Automobile air bag inflation system based on fast combustion reaction

Current automobile air bag inflator technology is complex, expensive and environmentally unsafe. A new and novel air bag inflator based on fast combustion reactions of methane-oxygen mixtures has been developed and studied. The thermodynamics and mass flow parameters of this new inflator have been modeled and found to be in agreement with experimental results. The performance of the fast combustion inflator was evaluated in terms of pressure-time relationships inside the inflator and in a receiving tank simulating an air bag as well as the temperature-time relationship in the tank. In order to develop this fast combustion inflator, several critical issues were studied and evaluated. These included the effects of stoichiometry, initial mixture pressure and extreme hot and cold conditions. Other design and practical parameters, such as burst disk thickness and type, ignition device, tank purging gas, concentration of carbon monoxide produced and severity of temperature in the tank were also studied and optimized. Several inflator sizes were investigated and found to meet most of the requirements for a successful air bag inflator. A theoretical and integrated model has been developed to simulate the transient pressure and temperature as well as the mass flow rate from the inflator to the tank. The model is based on the change in the internal energy inside the inflator and the receiving tank as the mass flows from the inflator to the tank. The model utilizes the Chemical Equilibrium Compositions and Applications code developed by NASA to estimate the equilibrium conditions in the inflator. A large volume of experimental results made at different conditions were found to be in agreement with the integrated model. The fast combustion inflator developed during this research is simple in principle and construction and is environmentally attractive

Expressive Talking Head Video Encoding in StyleGAN2 Latent-Space

While the recent advances in research on video reenactment have yielded promising results, the approaches fall short in capturing the fine, detailed, and expressive facial features (e.g., lip-pressing, mouth puckering, mouth gaping, and wrinkles) which are crucial in generating realistic animated face videos. To this end, we propose an end-to-end expressive face video encoding approach that facilitates data-efficient high-quality video re-synthesis by optimizing low-dimensional edits of a single Identity-latent. The approach builds on StyleGAN2 image inversion and multi-stage non-linear latent-space editing to generate videos that are nearly comparable to input videos. While existing StyleGAN latent-based editing techniques focus on simply generating plausible edits of static images, we automate the latent-space editing to capture the fine expressive facial deformations in a sequence of frames using an encoding that resides in the Style-latent-space (StyleSpace) of StyleGAN2. The encoding thus obtained could be super-imposed on a single Identity-latent to facilitate re-enactment of face videos at $1024^2$. The proposed framework economically captures face identity, head-pose, and complex expressive facial motions at fine levels, and thereby bypasses training, person modeling, dependence on landmarks/ keypoints, and low-resolution synthesis which tend to hamper most re-enactment approaches. The approach is designed with maximum data efficiency, where a single $W+$ latent and 35 parameters per frame enable high-fidelity video rendering. This pipeline can also be used for puppeteering (i.e., motion transfer).Comment: The project page is located at https://trevineoorloff.github.io/ExpressiveFaceVideoEncoding.io

One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2

While recent research has progressively overcome the low-resolution constraint of one-shot face video re-enactment with the help of StyleGAN's high-fidelity portrait generation, these approaches rely on at least one of the following: explicit 2D/3D priors, optical flow based warping as motion descriptors, off-the-shelf encoders, etc., which constrain their performance (e.g., inconsistent predictions, inability to capture fine facial details and accessories, poor generalization, artifacts). We propose an end-to-end framework for simultaneously supporting face attribute edits, facial motions and deformations, and facial identity control for video generation. It employs a hybrid latent-space that encodes a given frame into a pair of latents: Identity latent, $\mathcal{W}_{ID}$, and Facial deformation latent, $\mathcal{S}_F$, that respectively reside in the $W+$ and $SS$ spaces of StyleGAN2. Thereby, incorporating the impressive editability-distortion trade-off of $W+$ and the high disentanglement properties of $SS$. These hybrid latents employ the StyleGAN2 generator to achieve high-fidelity face video re-enactment at $1024^2$. Furthermore, the model supports the generation of realistic re-enactment videos with other latent-based semantic edits (e.g., beard, age, make-up, etc.). Qualitative and quantitative analyses performed against state-of-the-art methods demonstrate the superiority of the proposed approach.Comment: The project page is located at https://trevineoorloff.github.io/FaceVideoReenactment_HybridLatents.io

COVID-VTS: Fact Extraction and Verification on Short Video Platforms

We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation. We propose, TwtrDetective, an effective model incorporating cross-media consistency checking to detect token-level malicious tampering in different modalities, and generate explanations. Due to the scarcity of training data, we also develop an efficient and scalable approach to automatically generate misleading video posts by event manipulation or adversarial matching. We investigate several state-of-the-art models and demonstrate the superiority of TwtrDetective.Comment: 11 pages, 5 figures, accepted to EACL202

Electrodermal Activity: Simultaneous Recordings

Electrodermal activity (EDA) is a sensitive measure of the sympathetic nervous system activity. It is used to describe changes in the skin electrical properties. This chapter aimed to show advantages of simultaneous recordings of EDA parameters at the same skin site over other recordings. The literature databases, Web of Science and Google Scholar, were searched using terms like “electrodermal activity,” “sequential recording,” “simultaneous recording,” “skin conductance,” “skin potential,” and “skin susceptance.” Articles that include sequential and/or simultaneous recording of EDA parameters were analyzed. The chapter presents a description of the oldest and current methods used for recording EDA parameters and an explanation of the newest techniques used in EDA researches. Although sequential recordings are predominant and widely spreading, much effort has been made to simultaneously record skin conductance (SC) and skin potential (SP), and recently researchers realized the capability of simultaneously recording SC, SP, and skin susceptance (SS) at the same skin site. The advantage of simultaneous over the sequence measurements is that the latter must be manually time realigned when measured by different instruments, which means it is time-consuming. Although the simultaneous measurements are used exclusively for research purposes at this stage, this may open horizons in the modern trends of psychophysiology applications in the near future

Bioelectrical impedance method for assessing later body-composition considering the influence of breastfed on gender

Purpose: to study the effect of breastfeeding on body composition in both male and female via bioimpedance analysis method. analysis methods. Methods: we determined the body composition (body fat percentage, muscle mass percentage, and bone mass percentage) by bioimpedance analysis methods in 60 adults (31 male and 29 female) who were classified as underweight, normal and overweight subjects aged 20 years old. Average BMI was 21.4 Kg/ m2 which calculated by traditional formula. Findings: The differences in %BF assessed using bipolar analyzers were significantly higher for female than male. There was a significant difference (