An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005.     

A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.     

Surface modification of ultra‐high‐molecular‐weight polyethylene. II. Effect on the physicomechanical and tribological properties of ultra‐high‐molecular‐weight polyethylene/poly(ethylene terephthalate) composites

We performed surface modification of ultra‐high‐molecular‐weight polyethylene (UHMWPE) through chromic acid etching, with the aim of improving the performance of its composites with poly(ethylene terephthalate) (PET) fibers. In this article, we report on the morphology and physicomechanical and tribological properties of modified UHMWPE/PET composites. Composites containing chemically modified UHMWPE had higher impact properties than those based on unmodified UHMWPE because of improved interfacial bonding between the polymer matrix and the fibers and better dispersion of the fibers within the modified UHMWPE matrix. Chemical modification of UHMWPE before the introduction of PET fibers resulted in composites exhibiting improved wear resistance compared to the base material and compared to unmodified UHMWPE/PET composites. On the basis of the morphological studies of worn samples, microploughing and fatigue failure associated with microcracking were identified as the principle wear mechanisms. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci, 2006     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique (HEART) is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.     

Semantic weaving of configuration fragments into a consistent system configuration

Configuration fragments developed separately and focusing on different aspects, such as availability, security or performance of a system need to be integrated into a consistent system configuration to avoid system malfunctions. The main challenges of such integration are due to the overlapping entities and the integration relations between the entities of the different configuration fragments. In this paper we propose a model based approach for a consistent integration of configuration fragments into a system configuration. We use and extend the model weaving technique to capture the semantics of the relations between the entities of the configuration fragments. Moreover, we generate automatically the constraints corresponding to these semantic relations to complete the target system configuration profile. These constraints can be used to guard the configuration consistency during runtime modifications.     

Active XML-based Web data integration

Today, the Web is the largest source of information worldwide. There is currently a strong trend for decision-making applications such as Data Warehousing (DW) and Business Intelligence (BI) to move onto the Web, especially in the cloud. Integrating data into DW/BI applications is a critical and time-consuming task. To make better decisions in DW/BI applications, next generation data integration poses new requirements to data integration systems, over those posed by traditional data integration. In this paper, we propose a generic, metadata-based, service-oriented, and event-driven approach for integrating Web data timely and autonomously. Beside handling data heterogeneity, distribution and interoperability, our approach satisfies near real-time requirements and realize active data integration. For this sake, we design and develop a framework that utilizes Web standards (e.g., XML and Web services) for tackling data heterogeneity, distribution and interoperability issues. Moreover, our framework utilizes Active XML (AXML) to warehouse passive data as well as services to integrate active and dynamic data on-the-fly. AXML embedded services and changes detection services ensure near real-time data integration. Furthermore, the idea of integrating Web data actively and autonomously revolves around mining events logged by the data integration environment. Therefore, we propose an incremental XML-based algorithm for mining association rules from logged events. Then, we define active rules dynamically upon mined data to automate and reactivate integration tasks. Finally, as a proof of concept, we implement a framework prototype as a Web application using open-source tools.     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

Inhibition effect of a synthesized N, N＇-bis（2-hydroxybenzaldehyde）- 1, 3-propandiimine on corrosion of mild steel in HC1

The corrosion inhibition of mild steel in 1 mol/L HCl by N, N′-bis (2-hydroxybenzaldehyde)-1, 3-propandiimine (2-HBP) has been investigated by using potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and chronoamperometry measurements. The experimental results suggest that this compound is an excellent corrosion inhibitor for mild steel and the inhibition efficiency increases with the increase in inhibitor concentration. Polarization curves reveal that this organic compound is a mixed type inhibitor. The effect of temperature on the corrosion behavior of mild steel with the addition of the Schiff base was studied in the temperature range from 25℃ to 65℃. The experimentally obtained adsorption isotherms follow the Langmuir equation. Activation and thermodynamic adsorption parameters such as E a , ΔH, ΔS, K ads and ΔG ads were calculated by the corrosion currents at different temperatures and using the adsorption isotherm. The morphology of mild steel surface in the absence and presence of 2-HBP was examined by atomic force microscope (AFM) images.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.