Modeling eye movement in dynamic interactive tasks for maximizing situation awareness based on Markov decision process.

For complex dynamic interactive tasks (such as aviating), operators need to continuously extract information from areas of interest (AOIs) through eye movement to maintain high level of situation awareness (SA), as failures of SA may cause task performance degradation, even system accident. Most of the current eye movement models focus on either static tasks (such as image viewing) or simple dynamic tasks (such as video watching), without considering SA. In this study, an eye movement model with the goal of maximizing SA is proposed based on Markov decision process (MDP), which is designed to describe the dynamic eye movement of experienced operators in dynamic interactive tasks. Two top-down factors, expectancy and value, are introduced into this model to represent the update probability and the importance of information in AOIs, respectively. In particular, the model regards sequence of eye fixations to different AOIs as sequential decisions to maximize the SA-related reward (value) in the context of uncertain information update (expectancy). Further, this model was validated with a flight simulation experiment. Results show that the predicted probabilities of fixation on and shift between AOIs are highly correlated ([Formula: see text] and [Formula: see text], respectively) with those of the experiment data.

Remaining capacity estimation of lithium-ion batteries based on the constant voltage charging profile.

Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization-based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.

Global Reliability Sensitivity Analysis Based on Maximum Entropy and 2-Layer Polynomial Chaos Expansion.

To optimize contributions of uncertain input variables on the statistical parameter of given model, e.g., reliability, global reliability sensitivity analysis (GRSA) provides an appropriate tool to quantify the effects. However, it may be difficult to calculate global reliability sensitivity indices compared with the traditional global sensitivity indices of model output, because statistical parameters are more difficult to obtain, Monte Carlo simulation (MCS)-related methods seem to be the only ways for GRSA but they are usually computationally demanding. This paper presents a new non-MCS calculation to evaluate global reliability sensitivity indices. This method proposes: (i) a 2-layer polynomial chaos expansion (PCE) framework to solve the global reliability sensitivity indices; and (ii) an efficient method to build a surrogate model of the statistical parameter using the maximum entropy (ME) method with the moments provided by PCE. This method has a dramatically reduced computational cost compared with traditional approaches. Two examples are introduced to demonstrate the efficiency and accuracy of the proposed method. It also suggests that the important ranking of model output and associated failure probability may be different, which could help improve the understanding of the given model in further optimization design.

Effects of the Manufacturing Process on the Reliability of the Multilayer Structure in MetalMUMPs Actuators: Residual Stresses and Variation of Design Parameters.

Potential problems induced by the multilayered manufacturing process pose a serious threat to the long-term reliability of MEMSCAP® actuators under in-service thermal cycling. Damage would initiate and propagate in different material layers because of a large mismatch of their thermal expansions. In this research, residual stresses and variations of design parameters induced by metal multi-user micro electromechanical system processes (MetalMUMPs) were examined to evaluate their effects on the thermal fatigue lifetime of the multilayer structure and, thus, to improve MEMSCAP® design. Since testing in such micro internal structure is difficult to conduct and traditional testing schemes are destructive, a numerical subdomain method based on a finite element technique was employed. Thermomechanical deformation from metal to insulator layers under in-service temperature cycling (obtained from the multiphysics model of the entire actuator, which was validated by experimental and specified analytical solutions) was accurately estimated to define failures with a significant efficiency and feasibility. Simulation results showed that critical failure modes included interface delamination, plastic deformation, micro cracking, and thermal fatigue, similarly to what was concluded in the MEMSCAP® technical report.