Showing papers by "Missouri University of Science and Technology published in 2015"

Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations

Abstract: Additive manufacturing (AM), the process of joining materials to make objects from three-dimensional (3D) model data, usually layer by layer, is distinctly a different form and has many advantages over traditional manufacturing processes. Commonly known as “3D printing,” AM provides a cost-effective and time-efficient way to produce low-volume, customized products with complicated geometries and advanced material properties and functionality. As a result of the 2013 National Science Foundation (NSF) Workshop on Frontiers of Additive Manufacturing Research and Education, this paper summarizes AM's current state, future potential, gaps and needs, as well as recommendations for technology and research, university–industry collaboration and technology transfer, and education and training.

A Unified in Vitro Evaluation for Apatite-Forming Ability of Bioactive Glasses and Their Variants

Abstract: The aim of this study was to propose and validate a new unified method for testing dissolution rates of bioactive glasses and their variants, and the formation of calcium phosphate layer formation on their surface, which is an indicator of bioactivity At present, comparison in the literature is difficult as many groups use different testing protocols An ISO standard covers the use of simulated body fluid on standard shape materials but it does not take into account that bioactive glasses can have very different specific surface areas, as for glass powders Validation of the proposed modified test was through round robin testing and comparison to the ISO standard where appropriate The proposed test uses fixed mass per solution volume ratio and agitated solution The round robin study showed differences in hydroxyapatite nucleation on glasses of different composition and between glasses of the same composition but different particle size The results were reproducible between research facilities Researchers should use this method when testing new glasses, or their variants, to enable comparison between the literature in the future

Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks

Abstract: Human physical activity recognition based on wearable sensors has applications relevant to our daily life such as healthcare. How to achieve high recognition accuracy with low computational cost is an important issue in the ubiquitous computing. Rather than exploring handcrafted features from time-series sensor signals, we assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to automatically learn the optimal features from the activity image for the activity recognition task. Our proposed approach is evaluated on three public datasets and it outperforms state-of-the-arts in terms of recognition accuracy and computational cost.

Beam Division Multiple Access Transmission for Massive MIMO Communications

Abstract: We study multicarrier multiuser multiple-input multiple-output (MU-MIMO) systems, in which the base station employs an asymptotically large number of antennas. We analyze a fully correlated channel matrix and provide a beam domain channel model, where the channel gains are independent of sub-carriers. For this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate, based on which, we develop asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter. Furthermore, we propose a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams. By selecting users within non-overlapping beams, the MU-MIMO channels can be equivalently decomposed into multiple single-user MIMO channels; this scheme significantly reduces the overhead of channel estimation, as well as, the processing complexity at transceivers. For BDMA transmission, we work out an optimal pilot design criterion to minimize the mean square error (MSE) and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. Simulations demonstrate the near-optimal performance of BDMA transmission and the advantages of the proposed pilot sequences.

Structural color printing based on plasmonic metasurfaces of perfect light absorption

Abstract: Subwavelength structural color filtering and printing technologies employing plasmonic nanostructures have recently been recognized as an important and beneficial complement to the traditional colorant-based pigmentation. However, the color saturation, brightness and incident angle tolerance of structural color printing need to be improved to meet the application requirement. Here we demonstrate a structural color printing method based on plasmonic metasurfaces of perfect light absorption to improve color performances such as saturation and brightness. Thin-layer perfect absorbers with periodic hole arrays are designed at visible frequencies and the absorption peaks are tuned by simply adjusting the hole size and periodicity. Near perfect light absorption with high quality factors are obtained to realize high-resolution, angle-insensitive plasmonic color printing with high color saturation and brightness. Moreover, the fabricated metasurfaces can be protected with a protective coating for ambient use without degrading performances. The demonstrated structural color printing platform offers great potential for applications ranging from security marking to information storage.

An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments

Abstract: Smart energy in buildings is an important research area of Internet of Things (IoT). As important parts of the smart grids, the energy efficiency of buildings is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect 1-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be “green” by design. Inspired by “energy proportional computing” in modern computers, we propose an IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud-computing technologies to enable multiscale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments, which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.

k -Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Abstract: Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure $k$ -NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user’s input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure $k$ -NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings.

Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces

Abstract: Development and applications of neural network (NN)-based approaches for representing potential energy surfaces (PES) of bound and reactive molecular systems are reviewed. Specifically, it is shown that when the density of ab initio points is low, NNs-based potentials with multibody or multimode structure are advantageous for representing high-dimensional PESs. Importantly, with an appropriate choice of the neuron activation function, PESs in the sum-of-products form are naturally obtained, thus addressing a bottleneck problem in quantum dynamics. The use of NN committees is also analyzed and it is shown that while they are able to reduce the fitting error, the reduction is limited by the nonrandom nature of the fitting error. The approaches described here are expected to be directly applicable in other areas of science and engineering where a functional form needs to be constructed in an unbiased way from sparse data. © 2014 Wiley Periodicals, Inc.

Characterization of Gold Nanoparticle Uptake by Tomato Plants Using Enzymatic Extraction Followed by Single-Particle Inductively Coupled Plasma–Mass Spectrometry Analysis

Abstract: Plant uptake and accumulation of nanoparticles (NPs) represent an important pathway for potential human expose to NPs. Consequently, it is imperative to understand the uptake of accumulation of NPs in plant tissues and their unique physical and chemical properties within plant tissues. Current technologies are limited in revealing the unique characteristics of NPs after they enter plant tissues. An enzymatic digestion method, followed by single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) analysis, was developed for simultaneous determination of gold NP (AuNP) size, size distribution, particle concentration, and dissolved Au concentration in tomato plant tissues. The experimental results showed that Macerozyme R-10 enzyme was capable of extracting AuNPs from tomato plants without causing dissolution or aggregation of AuNPs. The detection limit for quantification of AuNP size was 20 nm, and the AuNP particle concentration detection limit was 1000 NPs/mL. The particle concentration recoveries of spiked AuNPs were high (79-96%) in quality control samples. The developed SP-ICP-MS method was able to accurately measure AuNP size, size distribution, and particle concentration in the plant matrix. The dosing study indicated that tomato can uptake AuNPs as intact particles without alternating the AuNP properties.

SOx/NOx Removal from Flue Gas Streams by Solid Adsorbents: A Review of Current Challenges and Future Directions

Abstract: One of the main challenges in the power and chemical industries is to remove generated toxic or environmentally harmful gases before atmospheric emission. To comply with stringent environmental and pollutant emissions control regulations, coal-fired power plants must be equipped with new technologies that are efficient and less energy-intensive than status quo technologies for flue gas cleanup. While conventional sulfur oxide (SOx) and nitrogen oxide (NOx) removal technologies benefit from their large-scale implementation and maturity, they are quite energy-intensive. In view of this, the development of lower-cost, less energy-intensive technologies could offer an advantage. Significant energy and cost savings can potentially be realized by using advanced adsorbent materials. One of the major barriers to the development of such technologies remains the development of materials that are efficient and productive in removing flue gas contaminants. In this review, adsorption-based removal of SOx/NOx impuritie...

Broadband perfect absorber based on one ultrathin layer of refractory metal

Abstract: Broadband perfect absorber based on one ultrathin layer of the refractory metal chromium without structure patterning is proposed and demonstrated. The ideal permittivity of the metal layer for achieving broadband perfect absorption is derived based on the impedance transformation method. Since the permittivity of the refractory metal chromium matches this ideal permittivity well in the visible and near-infrared range, a silica-chromium-silica three-layer absorber is fabricated to demonstrate the broadband perfect absorption. The experimental results under normal incidence show that the absorption is above 90% over the wavelength range of 0.4–1.4 μm, and the measurements under angled incidence within 400–800 nm prove that the absorber is angle-insensitive and polarization-independent.

Reduced-Order Small-Signal Model of Microgrid Systems

Abstract: The objective of this study was to develop a reduced-order small-signal model of a microgrid system capable of operating in both the grid-tied and the islanded conditions. The nonlinear equations of the proposed system were derived in the $dq$ reference frame and then linearized around stable operating points to construct a small-signal model. The high-order state matrix was then reduced using the singular perturbation technique. The dynamic equations were divided into two groups based on the small-signal model parameters $\varepsilon$ . The slow states, which dominated the systems dynamics, were preserved, whereas the fast states were eliminated. Step responses of the model were compared to the experimental results from a hardware test to assess their accuracy and similarity to the full-order system. The proposed reduced-order model was applied to a modified IEEE-37 bus grid-tied microgrid system to evaluate systems dynamic response in grid-tied mode, islanded mode, and transition from grid-tied to islanded mode.

Optimal Sizing of a Vanadium Redox Battery System for Microgrid Systems

Abstract: The vanadium redox battery (VRB) has proven to be a reliable and highly efficient energy storage system (ESS) for microgrid applications. However, one challenge in designing a microgrid system is specifying the size of the ESS. This selection is made more complex due to the independent power and energy ratings inherent in VRB systems. Sizing a VRB for both required power output and energy storage capacity requires an in-depth analysis to produce both optimal scheduling capabilities and minimum capital costs. This paper presents an analytical method to determine the optimal ratings of VRB energy storage based on an optimal scheduling analysis and cost-benefit analysis for microgrid applications. A dynamic programming (DP) algorithm is used to solve the optimal scheduling problem considering the efficiency and operating characteristics of the VRBs. The proposed method has been applied to determine the optimal VRB power and energy ratings for both isolated and grid-connected microgrids, which contain PV arrays and fossil-fuel-based generation. We first consider the case in which a grid-tie is not available and diesel generation is the backup source of power. The method is then extended to consider the case in which a utility grid tie is available.

Ceramics and Ceramic Coatings in Orthopaedics

Abstract: Advanced bioceramics have played integral roles in treatment modalities for damaged or diseased human joints and osseous defects. This paper reviews the uses and properties of ceramics and ceramic coatings variously employed as articulation devices in hip, knee, shoulder, and other joints, either as self-mated surfaces, or against polyethylene (both conventional and highly cross-linked versions), or for osseous- fixation as arthrodesis devices, bone scaffolds, and substitutes in the spine or extremities. The modern uses of oxide and non-oxide materials in these applications will be discussed, followed by an assessment and comparison of their mechanical and physicochemical properties. Recent developments in new bioceramic materials and composites along with advanced processing and testing methods are presented. Advanced bioceramics and coatings are expected to have increasing use in orthopaedics because of their unique combination and range of properties including strength and toughness, hardness and wear resistance, biocompatibility, bacteriostasis, and osseointegration.

American Sign Language alphabet recognition using Microsoft Kinect

Abstract: American Sign Language (ASL) alphabet recognition using marker-less vision sensors is a challenging task due to the complexity of ASL alphabet signs, self-occlusion of the hand, and limited resolution of the sensors. This paper describes a new method for ASL alphabet recognition using a low-cost depth camera, which is Microsoft's Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-seeking method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Forest (RF) classifier is built to recognize ASL signs using the joint angles. To validate the performance of this method, we used a publicly available dataset from Surrey University. The results have shown that our method can achieve above 90% accuracy in recognizing 24 static ASL alphabet signs, which is significantly higher in comparison to the previous benchmarks.

First order reliability method for time-variant problems using series expansions

Abstract: Time-variant reliability is often evaluated by Rice's formula combined with the First Order Reliability Method (FORM). To improve the accuracy and efficiency of the Rice/FORM method, this work develops a new simulation method with the first order approximation and series expansions. The approximation maps the general stochastic process of the response into a Gaussian process, whose samples are then generated by the Expansion Optimal Linear Estimation if the response is stationary or by the Orthogonal Series Expansion if the response is non-stationary. As the computational cost largely comes from estimating the covariance of the response at expansion points, a cheaper surrogate model of the covariance is built and allows for significant reduction in computational cost. In addition to its superior accuracy and efficiency over the Rice/FORM method, the proposed method can also produce the failure rate and probability of failure with respect to time for a given period of time within only one reliability analysis.

Chemoselective Transfer Hydrogenation of Nitroarenes Catalyzed by Highly Dispersed, Supported Nickel Nanoparticles

Abstract: A recyclable highly dispersed Ni/SiO2 catalyst was prepared by atomic layer deposition. Chemoselective reduction of nitroarenes was studied using the prepared Ni/SiO2 as the catalyst and hydrazine hydrate as a hydrogen donor. Different kinds of nitroarenes were converted to the corresponding anilines with high yields. The high activity of the catalysts could be a result of the highly dispersed Ni nanoparticles.

Impact of Sleep and Circadian Disruption on Energy Balance and Diabetes: A Summary of Workshop Discussions.

Abstract: A workshop was held at the National Institute for Diabetes and Digestive and Kidney Diseases with a focus on the impact of sleep and circadian disruption on energy balance and diabetes. The workshop identified a number of key principles for research in this area and a number of specific opportunities. Studies in this area would be facilitated by active collaboration between investigators in sleep/circadian research and investigators in metabolism/diabetes. There is a need to translate the elegant findings from basic research into improving the metabolic health of the American public. There is also a need for investigators studying the impact of sleep/circadian disruption in humans to move beyond measurements of insulin and glucose and conduct more in-depth phenotyping. There is also a need for the assessments of sleep and circadian rhythms as well as assessments for sleep-disordered breathing to be incorporated into all ongoing cohort studies related to diabetes risk. Studies in humans need to complement the elegant short-term laboratory-based human studies of simulated short sleep and shift work etc. with studies in subjects in the general population with these disorders. It is conceivable that chronic adaptations occur, and if so, the mechanisms by which they occur needs to be identified and understood. Particular areas of opportunity that are ready for translation are studies to address whether CPAP treatment of patients with pre-diabetes and obstructive sleep apnea (OSA) prevents or delays the onset of diabetes and whether temporal restricted feeding has the same impact on obesity rates in humans as it does in mice.

Mechanical behavior of zirconium diboride–silicon carbide–boron carbide ceramics up to 2200 °C

Abstract: The mechanical properties of hot pressed zirconium diboride–silicon carbide–boron carbide (ZrB2–SiC–B4C) ceramics were characterized from room temperature up to 2200 °C in an argon atmosphere. The average ZrB2 grain size was 3.0 μm. The SiC particles segregated into clusters, and the largest clusters were >30 μm in diameter. The room temperature flexural strength was 700 MPa, decreasing to 540 MPa at 1800 °C and to 260 MPa at 2200 °C. The strength was controlled by the SiC cluster size up to 1800 °C. At higher temperatures, strength was controlled by formation of liquid phases, and precipitation of large BN and B–O–C–N inclusions. The mechanical behavior of these materials changes at ∼1800 °C, meaning that extrapolation of properties from lower temperatures is not accurate. Mechanical behavior in the ultra-high temperature regime was dominated by impurities and changes in microstructure. Therefore, the use of higher purity materials could lead to significant improvements in ultra-high temperature strength.