The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit

The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.

Numerical heat transfer analysis of carbon-based foams for use in thermal protection system

The applicability of carbon-based foams as an insulating or active cooling material in thermal protection systems (TPSs) of space vehicles is considered using a computer modeling. This study focuses on numerical investigation of the performance of carbon foams for use in TPSs of space vehicles. Two kinds of carbon foams are considered in this study. For active cooling, the carbon foam that has a thermal conductivity of 100 W/m-k is used and for the insulation, the carbon foam having a thermal conductivity of 0.225 W/m-k is used. A 3D geometry is employed to simulate coolant flow and heat transfer through carbon foam model. Gambit has been used to model the 3D geometry and the numerical simulation is carried out in FLUENT. Numerical results from this thesis suggests that the use of CFOAM and HTC carbon foams in TPS's may effectively protect the aluminum structure of the space shuttle during reentry of the space vehicle.

Efficiency and power density improvement of grid-connected hybrid renewable energy systems utilizing high frequency-based power converters

High efficiency of power converters placed between renewable energy sources and the utility grid is required to maximize the utilization of these sources. Power quality is another aspect that requires large passive elements (inductors, capacitors) to be placed between these sources and the grid. The main objective is to develop higher-level high frequency-based power converter system (HFPCS) that optimizes the use of hybrid renewable power injected into the power grid. The HFPCS provides high efficiency, reduced size of passive components, higher levels of power density realization, lower harmonic distortion, higher reliability, and lower cost. The dynamic modeling for each part in this system is developed, simulated and tested. The steady-state performance of the grid-connected hybrid power system with battery storage is analyzed. Various types of simulations were performed and a number of algorithms were developed and tested to verify the effectiveness of the power conversion topologies. A modified hysteresis-control strategy for the rectifier and the battery charging/discharging system was developed and implemented. A voltage oriented control (VOC) scheme was developed to control the energy injected into the grid. The developed HFPCS was compared experimentally with other currently available power converters. The developed HFPCS was employed inside a microgrid system infrastructure, connecting it to the power grid to verify its power transfer capabilities and grid connectivity. Grid connectivity tests verified these power transfer capabilities of the developed converter in addition to its ability of serving the load in a shared manner. In order to investigate the performance of the developed system, an experimental setup for the HF-based hybrid generation system was constructed. We designed a board containing a digital signal processor chip on which the developed control system was embedded. The board was fabricated and experimentally tested. The system's high precision requirements were verified. Each component of the system was built and tested separately, and then the whole system was connected and tested. The simulation and experimental results confirm the effectiveness of the developed converter system for grid-connected hybrid renewable energy systems as well as for hybrid electric vehicles and other industrial applications.

Design principles of semantic binary database management systems

The Semantic Binary Data Model (SBM) is a viable alternative to the now-dominant relational data model. SBM would be especially advantageous for applications dealing with complex interrelated networks of objects provided that a robust efficient implementation can be achieved. This dissertation presents an implementation design method for SBM, algorithms, and their analytical and empirical evaluation. Our method allows building a robust and flexible database engine with a wider applicability range and improved performance. ^ Extensions to SBM are introduced and an implementation of these extensions is proposed that allows the database engine to efficiently support applications with a predefined set of queries. A New Record data structure is proposed. Trade-offs of employing Fact, Record and Bitmap Data structures for storing information in a semantic database are analyzed. ^ A clustering ID distribution algorithm and an efficient algorithm for object ID encoding are proposed. Mapping to an XML data model is analyzed and a new XML-based XSDL language facilitating interoperability of the system is defined. Solutions to issues associated with making the database engine multi-platform are presented. An improvement to the atomic update algorithm suitable for certain scenarios of database recovery is proposed. ^ Specific guidelines are devised for implementing a robust and well-performing database engine based on the extended Semantic Data Model. ^

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Hybrid power system intelligent operation and protection involving plug-in electric vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. ^ The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. ^ The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.^

Genetic algorithms and investment strategies: A global perspective

The profitability of momentum portfolios in the equity markets is derived from the continuation of stock returns over medium time horizons. The empirical evidence of momentum, however, is significantly different across markets around the world. The purpose of this dissertation is to: (1) help global investors determine the optimal selection and holding periods for momentum portfolios, (2) evaluate the profitability of the optimized momentum portfolios in different time periods and market states, (3) assess the investment strategy profits after considering transaction costs, and (4) interpret momentum returns within the framework of prior studies on investors’ behavior. Improving on the traditional practice of selecting arbitrary selection and holding periods, a genetic algorithm (GA) is employed. The GA performs a thorough and structured search to capture the return continuations and reversals patterns of momentum portfolios. Three portfolio formation methods are used: price momentum, earnings momentum, and earnings and price momentum and a non-linear optimization procedure (GA). The focus is on common equity of the U.S. and a select number of countries, including Australia, France, Germany, Japan, the Netherlands, Sweden, Switzerland and the United Kingdom. The findings suggest that the evolutionary algorithm increases the annualized profits of the U.S. momentum portfolios. However, the difference in mean returns is statistically significant only in certain cases. In addition, after considering transaction costs, both price and earnings and price momentum portfolios do not appear to generate abnormal returns. Positive risk-adjusted returns net of trading costs are documented solely during “up” markets for a portfolio long in prior winners only. The results on the international momentum effects indicate that the GA improves the momentum returns by 2 to 5% on an annual basis. In addition, the relation between momentum returns and exchange rate appreciation/depreciation is examined. The currency appreciation does not appear to influence significantly momentum profits. Further, the influence of the market state on momentum returns is not uniform across the countries considered. The implications of the above findings are discussed with a focus on the practical aspects of momentum investing, both in the U.S. and globally.

Enhancement of wireless networks with heterogeneous antennas

In recent years, wireless communication infrastructures have been widely deployed for both personal and business applications. IEEE 802.11 series Wireless Local Area Network (WLAN) standards attract lots of attention due to their low cost and high data rate. Wireless ad hoc networks which use IEEE 802.11 standards are one of hot spots of recent network research. Designing appropriate Media Access Control (MAC) layer protocols is one of the key issues for wireless ad hoc networks. ^ Existing wireless applications typically use omni-directional antennas. When using an omni-directional antenna, the gain of the antenna in all directions is the same. Due to the nature of the Distributed Coordination Function (DCF) mechanism of IEEE 802.11 standards, only one of the one-hop neighbors can send data at one time. Nodes other than the sender and the receiver must be either in idle or listening state, otherwise collisions could occur. The downside of the omni-directionality of antennas is that the spatial reuse ratio is low and the capacity of the network is considerably limited. ^ It is therefore obvious that the directional antenna has been introduced to improve spatial reutilization. As we know, a directional antenna has the following benefits. It can improve transport capacity by decreasing interference of a directional main lobe. It can increase coverage range due to a higher SINR (Signal Interference to Noise Ratio), i.e., with the same power consumption, better connectivity can be achieved. And the usage of power can be reduced, i.e., for the same coverage, a transmitter can reduce its power consumption. ^ To utilizing the advantages of directional antennas, we propose a relay-enabled MAC protocol. Two relay nodes are chosen to forward data when the channel condition of direct link from the sender to the receiver is poor. The two relay nodes can transfer data at the same time and a pipelined data transmission can be achieved by using directional antennas. The throughput can be improved significant when introducing the relay-enabled MAC protocol. ^ Besides the strong points, directional antennas also have some explicit drawbacks, such as the hidden terminal and deafness problems and the requirements of retaining location information for each node. Therefore, an omni-directional antenna should be used in some situations. The combination use of omni-directional and directional antennas leads to the problem of configuring heterogeneous antennas, i e., given a network topology and a traffic pattern, we need to find a tradeoff between using omni-directional and using directional antennas to obtain a better network performance over this configuration. ^ Directly and mathematically establishing the relationship between the network performance and the antenna configurations is extremely difficult, if not intractable. Therefore, in this research, we proposed several clustering-based methods to obtain approximate solutions for heterogeneous antennas configuration problem, which can improve network performance significantly. ^ Our proposed methods consist of two steps. The first step (i.e., clustering links) is to cluster the links into different groups based on the matrix-based system model. After being clustered, the links in the same group have similar neighborhood nodes and will use the same type of antenna. The second step (i.e., labeling links) is to decide the type of antenna for each group. For heterogeneous antennas, some groups of links will use directional antenna and others will adopt omni-directional antenna. Experiments are conducted to compare the proposed methods with existing methods. Experimental results demonstrate that our clustering-based methods can improve the network performance significantly. ^

Genetic Algorithms and Investment Strategies: A Global Perspective

The profitability of momentum portfolios in the equity markets is derived from the continuation of stock returns over medium time horizons. The empirical evidence of momentum, however, is significantly different across markets around the world. The purpose of this dissertation is to: 1) help global investors determine the optimal selection and holding periods for momentum portfolios, 2) evaluate the profitability of the optimized momentum portfolios in different time periods and market states, 3) assess the investment strategy profits after considering transaction costs, and 4) interpret momentum returns within the framework of prior studies on investors’ behavior. Improving on the traditional practice of selecting arbitrary selection and holding periods, a genetic algorithm (GA) is employed. The GA performs a thorough and structured search to capture the return continuations and reversals patterns of momentum portfolios. Three portfolio formation methods are used: price momentum, earnings momentum, and earnings and price momentum and a non-linear optimization procedure (GA). The focus is on common equity of the U.S. and a select number of countries, including Australia, France, Germany, Japan, the Netherlands, Sweden, Switzerland and the United Kingdom. The findings suggest that the evolutionary algorithm increases the annualized profits of the U.S. momentum portfolios. However, the difference in mean returns is statistically significant only in certain cases. In addition, after considering transaction costs, both price and earnings and price momentum portfolios do not appear to generate abnormal returns. Positive risk-adjusted returns net of trading costs are documented solely during “up” markets for a portfolio long in prior winners only. The results on the international momentum effects indicate that the GA improves the momentum returns by 2 to 5% on an annual basis. In addition, the relation between momentum returns and exchange rate appreciation/depreciation is examined. The currency appreciation does not appear to influence significantly momentum profits. Further, the influence of the market state on momentum returns is not uniform across the countries considered. The implications of the above findings are discussed with a focus on the practical aspects of momentum investing, both in the U.S. and globally.

Design and implementation of a web based shop floor control system to maintain schedule feasibility in a dynamic job shop environment

The effective control of production activities in dynamic job shop with predetermined resource allocation for all the jobs entering the system is a unique manufacturing environment, which exists in the manufacturing industry. In this thesis a framework for an Internet based real time shop floor control system for such a dynamic job shop environment is introduced. The system aims to maintain the schedule feasibility of all the jobs entering the manufacturing system under any circumstance. The system is capable of deciding how often the manufacturing activities should be monitored to check for control decisions that need to be taken on the shop floor. The system will provide the decision maker real time notification to enable him to generate feasible alternate solutions in case a disturbance occurs on the shop floor. The control system is also capable of providing the customer with real time access to the status of the jobs on the shop floor. The communication between the controller, the user and the customer is through web based user friendly GUI. The proposed control system architecture and the interface for the communication system have been designed, developed and implemented.