Online cage rotor fault detection using air-gap torque spectra

This paper discusses use of air-gap torque spectra as a means of identifying faults in cage rotors. Being dependent on both stator and rotor currents, the torque is very sensitive to faults in the rotor. Through a comparative study using a detailed machine model and the standard dq model, the paper shows that the characteristic frequencies generated by a particular fault are preserved even if the standard dq model is used for estimation of air-gap torque. This is validated through a practical hardware implementation for online spectrum estimation of air-gap torque using TMS320C31, where several faulted cage rotors were used for study.     

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time‐of‐Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple‐phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light‐ and voltage‐dependent time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) imaging of lateral perovskite heterostructures. While ToF‐SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non‐negative matrix factorization and non‐negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light‐ and voltage‐dependent dynamics. Combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.     

Subcritical and supercritical droplet evaporation within a zero-gravity environment: Low Weber number relative motion

A validated comprehensive axisymmetric numerical model, which includes the high pressure transient effects, variable thermo-physical properties and inert species solubility in the liquid phase, has been employed to study the evaporation of moving n-heptane droplets within a zero-gravity nitrogen environment, for a wide range of ambient pressures and initial freestream velocities. At the high ambient temperature considered (1000 K), the evaporation constant increases with the ambient pressure. At low ambient pressure, the evaporation constant becomes almost a constant during the end of the lifetime. At high ambient pressures, the transient behavior is present throughout the droplet lifetime. The final penetration distance of a moving droplet decreases exponentially with increasing ambient pressure. The average evaporation constant increases with ambient pressure. The variation is almost linear for reduced ambient pressures smaller than approximately 2. For higher values, depending on the initial freestream velocity, the average evaporation constant either becomes a constant (at low initial freestream velocities) or it non-linearly increases (at high initial freestream velocities) with the ambient pressure. Droplet lifetime decreases with increasing ambient pressure and/or increasing initial freestream velocity.     

Gaming Social Capital: Exploring Civic Value in Multiplayer Video Games

Recent research suggests that social interactions in video games may lead to the development of community bonding and prosocial attitudes. Building on this line of research, a national survey of U.S. adults finds that gamers who develop ties with a community of fellow gamers possess gaming social capital, a new gaming‐related community construct that is shown to be a positive antecedent in predicting both face‐to‐face social capital and civic participation.     

uWave: Accelerometer-based personalized gesture recognition and its applications

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.     

Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulations

Manufacturing facilities are expected to maintain a high level of production and at the same time, employ strict safety standards to ensure the safe evacuation of the people in the event of emergencies (fire is considered in this paper). These two goals are often conflicting. This paper presents a methodology to evaluate evacuation safety versus productivity concurrently for various, widely known manufacturing layouts. While the safety performance indicators such as evacuation times are inferred from the crowd (agent based) simulation, the productivity performance indicators (e.g. throughput) are analyzed using the discrete event simulation. To this end, this research focuses on creating innovative techniques for developing accurate crowd simulations, where Belief-Desire-Intention (BDI) agent framework is employed to build each person’s individual actions and the interactions between them. The data model and rule based action algorithms for each agent are reverse-engineered from the human-in-the-loop experiments in the immersive virtual reality environments. Finally, experiments are conducted using the constructed simulations to compare safety and productivity for different layouts. To demonstrate the proposed methodology, an automotive power-train (engine and transmission) manufacturing plant was used. Initial results look quite promising.     

Data fusion with Gaussian processes

This paper addresses the problem of fusing multiple sets of heterogeneous sensor data using Gaussian processes (GPs). Experiments on large scale terrain modeling in mining automation are presented. Three techniques in increasing order of model complexity are discussed. The first is based on adding data to an existing GP model. The second approach treats data from different sources as different noisy samples of a common underlying terrain and fusion is performed using heteroscedastic GPs. The final approach, based on dependent GPs, models each data set by a separate GP and learns spatial correlations between data sets through auto and cross covariances. The paper presents a unifying view of approaches to data fusion using GPs, a statistical evaluation that compares these approaches and multiple previously untested variants of them and an insight into the effect of model complexity on data fusion. Experiments suggest that in situations where data being fused is not rich enough to require a complex GP data fusion model or when computational resources are limited, the use of simpler GP data fusion techniques, which are constrained versions of the more generic models, reduces optimization complexity and consequently can enable superior learning of hyperparameters, resulting in a performance gain.     

Modeling Evaporation-Seepage Losses for Reservoir Water Balance in Semi-arid Regions

In the water balance of reservoir system, evaporation plays a crucial role particularly so for the reservoir systems of smaller size located in the semi-arid or arid regions. Such regions are most often characterized by significant seepage losses from reservoirs, besides evaporation losses. Usually, in the optimization of a reservoir system, it is a common practice to assume evaporation loss either as some constant value or as negligible. Such assumptions, however, may affect the results of reservoir optimization. This is demonstrated in this study by a case study in the optimal scheduling of Pilavakkal reservoir system in Vaipar basin of Tamilnadu, India. For modeling reservoir losses, many models are available, of which, Penman combination model is most commonly used. In this study, an alternative approach based on Genetic Programming (GP) is proposed. The results of GP and Penman model for both evaporation loss estimation and reservoir scheduling are compared. It is found that while GP and Penman combination model performs equally well for estimating evaporation losses, GP is also able to model seepage losses (or other losses from reservoir) to a much better degree. It is also shown the reservoir scheduling does get influenced based on how the reservoir losses are modeled in the reservoir water balance equation.     

Ultrasonic velocity measurements for characterizing the annealing behaviour of cold worked austenitic stainless steel

Precise measurements of shear wave velocities have been made in 20% cold worked and annealed samples of alloy D9 (Ti-modified austenitic stainless steel) to characterize the microstructural changes during annealing. The variation in wave velocity with annealing time exhibited a three stage behaviour. In the first stage, a slight increase in the velocity during recovery regime has been observed. This is followed by a significant increase in the velocity in the recrystallization regime (second stage) and reaching saturation on completion of recrystallization (third stage). Microstructural observations using optical metallography confirmed these three stages. The maximum variation in velocity is observed only when the polarization or the propagation direction of the shear wave is alingned with the cold working direction. Variation in shear wave velocity during annealing process, in general, is just opposite to that of the variation in longitudinal wave velocity. A number of velocity measurements were made by changing the propagation and polarization directions of the shear waves with respect to the cold working direction. Based on these measurements, a suitable velocity ratio parameter is suggested for determining the degree of recrystallization during annealing of cold worked alloy D9.