived from the experiments conducted on a CNC milling machine. In order to obtain better surface roughness, the proper sets of cutting parameters are determined before the process takes place. The factors considered for DOE in the experiment were the depth of cut, feed rate per tooth, cutting speed, tool nose radius, the use of cutting fluid and the three components of the cutting force. Finally the significant factors were used as input factors for fuzzy logic mechanism and surface roughness is predicted with empirical formula developed. Test results show good agreement between the actual process output and the predicted surface roughness. Highlights This work uses innovative methods in process prediction with the application of FST. Many potential benefits of fuzzy set theory have been presented and instantiated. The fuzzy set equations are built on the data obtained from the experiments. A detailed analysis is presented using experimental case study. The results indicate the good accuracy of proposed method.

This paper presents a reliable and secure supervisory control and data acquisition (SCADA) system equipped with advanced communication technologies (ACT) to enhance the operation and cyber security of the communication network in residential microgrid. The proposed approach uses the mobile ad hoc networks (MANET) for collecting data of power consumption from smart meters of residential areas and electric vehicles (EVs), and also for connecting mobile system operators to the network. Moreover, by understanding the dynamic nature of MANET and their exposure to cyber-attacks, we propose an intrusion detection and prevention (IDP) technology with secure knowledge algorithm and anomaly detection for preventing the black hole attacks, and other unknown attacks that result into packet dropping. Test results obtained by using Network Simulator (NS-2) demonstrate the effectiveness of the proposed IDP technology in preventing the cyber-attacks in the proposed residential microgrid communication network.

Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean-squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio test is developed to compare the difference of the two covariance matrices and to detect the steady state. The intensive numerical studies and real case study show that the proposed method can accurately detect the steady state of a multivariate system.