A novel soy-based epoxy resin system was synthesized by the process of transesterification and epoxidation of regular soy bean oil, which has the potential to be widely usable in various composite manufacturing processes. Cure kinetics and rheology are two chemical properties commonly required in process modeling. In this work, the cure kinetics and rheology of the soy-based resin system were measured by means of differential scanning calorimetry (DSC) and viscometer. DSC was used to measure the heat flow of dynamic and isothermal curing processes. The cure kinetics models of the different formulations were thus developed. A Brookfield viscometer was used to measure the change in viscosity under isothermal conditions. A novel neural network-based model was developed to improve modeling accuracy. The models developed for cure kinetics and rheology for soy-based epoxy resin system can be readily applied to composite processing. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 3168 -3180, 2006
Polymer matrix composites using renewable resources are currently of great interest. A novel soy-based resin system for pultrusion was synthesized at University of Missouri-Rolla (UMR). Due to relatively low reactivity of soy-based resin and short curing time of pultrusion, it is important to set up the process parameters properly in order to obtain a high quality pultruded product. In the present work, a combination of experimental and numerical methods is used to investigate the effect of pultrusion process variables on the cure of composites. A mathematical model has been developed and implemented in the commercial ABAQUS finite element package to predict the temperature and degree of cure of soy-based composites. The kinetic parameters of the soy-based epoxy resins are obtained from a differential scanning calorimeter (DSC). Glass fiber-reinforced composite samples are manufactured using a pultrusion machine. For experimental verification, an online monitoring system is used to measure the temperature profiles inside the die. The degrees of cure of pultruded soy-based composites are measured by DSC. The experimental findings are in good agreement with the finite element prediction. The results indicate that the novel soy-based epoxy resin system is suitable for application in the pultrusion process.
Abstract-The research on detection malware variants attracts much attention in recent years. However current variant classification methods either are interfered by some confusion technologies or have a high time or space complexity. In this paper, a classification technique using dynamic analysis based on behavior profile is proposed. We capture API calls and other essential information of running malware, then establish their multilayer dependency chain according to the dependency relationship of these function calls. In order to deal with the confusion, we remove sequence confusion, sequence noise, and other confusions to optimize the multilayer dependency chain. Finally, a similarity comparison algorithm is used to identify the degree of similarity between malware variants. The experimental results demonstrate that our classification technique is feasible and effective.Index Terms-Malware, variants, dependency chain.
To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.
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