Showing papers by "Missouri University of Science and Technology published in 2012"

Dynamic Security Risk Management Using Bayesian Attack Graphs

Abstract: Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model. In this paper, we propose a risk management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels. We show how to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multiobjective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.

Generalized Average Modeling of Dual Active Bridge DC–DC Converter

Abstract: Full-order continuous-time average modeling and dynamic analysis of bidirectional dc-dc dual active bridge (DAB) converters are studied. The transformer current in DAB converter is purely ac, making continuous-time modeling difficult. The proposed full-order continuous-time average model uses the dc terms and first order terms of transformer current and capacitor voltage as state variables, resulting in a third-order model, if capacitor equivalent series resistance (ESR) is not considered, and a sixth-order model if ESR is considered. A control-to-output-voltage transfer function is derived for DAB converters. Experimental results confirm that the proposed model correctly predicts the small-signal frequency response and an even more accurate prediction can be obtained if capacitor ESR is taken into account.

Electrodeposition of Crystalline Co3O4—A Catalyst for the Oxygen Evolution Reaction

Abstract: Crystalline films of Co3O4 are deposited by electrochemically oxidizing a tartrate complex of Co2+ in an aqueous, alkaline solution at elevated temperatures. The crystallinity and stability of the films are a strong function of the deposition temperature. Films deposited at temperatures from 50 to 90 °C are amorphous, but films deposited from refluxing solution at 103 °C are crystalline. The crystalline films adhere strongly to the substrate, whereas the amorphous films peel off of the substrate when dried due to drying stresses. The crystalline films deposit with the normal spinel structure, with a lattice parameter of 0.8097 nm and crystallite size of 26 nm. The catalytic activity of Co3O4 for the oxygen evolution reaction (OER) of the crystalline and amorphous films is compared by Tafel analysis in alkaline solution at pH 14. The crystalline Co3O4 film has a Tafel slope of 49 mV/decade and an exchange current density of 2.0 × 10–10 A cm–2, whereas an amorphous film deposited at 50 °C has a Tafel slope ...

Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles

Abstract: The power system and transportation sector are our planet's main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicle-to-grid capability, referred to as gridable vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability of GVs. Forecasted load is used in unit commitment (UC); however, the actual load usually differs from the forecasted one. Thus, UC with plug-in vehicles under uncertainty in a smart grid is very complex considering smart charging and discharging to and from various energy sources and loads to reduce both cost and emissions. A set of valid scenarios is considered for the uncertainties of wind and solar energy sources, load and GVs. In this paper, an optimization algorithm is used to minimize the expected cost and emissions of the UC schedule for the set of scenarios. Results are presented indicating that the smart grid has the potential to maximally utilize RESs and GVs to reduce cost and emissions from the power system and transportation sector.

Biomedical production of implants by additive electro-chemical and physical processes

Abstract: Biomanufacturing integrates life science and engineering fundamentals to produce biocompatible products enhancing the quality of life. The state-of-the-art of this rapidly evolving manufacturing sector is presented and discussed, in particular the additive electrical, chemical and physical processes currently being applied to produce synthetic and biological parts. This fabrication strategy is strongly material-dependent, so the main classes of biomaterials are detailed. It is explained the potential to process composite materials combining synthetic and biological materials, such as cells, proteins and growth factors, as well the interdependences between materials and processes. The techniques commonly used to increase the bioactivity of clinical implants and improve the interface characteristics between biological tissues and implants are also presented.

Active Capacitor Voltage Balancing in Single-Phase Flying-Capacitor Multilevel Power Converters

Abstract: Two active capacitor voltage balancing schemes are proposed for single-phase (H-bridge) flying-capacitor multilevel converters. They are based on the circuit equations of flying-capacitor converters. Consequently, they can be implemented using straightforward control rules. In particular, the first technique is based on an algorithm which follows the standard multilevel modulation. Then, it utilizes a redundant state selection table for capacitor voltage balancing. In the second method, multiple duty cycles are defined and modulated in direct response to the capacitor voltages. The most important advantage of these two proposed methods is that they can be utilized to converters with any desired number of levels in their output voltage. Moreover, the analysis and implementation of both methods are straightforward. Through simulation and experimental implementation, these methods are shown to be effective on capacitor voltage regulation in flying-capacitor multilevel converters.

Online Optimal Control of Affine Nonlinear Discrete-Time Systems With Unknown Internal Dynamics by Using Time-Based Policy Update

Abstract: In this paper, the Hamilton-Jacobi-Bellman equation is solved forward-in-time for the optimal control of a class of general affine nonlinear discrete-time systems without using value and policy iterations. The proposed approach, referred to as adaptive dynamic programming, uses two neural networks (NNs), to solve the infinite horizon optimal regulation control of affine nonlinear discrete-time systems in the presence of unknown internal dynamics and a known control coefficient matrix. One NN approximates the cost function and is referred to as the critic NN, while the second NN generates the control input and is referred to as the action NN. The cost function and policy are updated once at the sampling instant and thus the proposed approach can be referred to as time-based ADP. Novel update laws for tuning the unknown weights of the NNs online are derived. Lyapunov techniques are used to show that all signals are uniformly ultimately bounded and that the approximated control signal approaches the optimal control input with small bounded error over time. In the absence of disturbances, an optimal control is demonstrated. Simulation results are included to show the effectiveness of the approach. The end result is the systematic design of an optimal controller with guaranteed convergence that is suitable for hardware implementation.

A general model for effects of temperature on ectotherm ontogenetic growth and development

Abstract: The temperature size rule (TSR) is the tendency for ectotherms to develop faster but mature at smaller body sizes at higher temperatures. It can be explained by a simple model in which the rate of growth or biomass accumulation and the rate of development have different temperature dependence. The model accounts for both TSR and the less frequently observed reverse-TSR, predicts the fraction of energy allocated to maintenance and synthesis over the course of development, and also predicts that less total energy is expended when developing at warmer temperatures for TSR and vice versa for reverse-TSR. It has important implications for effects of climate change on ectothermic animals.

Oxidation of ultra-high temperature transition metal diboride ceramics

Abstract: The oxidation behaviour of transition metal diboride ceramics is reviewed with emphasis on the performance of zirconium diboride and hafnium diboride. First, the oxidation behaviour of nominally pure diborides is discussed, focusing on the transition to linear mass gain kinetics at temperatures above ∼1100°C. Next, the use of SiC and other additives that produce silica based scales when oxidised is reviewed. These additives improve oxidation protection due to the formation/stability of the outer layer of borosilicate glass that acts as a barrier to diffusion of oxygen to the substrate. However, elevated temperatures (>1650°C) and/or the combination of aerodynamic flow, high heat flux and reactive atmosphere associated with hypersonic flight destabilises the outer oxide and decreases oxidation protection. Other additives that affect the composition and structure of the crystalline oxide scale without forming an outer glassy layer are a promising approach to improving oxidation behaviour of diboride...

Location-Aware Collaborative Filtering for QoS-Based Service Recommendation

Abstract: Collaborative filtering is one of widely used Web service recommendation techniques. In QoS-based Web service recommendation, predicting missing QoS values of services is often required. There have been several methods of Web service recommendation based on collaborative filtering, but seldom have they considered locations of both users and services in predicting QoS values of Web services. Actually, locations of users or services do have remarkable impacts on values of QoS factors, such as response time, throughput, and reliability. In this paper, we propose a method of location-aware collaborative filtering to recommend Web services to users by incorporating locations of both users and services. Different from existing user-based collaborative filtering for finding similar users for a target user, instead of searching entire set of users, we concentrate on users physically near to the target user. Similarly, we also modify existing service similarity measurement of collaborative filtering by employing service location information. After finding similar users and services, we use the similarity measurement to predict missing QoS values based on a hybrid collaborative filtering technique. Web service candidates with the top QoS values are recommended to users. To validate our method, we conduct series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that the location-aware method improves performance of recommendation significantly.

Ensuring Distributed Accountability for Data Sharing in the Cloud

Abstract: Cloud computing enables highly scalable services to be easily consumed over the Internet on an as-needed basis. A major feature of the cloud services is that users' data are usually processed remotely in unknown machines that users do not own or operate. While enjoying the convenience brought by this new emerging technology, users' fears of losing control of their own data (particularly, financial and health data) can become a significant barrier to the wide adoption of cloud services. To address this problem, in this paper, we propose a novel highly decentralized information accountability framework to keep track of the actual usage of the users' data in the cloud. In particular, we propose an object-centered approach that enables enclosing our logging mechanism together with users' data and policies. We leverage the JAR programmable capabilities to both create a dynamic and traveling object, and to ensure that any access to users' data will trigger authentication and automated logging local to the JARs. To strengthen user's control, we also provide distributed auditing mechanisms. We provide extensive experimental studies that demonstrate the efficiency and effectiveness of the proposed approaches.

A Brokerage-Based Approach for Cloud Service Selection

Abstract: great opportunities for consumers to find the best service and best pricing, which however raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. This is also a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose a novel brokerage-based architecture in the Cloud, where the Cloud brokers is responsible for the service selection. In particular, we design a unique indexing technique for managing the information of a large number of Cloud service providers. We then develop efficient service selection algorithms that rank potential service providers and aggregate them if necessary. We prove the efficiency and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.

Reinforcement Learning Controller Design for Affine Nonlinear Discrete-Time Systems using Online Approximators

Abstract: In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.

Removal of Iron From Aluminum: A Review

Abstract: In this paper, the Fe-rich phases in and their detrimental effect on aluminum alloys are summarized. The existence of brittle platelet β-Fe-rich phases lowers the mechanical properties of aluminum ...

Closed-loop control of DC-DC dual active bridge converters driving single-phase inverters

Abstract: A solid-state transformer (SST) is a high-frequency power electronic converter that is used as a distribution power transformer. A common three-stage configuration of an SST consists of ac-dc rectifier, isolated dc-dc dual-active-bridge (DAB) converter, and dc-ac inverter. This study addresses the controller design issue for a dc-dc DAB converter when driving a regulated single-phase dc-ac inverter. Since the switching frequency of the inverter stage is much higher than that of the DAB stage, the single-phase inverter is modeled as a double-line-frequency (e.g., 120 Hz) current sink. The effect of 120-Hz current by the single-phase inverter is studied. The limitation of a PI-controller, low gain at 120 Hz, is investigated. Two methods are proposed to improve the regulation of the output voltage of DAB converters. The first one uses a bandstop filter and feedforward, while the second method uses an additional proportional-resonant controller in the feedback loop. Theoretical analysis, simulation, and experiment results are provided.

Energetic and biomechanical constraints on animal migration distance

Abstract: Animal migration is one of the great wonders of nature, but the factors that determine how far migrants travel remain poorly understood. We present a new quantitative model of animal migration and use it to describe the maximum migration distance of walking, swimming and flying migrants. The model combines biomechanics and metabolic scaling to show how maximum migration distance is constrained by body size for each mode of travel. The model also indicates that the number of body lengths travelled by walking and swimming migrants should be approximately invariant of body size. Data from over 200 species of migratory birds, mammals, fish, and invertebrates support the central conclusion of the model - that body size drives variation in maximum migration distance among species through its effects on metabolism and the cost of locomotion. The model provides a new tool to enhance general understanding of the ecology and evolution of migration.

Linear Precoding for Finite-Alphabet Signaling Over MIMOME Wiretap Channels

Abstract: In this paper, we investigate the secrecy rate of finite-alphabet communications over multiple-input-multiple-output-multiple-antenna eavesdropper (MIMOME) channels. Traditional precoding designs based on Gaussian input assumption may lead to substantial secrecy rate loss when the Gaussian input is replaced by practical finite-alphabet input. To address this issue, we investigate linear precoding designs to directly maximize the secrecy rate for MIMOME systems under the constraint of finite-alphabet input. By exploiting the theory of Karush-Kuhn-Tucker (KKT) analysis and matrix calculus, we first present necessary conditions of the optimal precoding design when instantaneous channel-state information (CSI) of the eavesdropper is known at the transmitter. In this light, an iterative algorithm for finding the optimal precoding matrix is developed, utilizing a gradient decent method with backtracking line search. Moreover, we find that the beamforming design in MIMONE systems, which is a secrecy-capacity-achieving approach for Gaussian signaling, no longer provides the maximum secrecy rate for finite-alphabet input data. This case is substantially different from the Gaussian input case. In addition, we derive the closed-form results on the precoding matrix, which maximizes the secrecy rate in the low signal-to-noise ratio (SNR) region, and reveal the optimal precoding structure in the high-SNR region. A novel jamming signal generation method that draws on the CSI of the eavesdropper to additionally increase the secrecy rate is further proposed. The precoding design with only statistical CSI of the eavesdropper available at the transmitter is also considered. Numerical results show that the proposed designs provide significant gains over recent precoding designs through a power control policy and the precoding design with the Gaussian input assumption in various scenarios.

Formation and Modification of MgO·Al2O3-Based Inclusions in Alloy Steels

Abstract: The current study performed thermadynamic calculation, laboratory experiments, and industrial trials for the formation and modification of MgO-Al2O3 spinel inclusions in alloy steels. The stability Mg-Al-O diagram was obtained using the thermodymanic study. The resulting MgO-Al2O3-CaO inclusions from MgO-Al2O3 spinel inclusions after the calcium treatment were spherical, and > 5 μm MgO-Al2O3-CaO inclusions have a two-layer structure: an outside CaO-Al2O3 layer and a MgO-Al2O3 core. The modification of > 5 μm MgO·Al2O3 spinel inclusions by calcium treatment includes two steps: (1) reducing MgO in the inclusion into the dissolved magnesium by the dissolved calcium in the steel and (2) generating a liquid xCaO·yAl2O3 layer at the outside of the spinel inclusion. For <2 μm MgO·Al2O3 spinel inclusions, they can possibly be modified into a xCaO·yAl2O3 inclusion by reducing all MgO component in the spinel inclusions with the added calcium.