Thermal characterization of n‐alkyl maleate‐styrene‐allyl propionate terpolymers

Thermal characterization of maleic anhydride‐styrene‐allyl propionate (MA‐St‐AP) terpolymer and its ester derivatives named as n‐alkyl maleate and shown as nPr MA‐St‐AP, nBu MA‐St‐AP, nPn MA‐St‐AP, and nBz MA‐St‐AP was carried out. The thermal characterization was performed using thermal analysis techniques such as TGA, DTA, DSC, and TMA. Different results were observed between the original terpolymer and its ester derivatives. Thermal stabilities of the terpolymer and its ester derivatives were compared by using various measurements plotted as TGA, DTA, DSC, and TMA curves. The increase in the alcohols' carbon numbers added to the original terpolymer results in ester derivatives with different thermal stability behavior. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 600–604, 2007     

Polymeric absorbent for water sorption based on chemically crosslinked poly (acrylamide/2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt) hydrogels

Summary Swelling equilibrium of polyelectrolyte copolymer gels containing of acrylamide (AAm) and 2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt (AMPS) have been studied as a function of copolymer composition. AAm/AMPS hydrogels were prepared by free radical solution polymerization in aqueous solution of AAm with AMPS as anionic comonomer and two multifunctional crosslinkers such as ethylene glycol dimethacrylate (EGDMA) and trimethylolpropane triacrylate (TMPTA). Swelling experiments were performed in water at 25 °C, gravimetrically. The influence of AMPS content in hydrogels was examined. Swelling of AAm/AMPS hydrogels was increased up to 1018% (for containing 2% AMPS and crosslinked by EGDMA) 15246% (for containing 8% AMPS and crosslinked by TMPTA), while AAm hydrogels swelled up to 804% (crosslinked by TMPTA)–770% (crosslinked by EGDMA). The values of equilibrium water content of the hydrogels are 0.8851–0.9935. Diffusion behavior was investigated. Water diffusion into hydrogels was found to be non-Fickian in character.     

AHP and multichoice goal programming integration for course planning

Course planning is one of the important problems in the education systems of universities. The processes cannot continue efficiently without planning, and various interruptions can appear in the system. This way decisions concerning which courses, when, how, and for what purposes should be answered by considering the available resources and stakeholders’ preferences. Besides, universities aiming to be in the European Higher Education Area (EHEA) have to adapt their systems to the Bologna process in order to create a lifelong student‐centered, learning‐oriented area based on quality assurance. In this study, an integrated approach based on the analytic hierarchy process (AHP) and multichoice goal programming (MCGP) model was proposed to construct an efficient course plan following the Bologna process. The proposed approach was applied in an industrial engineering department.     

Interpretation of MR images using self-organizing maps and knowledge-based expert systems

A new image segmentation system is presented to automatically segment and label brain magnetic resonance (MR) images to show normal and abnormal brain tissues using self-organizing maps (SOM) and knowledge-based expert systems. Elements of a feature vector are formed by image intensities, first-order features, texture features extracted from gray-level co-occurrence matrix and multiscale features. This feature vector is used as an input to the SOM. SOM is used to over segment images and a knowledge-based expert system is used to join and label the segments. Spatial distributions of segments extracted from the SOM are also considered as well as gray level properties. Segments are labeled as background, skull, white matter, gray matter, cerebrospinal fluid (CSF) and suspicious regions.     

Preparation and characterization of conductive polypyrrole/kaolinite composites

Conductive polypyrrole (PPy)/kaolinite clay composites were prepared by in situ chemical polymerization of pyrrole in the presence of kaolinite using FeCl₃ as oxidant. The PPy content and conductivity of the composites reached 32.8% and 8.3×10^?2 S/cm at HCl concentrations of 1.5 M and 0.5 M, respectively. The microhardness of the composites containing different amounts of PPy was higher than that of the PPy and kaolinite components. The highest microhardness observed was 30.17 kg/mm² for the composite containing 9.6% PPy. The electrical resistance of the composites was monitored during heating–cooling cycles over the range 5–120 °C. The change in resistance with temperature was more repeatable for the composite than for PPy. The composites were characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The humidity-sensing properties were also examined.     

Adaptive lead time quotation in a pull production system with lead time responsive demand

In this paper, a single-stage single-class kanban controlled manufacturing system is considered, where the problem is quoting accurate lead times for orders that are generated by an MRP system. Order release mechanism as in classical MRP is sensitive to the changes in the lead time according to the concept of lead time syndrome. The objective is to establish a cost effective lead time quoting procedure. The problem is modeled as a two-dimensional Markov chain, and it is solved explicitly by using matrix geometric techniques. Comparative analysis is done between static and dynamic lead time quoting procedures, and significant cost benefits of the dynamic procedure are shown under various scenarios. Guidelines for setting design parameters such as the number of kanbans and the frequency of updating the lead time are provided through numerical tests.     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

Batch sequencing and cooperation

Game theoretic analysis of sequencing situations has been restricted to manufacturing systems which consist of machines that can process only one job at a time. However, in many manufacturing systems, operations are carried out by batch machines which can simultaneously process multiple jobs. This paper aims to extend the game theoretical approach to the cost allocation problems arising from sequencing situations on systems that consist of batch machines. To analyze the allocation problem at hand, it focusses on the existence of core elements, convexity, and the Shapley value.     

Uncertainty modeling and robust minimax LQR control of multivariable nonlinear systems with application to hypersonic flight

For a class of multi‐input and multi‐output nonlinear uncertainty systems, a novel approach to design a nonlinear controller using minimax linear quadratic regulator (LQR) control is proposed. The proposed method combines a feedback linearization method with the robust minimax LQR approach in the presence of time‐varying uncertain parameters. The uncertainties, which are assumed to satisfy a certain integral quadratic constraint condition, do not necessarily satisfy a generalized matching condition. The procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating point. This two‐stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided for a flight control problem of an air‐breathing hypersonic flight vehicle (AHFV), where the outputs to be controlled are the longitudinal velocity and altitude, and the control variables are the throttle setting and elevator deflection. The proposed method is used to derive a linearized uncertainty model for the longitudinal motion dynamics of the AHFV first, and then a robust minimax LQR controller is designed, which is based on this uncertainty model. The controller is synthesized considering seven uncertain aerodynamic and inertial parameters. The stability and performance of the synthesized controller is evaluated numerically via single scenario simulations for particular cruise conditions as well as a Monte‐Carlo type simulation based on numerous cases. It is observed that the control scheme proposed in this paper performs better, especially from the aspect of robustness to large ranges of uncertainties, than some controller design schemes previously published in the literature. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society