In order to enhance the compatibilization and interfacial adhesion between epoxy and liquid carboxyl-terminated butadiene acrylonitrile (CTBN) rubber, an initiator was introduced into the mixture and heated to initiate the cross-linking reaction of CTBN. After the addition of curing agents, the CTBN/epoxy blends with a localized interpenetrating network structure were prepared. The mechanical p...

The continuous time Bayesian network (CTBN) model can be thought of as a factored Markov process. Sensitivity analysis of a Markov process is done by calculating partial derivatives of a user-defined performance function with respect to changes in the transition intensities of the Markov process. On the other hand, sensitivity analysis has yet to be applied to the CTBN model. To address this, w...

We show how to perform sensitivity analysis on continuous time Bayesian networks (CTBNs) as applied specifically to reliability models. Sensitivity analysis of these models can be used, for example, to measure how uncertainty in the failure rates impact the reliability of the modeled system. The CTBN can be thought of as a type of factored Markov process that separates a system into a set of in...

The main purpose of this work is to reveal the effects carboxyl-terminated butadiene–acrylonitrile (CTBN) rubber particles on fracture and tensile behavior anhydride-and amine-cured epoxy/CTBN blends. In study, 1 wt.%, 3 5 7 10 wt.% 15 CTBN were added two different epoxy-hardener systems. CTBN/epoxy blends prepared by ultrasonic mixing device curing processes determined DSC analysis. As fractio...

We present a continuous time Bayesian network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian network (CTBN) provides a compact (factored) description of a continuoustime Markov process. This software provides libraries and programs for most of the algorithms developed for CTBNs. For learning, CTBN-RLE implements structure and parameter learning for both complete and partia...

The creep and recovery of 5% carboxyl-terminated butadiene acrylonitrile (CTBN) modified epoxy shape memory polymers (SMP) was studied. The results were compared with those for the unmodified epoxy SMP. The glass transition temperatures (Tg) were determined using Advanced Rheometric Expansion System (ARES). The creep behavior of the unmodified epoxy and 5wt.% CTBN modified epoxy SMP were studie...

Extensive efforts have been devoted to recognizing facial action units (AUs). However, it is still challenging to recognize AUs from spontaneous facial displays especially when they are accompanied with speech. Different from all prior work that utilized visual observations for facial AU recognition, this paper presents a novel approach that recognizes speech-related AUs exclusively from audio ...

The continuous time Bayesian network (CTBN) is a probabilistic graphical model that enables reasoning about complex, interdependent, and continuous-time subsystems. The model uses nodes to denote subsystems and arcs to denote conditional dependence. This dependence manifests in how the dynamics of a subsystem change based on the current states of its parents in the network. While the original C...

The continuous time Bayesian network (CTBN) enables temporal reasoning by representing a system as a factored, finite-state Markov process. The CTBN uses a traditional Bayesian network (BN) to specify the initial distribution. Thus, the complexity results of Bayesian networks also apply to CTBNs through this initial distribution. However, the question remains whether propagating the probabiliti...

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters...