Forces required to jettison a simulated S92 passenger exit: Optimal helicopter underwater egress training techniques

This paper outlines the first known examination of the forces required to jettison a simulated exit used during helicopter underwater egress training (HUET). To capture the forces placed on the simulated cabin exit, a purpose build force plate was designed to replace an existing simulator exit used during HUET. A 25-point map was created to identify specific jettison forces required across the entire exit surface. Ten participants completed a total of 120 underwater egress sequences in–air and in-water from a normal flight and fully compressed crash attenuating seat position. The results indicate that the force required to jettison the simulated exit is significantly different in relationship to location on the exit surface. From the results, it can be concluded that helicopter underwater egress training protocols should ensure that offshore candidates are informed of the different force requirements as well as have the opportunity to practice jettisoning a high physical fidelity exit from a fully compressed crash attenuating seat.     

Infrared study of salt free and salt treated cellulose pyrolysis under nitrogen atmosphere

The pyrolysis under nitrogen of salt free and salt treated cellulose was studied by infrared spectroscopy. The results confirm the unzipping mechanism suggested by DTA and TGA studies, and show this mechanism to apply to the untreated and salt treated samples. The results also show the unzipping to stop at the stage of char formation. The onset of this stage was found to occur after the volatilization of only 45–50% of the initial sample of the borax treated cellulose, compared with 65–70% of the KCl treated cellulose and 80–85% of the untreated cellulose.     

A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.     

Holistic schedulability analysis for multipacket messages in AFDX networks

The ARINC-664, Part 7 (AFDX) standard defines a communication network based on Ethernet and the UDP/IP protocols. Contrary to general-purpose Ethernet, the timing behavior in AFDX is deterministic due to the use of special network switches and end systems with static routing tables and traffic policing at the sending end through mechanisms called virtual links. Even though the latencies in this network are bounded, there are scheduling and contention effects that need to be analyzed. In this paper we develop a response-time analysis for multipacket messages transmitted through an AFDX network including the scheduling of the virtual links and sub-virtual links, and also the contention in the end systems and in the switches. This analysis allows us to obtain worst-case latencies and output jitter for the network messages with a precise modeling of the sending and receiving ends. These results can be integrated in a holistic approach with the response time analysis of the threads in the processing nodes to obtain end-to-end response times in heterogeneous distributed systems.     

Painting-91: a large scale database for computational painting categorization

Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms.     

Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Equi-affine Invariant Geometry for Shape Analysis

Traditional models of bendable surfaces are based on the exact or approximate invariance to deformations that do not tear or stretch the shape, leaving intact an intrinsic geometry associated with it. These geometries are typically defined using either the shortest path length (geodesic distance), or properties of heat diffusion (diffusion distance) on the surface. Both measures are implicitly derived from the metric induced by the ambient Euclidean space. In this paper, we depart from this restrictive assumption by observing that a different choice of the metric results in a richer set of geometric invariants. We apply equi-affine geometry for analyzing arbitrary shapes with positive Gaussian curvature. The potential of the proposed framework is explored in a range of applications such as shape matching and retrieval, symmetry detection, and computation of Voroni tessellation. We show that in some shape analysis tasks, equi-affine-invariant intrinsic geometries often outperform their Euclidean-based counterparts. We further explore the potential of this metric in facial anthropometry of newborns. We show that intrinsic properties of this homogeneous group are better captured using the equi-affine metric.     

Reinigung lösungsmittelhaltiger Abluft – alternative Möglichkeiten

Purification of solvents containing air – Alternative methods. Different processes are used for the purification of solvents containing air: condensation, absorption, adsorption, combustion, membrane permeation, and biofilters or bioscrubbers. The processes will be explained by examples. The specifications required by “TA Luft” often cannot be reached only by condensation and membrane permeation. The success of absorption depends on the scrubbing liquid. Glycol ethers are useful scrubbing liquids – especially for chlorinated hydrocarbons. The choice of the process depends on the volume flow rate, the solvent concentration, and the kind of solvent. Recycling of the solvents reduces the costs of the process.     

Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm

The lithium-ion battery cycle life prediction with particle filter (PF) depends on the physical or empirical model. However, in observation equation based on model, the adaptability and accuracy for individual battery under different operating conditions are not fully considered. Therefore, a novel fusion prognostic framework is proposed, in which the data-driven time series prediction model is adopted as observation equation, and combined to PF algorithm for lithium-ion battery cycle life prediction. Firstly, the nonlinear degradation feature of the lithium-ion battery capacity degradation is analyzed, and then, the nonlinear accelerated degradation factor is extracted to improve prediction ability of linear AR model. So an optimized nonlinear degradation autoregressive (ND–AR) time series model for remaining useful life (RUL) estimation of lithium-ion batteries is introduced. Then, the ND–AR model is used to realize multi-step prediction of the battery capacity degradation states. Finally, to improve the uncertainty representation ability of the standard PF algorithm, the regularized particle filter is applied to design a fusion RUL estimation framework of lithium-ion battery. Experimental results with the lithium-ion battery test data from NASA and CALCE (The Center for Advanced Life Cycle Engineering, the University of Maryland) show that the proposed fusion prognostic approach can effectively predict the battery RUL with more accurate forecasting result and uncertainty representation of probability density distribution (pdf).