A resilience optimization approach for workforce-inventory control dynamics under uncertainty

The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.     

Straightforward modeling of dynamic I–V characteristics for microwave FETs

This work presents a straightforward approach aimed at modeling the dynamic I–V characteristics of microwave active solid‐state devices. The drain‐source current generator represents the most significant source of nonlinearity in a transistor and, therefore, its correct modeling is fundamental to predict accurately the current and voltage waveforms under large‐signal operation. The proposed approach relies on using a small set of low‐frequency time‐domain waveform measurements combined with numerical optimization‐based estimation of the nonlinear model parameters. The procedure is applied to a gallium nitride HEMT and silicon FinFET. The effectiveness of the modeling procedure in terms of prediction accuracy and generalization capability is demonstrated by validation of the extracted models under operating conditions different than the ones used for the parameters estimation. Good agreement between measurements and model simulations is achieved for both technologies and in both low‐ and high‐frequency range. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:109–116, 2014.     

Synthesis of antenna arrays with an efficient multiobjective differential evolution algorithm

An efficient multiobjective differential evolution algorithm for array synthesis is proposed in this article. By introducing novel strategies in the generalized differential evolution (GDE3), the proposed algorithm can generate a better converged and distributed Pareto front than the standard GDE3. Five typical benchmark functions are optimized by the proposed improved GDE3 (IGDE) to verify its effectiveness. Furthermore, the proposed algorithm is applied in two‐pattern synthesis problems, including an unequal spaced linear array and a conformal array. The superior performance of the proposed IGDE demonstrates that it is an efficient and competitive algorithm in the function optimization, pattern synthesis, and other electromagnetic problems. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:161–169, 2014.     

Low‐velocity impact of nanocomposite and polymer plates

Nanocomposites are more widely studied today because of higher stiffness, decreased permeability, thermal stability, and many other properties superior to those of regular polymers. However, manufacturers are concerned about implementing nanocomposites because of their lower impact properties with respect to the base polymer. This study focused on low‐velocity impact tests of a thermoplastic olefin by itself and with 5 wt % nanoclay. The impact tests were conducted at ?40, 23.9, and 65.6°C until the polymer and nanocomposite plates experienced complete striker penetration. The force–time and force–deflection responses obtained from the impact testing provided a means of comparing the impact performances of the two materials. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 2309–2315, 2005     

Controlled destruction of residual crosslinker in a silicone elastomer for drug delivery

In drug delivery systems that use silicone elastomers as a diffusion matrix for the active drug, it is common to crosslink the material by the hydrosilylation reaction. In this platinum‐catalyzed reaction, vinyl groups on a polymer add to the methyl siloxane hydride (MHS) groups on a low molecular mass crosslinker. With an excess of crosslinker, a fast curing is achieved and a fully crosslinked material is formed. Unreacted MHS groups were shown to remain in the elastomer after curing because of the excess crosslinker. In this work, a simple procedure was developed to eliminate the unreacted MHS groups from the final product. We found that storage of the product at +40°C and 75% relative humidity for a few weeks will effectively destroy the residual MHS groups in the elastomer. The effects of varying levels of humidity, oxygen, and temperature on this postcuring procedure were studied. The amount of MHS groups was measured with NMR and IR spectroscopy. We also found that the hardness of the material increased by approximately 25% as a consequence of this postcuring treatment. This increase is probably due to a secondary crosslinking reaction between MHS and silanol groups. Heat treatment at higher temperatures led to an even further increase in the hardness and compression modulus. Because no MHS groups remained in the elastomer when this heat treatment was started, it is apparent that another secondary crosslinking reaction is occurring, probably silanol condensation. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 2254–2264, 2002     

Persistent problems and practices in information systems development: a study of mobile applications development and distribution

The widespread uptake of mobile technologies has witnessed a re‐structuring of the mobile market with major shifts in the predominance of particular firms and the emergence of new business models. These sociotechnical trends are significant in the ways that they are influencing and shaping the working lives of software professionals. Building on prior research investigating the persistent problems and practices of systems development, this paper examines mobile applications development and distribution. A qualitative study of 60 developers based in Sweden, the UK and the USA was analysed around the interrelated problems of diversity, knowledge and structure. The analysis revealed how platform‐based development in an evolving mobile market represents significant changes at the business environment level. These changes ripple through and accentuate ongoing trends and developments, intensifying the persistent problems and challenges facing software developers.     

Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing recommendation system performing the recommendation using the information directly input by users and application usage record only. This study develops a context-aware model using the context information provided by the context information model. The study applies it to the extraction of the missing value in a collaborative filtering process. The context-aware model reflects the information that selects items by users according to the appropriate context using the C-HMM and provides it to users. The solution of the missing value in the preference significantly affects the recommendation accuracy in a preference based item supply method. Thus, this study developed a new collaborative filtering for ubiquitous environments by reflecting the missing preference value and reflecting it to the collaborative filtering using the context-aware model. Also, the validity of this method will be evaluated by applying it to menu services in u-healthcare services.     

Adsorption and desorption kinetics of hydrocarbons in FCC catalysts studied using a tapered element oscillating microbalance (TEOM). Part 2: numerical simulations

The factors affecting the adsorption and desorption kinetics in a TEOM are reviewed in detail with particular attention given to the assumptions required to obtain physical transport parameters from the data. Two models are presented to simulate TEOM adsorption data in the case that concentration differences down the catalyst bed can be neglected, as is appropriate when the amount of catalyst used is small, the carrier gas flowrate is large, and/or the adsorbate partial pressure is low. In the first model, the effective diffusion coefficient, D_e, is taken to be constant. In the second model, the effective diffusion coefficient is assumed to obey the Darken equation, D_e=D₀/(1−θ). The TEOM results obtained on n-hexane, n heptane, n-octane, toluene and p-xylene on a commercial FCC catalyst and on pure rare-earth exchanged zeolite Y under non-reacting conditions (373-) are analysed in detail. It is found that intracrystalline diffusion is not the limiting factor affecting the overall rates of adsorption and desorption for the systems studied. Instead, it is the transport of molecules between the adsorbed and vapour phases at the edge of zeolite crystallites that is the limiting transport step affecting the overall kinetics. For the FCC catalyst, the limiting step is the transport of molecules at the zeolite-matrix interface rather than, say, the matrix-vapour interface. Local rate constants for the desorption of the hydrocarbons at the rate-controlling interface have been obtained.     

Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants’ context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants’ context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.     

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.