A mechanistic study of strain rate sensitivity and high rate property of tendon

The ultrastructural mechanism for strain rate sensitivity of collagenous tissue has not been well studied at the collagen fibril level. The objective is to reveal the mechanistic contribution of the collagen fibril to strain rate sensitivity. Collagen fibrils underwent significantly greater fibril strain relative to global tissue strain at higher strain rates. A better understanding of tendon mechanisms at lower hierarchical levels would help establish a basis for future development of constitutive models and assist in tissue replacement design. High rate mechanical property of tendon was also studied. Tendon was compressed under high strain rate (550 /s) using a polycarbonate split Hopkinson pressure bar (PSHPB). The objectives are to investigate the tissue behavior of porcine tendon at high rates. Tendon’s high rate behavior was compared with brain and liver at both hydrated and dehydrated states to investigate how water content and ultrastructural affect high rate responses of soft tissues

A mechanistic study of strain rate sensitivity and high rate property of tendon

The ultrastructural mechanism for strain rate sensitivity of collagenous tissue has not been well studied at the collagen fibril level. The objective is to reveal the mechanistic contribution of the collagen fibril to strain rate sensitivity. Collagen fibrils underwent significantly greater fibril strain relative to global tissue strain at higher strain rates. A better understanding of tendon mechanisms at lower hierarchical levels would help establish a basis for future development of constitutive models and assist in tissue replacement design. High rate mechanical property of tendon was also studied. Tendon was compressed under high strain rate (550 /s) using a polycarbonate split Hopkinson pressure bar (PSHPB). The objectives are to investigate the tissue behavior of porcine tendon at high rates. Tendon’s high rate behavior was compared with brain and liver at both hydrated and dehydrated states to investigate how water content and ultrastructural affect high rate responses of soft tissues

Multivariate Stochastic Approximation to Tune Neural Network Hyperparameters for Criticial Infrastructure Communication Device Identification

The e-government includes Wireless Personal Area Network (WPAN) enabled internet-to-government pathways. Of interest herein is Z-Wave, an insecure, low-power/cost WPAN technology increasingly used in critical infrastructure. Radio Frequency (RF) Fingerprinting can augment WPAN security by a biometric-like process that computes statistical features from signal responses to 1) develop an authorized device library, 2) develop classifier models and 3) vet claimed identities. For classification, the neural network-based Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is employed. GRLVQI has shown high fidelity in classifying Z-Wave RF Fingerprints; however, GRLVQI has multiple hyperparameters. Prior work optimized GRLVQI via a full factorial experimental design. Herein, optimizing GRLVQI via stochastic approximation, which operates by iterative searching for optimality, is of interest to provide an unconstrained optimization approach to avoid limitations found in full factorial experimental designs. The results provide an improvement in GRLVQI operation and accuracy. The methodology is further generalizable to other problems and algorithms

Order Fulfillment Errors and Military Aircraft Readiness

This paper aims to measure the effect of supply discrepancy reports (SDRs) on military aircraft readiness metrics, including aircraft availability, not mission capable supply (NMCS) hours, cannibalizations and mission-impaired capability awaiting parts (MICAP) hours. Monthly SDR, NMCS, aircraft cannibalizations and MICAP data from 2009 to 2018 are analyzed using linear regression and independent samples t-tests to examine whether discrepant shipments negatively impact aircraft readiness

Easy and Efficient Hyperparameter Optimization to Address Some Artificial Intelligence “ilities”

Artificial Intelligence (AI), has many benefits, including the ability to find complex patterns, automation, and meaning making. Through these benefits, AI has revolutionized image processing among numerous other disciplines. AI further has the potential to revolutionize other domains; however, this will not happen until we can address the “ilities”: repeatability, explain-ability, reliability, use-ability, trust-ability, etc. Notably, many problems with the “ilities” are due to the artistic nature of AI algorithm development, especially hyperparameter determination. AI algorithms are often crafted products with the hyperparameters learned experientially. As such, when applying the same algorithm to new problems, the algorithm may not perform due to inappropriate settings. This research aims to provide a straightforward and reliable approach to automatically determining suitable hyperparameter settings when given an AI algorithm. Results, show reasonable performance is possible and end-to-end examples are given for three deep learning algorithms and three different data problems

Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; The depletion of natural resources has necessitated a better management of resources. One of the methods in this regard has been the reuse of materials and parts from products at the end of their life cycles, which requires a suitable configuration of disassembly systems for an effective operation. In this paper, we compare performances of two types of system configurations: standalone tear-down-stations and disassembly lines. These system configurations are tested for the disassembly of class 8 trucks to recover parts, which are then remanufactured or refurbished for reuse. A key feature of this product, and that of a used product in general that is disassembled, is the uncertainty of the processing time of a disassembly step. This uncertainty can lead to difficulties in proper line balancing, bottlenecks, inefficient use of resources, and generally, reduced throughput. In order to overcome these limitations, in this paper, we investigate the above disassembly facility configurations, and determine how their performances are affected by variability in operation time

Estimating demand for substitutable products when inventory records are unreliable

We present a procedure for estimating demand for substitutable products when the inventory record is unreliable and only validated infrequently and irregularly. The procedure uses a structural model of demand and inventory progression, which is estimated using a modied version of the Expectation Maximization-method. The procedure leads to asymptotically unbiased estimates without any restrictive assumptions about substitution patterns or that inventory records are periodically known with certainty. The procedure converges quickly also for large product categories, which makes it suitable for implementation at retailers or manufacturers that need to run the analysis for hundreds of categories or stores at the same time. We use the procedure to highlight the importance of considering inventory reliability problems when estimating demand, rst through simulation and then by applying the procedure to a data set from a major US retailer. The results show that for the product category in consideration, ignoring inventory reliability problems leads to demand estimates that on average underestimate demand by 5%. It also results in total lost sales estimates that account for only a fraction of actual lost sales