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A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

This report outlines in three sections the progress that has been made in the last two years. Work on the minimal spanning tree problem is first discussed, since this area has seen the most striking progress. The second section discuss work on the convex hulls of random walks. This work is the most recent, and it illustrates the broader applicability of ideas that were developed in the earlier stages of this grant. The third section discusses the thesis work of doctoral candidates Maki Monna and Jun Gao who have been supported in part through this contract.

This report outlines in three sections the progress that has been made in the last two years. Work on the minimal spanning tree problem is first discussed, since this area has seen the most striking progress. The second section discuss work on the convex hulls of random walks. This work is the most recent, and it illustrates the broader applicability of ideas that were developed in the earlier stages of this grant. The third section discusses the thesis work of doctoral candidates Maki Monna and Jun Gao who have been supported in part through this contract.

In fiscal year 2020, the U.S. Army spent nearly $77 billion on contracts. Auditors employ various techniques, including anomaly detection, to select contracts that merit scrutiny. But in a resource-constrained environment, auditors can review only a limited number of contracts. Using data obtained from USAspending.gov, we consider how anomaly detection combined with dimensionality reduction can be used to recommend contracts for investigation. We analyze over 20,000 fixed-price Army contracts between fiscal years 2017 to 2020, using more than one hundred combinations of dimensionality reduction and anomaly detection techniques, and formations of artificial anomalies. A consistent finding is that dimensionality reduction using principal components or autoencoders is not demonstrably beneficial. This finding may be due to the discrete nature of the USAspending.gov data and may not apply to other data sets. The best performance is obtained using isolation forests for anomaly detection without dimensionality reduction.

The work reviewed here is centered around the tools and results of probability theory that contribute to the theory of algorithms, especially the theory of combinatorial optimization. The reviewed cites sixteen articles and three Ph.d. theses that were supported in part by ARO Grant DAAL03-91-G-0110 during the period of May 1991 to October 1993, including the optional third year (so the present report necessarily overlaps with the report filed in October 1992 that covered the first two years of the grant). The work surveyed includes work on the objective method which led to the the resolution with David Aldous of R. Bland's conjecture that the sum of the squares of the edges of the minimal spanning tree of a random sample from the unit square converges to a constant as the sample size goes to infinity, work with R. Stine on symbolic stochastic calculus, the NAS Panel Report Probability and Algorithms, work with T. L. Snyder on the worst case behavior of the traveling salesman problem, and work with Jun Gao on the spacefilling curve heuristics, as well as other work on probability and its relation to the theory of algorithms. (AN)

The use of the classical (SMC) applied to control of stator’s powers of DFIG, gives the problem of chattering, therefore to avoid this phenomenon a robust algorithm (STSMC) is applied. This paper presents a comparison of conventional SMC with the proposed strategy of STSMC algorithm. The results are obtained using MATLAB and demonstrate stability and robustness of this algorithm

Global warming, as many other threats, requires more activity from the government, and people inquire about this, rightfully so. However, the public and private sectors nowadays have a joint responsibility with society. Global warming has come out of the scientific community and shows that climate change is an important social, economic, and political issue. Currently the carbon dioxide (CO2) emissions by transportation sector are the main polluter of the planet. For this reason, in this paper we propose a reflection on transport, which is an important sector of the economy, responsible for the generation of many jobs and wealth but, at the same time, it has negative impacts on people, companies, and the environment. Recent investigations demonstrate that mobility problems affects the life quality of the people, business productivity and gross domestic product (GDP) performance. In the face of the changes that we are experiencing, it is important to find alternatives that assistance governments in the decision-making to solve the problem of traffic in large and medium-size cities and bring well-being to the population. The goal is to provide accurate tool to help take the best decisions in situations that most of the time has a lack of economic resources and transparency. Another motivation is to increase the productivity of companies and achieve sustainable development by reducing mobility problems and CO2 emissions. As outcomes of the paper, an in-depth study is presented on the subject that indicates a gap in the literature with fuzzy logic applied to sustainability. We indicate a sustainable option for the transport sector using algorithms for decision-making, and broaden the discussion on climate change and solutions to reduce the impacts caused by humanity. When the models were applied through a conceptual study, it was possible to provide an instrument generated on the use of algorithms that can aid decision-making by getting qualitative data from a discussion with stakeholders on a topic. Finally, the document identifies the importance of the participation of stakeholder, reinforces legitimacy and transparency in decision-making processes and above all present a tool that can help governments to solve this problem. In short, this work proposes an original and valuable tool to be applied in the decision-making process for the sustainable transport sector based on the stakeholder engagement.

This grant has the central aim of exploring when, and how, probability is useful in the theory of algorithms. Most of the problems reviewed have their origins in the area of Euclidean Combinatorial Optimization, which might be operationally defined as the theory that has evolved out of the study Euclidean traveling salesman problem (TSP), the minimal spanning tree problem, and the minimal matching problem. Probability enters the study of such problems by two different paths. One path calls on exogenous randomization in the course of a genuine probabilistic algorithm. This path is of increasing importance in many areas, and on an elementary level is well illustrated by the method of simulated annealing. A second path of considerable importance calls on the introduction of stochastic models for the problem inputs. One then uses probability theory to understand as deeply as possible the behavior of the associated objective functions. This understanding is used subsequently to guide algorithm design.

Four, linear, exponential, integration algorithms (two implicit, one explicit, and one predictor/corrector) are applied to a viscoplastic model to assess their capabilities. Viscoplasticity comprises a system of coupled, nonlinear, stiff, first order, ordinary differential equations which are a challenge to integrate by any means. Two of the algorithms (the predictor/corrector and one of the implicits) give outstanding results, even for very large time steps.

This final report summarizes the contribution of the fourteen articles and two doctoral dissertations that were supported by this grant. The central aim of the work has been to understand the asymptotic behavior of the objective function of problems of classical combinatorial optimization, both in the stochastically modeled cases and in the deterministic worst-case. One of the engaging developments of this investigation has been that there are close parallels in these two problems, despite substantial differences in technique. In addition to reviewing the main contributions, there is a brief discussion of two articles that responded to targets of opportunity. One of these articles received the Wilcoxon Prize for Best Application Paper in 'Technometrics.'

Modern society depends on critical infrastructure (CI) managed by Programmable Logic Controllers (PLCs). PLCs depend on firmware, though firmware security vulnerabilities and contents remain largely unexplored. Attackers are acquiring the knowledge required to construct and install malicious firmware on CI. To the defender, firmware reverse engineering is a critical, but tedious, process. This thesis applies machine learning algorithms, from the le carving and malware identification fields, to firmware reverse engineering. It characterizes the algorithms' performance. This research describes and characterizes a process to speed and simplify PLC firmware analysis. The system partitions binary firmwares into segments, labels each segment with a le type, determines the target architecture of code segments, then disassembles and performs rudimentary analysis on the code segments. The research discusses the system's accuracy on a set of pseudo- firmwares. Of the algorithms this research considers, a combination of a byte-value frequency file carving algorithm and a support vector machine (SVM) algorithm using information gain (IG) for feature selection achieve the best performance. That combination correctly identifies the file types of 57.4% of non-code bytes, and the architectures of 85.3% of code bytes. This research applies the Firmware Disassembly System to a real-world firmware and discusses the contents.

Three solution algorithms, explicit under-relaxation, point implicit, and lower-upper symmetric Gauss-Seidel, are used to compute nonequilibrium flow around the Apollo 4 return capsule at the 62-km altitude point in its descent trajectory. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness.The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15 and 30, the lower-upper symmetric Gauss-Seidel method produces an eight order of magnitude drop in the energy residual in one-third to one-half the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 30 and above. At Mach 40 the performance of the lower-upper symmetric Gauss-Seidel algorithm deteriorates to the point that it is out performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.

An increasing variety of sensors are becoming available for use onboard autonomous vehicles. Given these enhanced sensing capabilities, scientific and military personnel are interested in exploiting autonomous vehicles for increasingly complex missions. Most of these missions require the vehicle to function in complex, cluttered environments and react to changing environmental parameters. The present state-of-art vehicles are not maneuverable enough to successfully accomplish most of these tasks. In this research, a nonlinear controller was derived, designed, implemented in simulation and onboard a AUV, and in-water tested in order to enhance vehicle maneuverability. The structure of a controller is hierarchical such that an inner loop non-linear controller (outputs the appropriate thrust values) is the same for all mission scenarios while a library of outer-loop non-linear controllers are available to implement specific maneuvering scenarios. On top of the outer-loop is the mission planner which selects which outer-loop controller will be used. The algorithms are generic and in no way vehicle specific and can therefore be implemented on various AUVs.

Current algorithms used to put a lattice gauge configuration into Landau gauge either suffer from the problem of critical slowing-down or involve an additional computational expense to overcome it. Evolutionary Algorithms (EAs), which have been widely applied to other global optimisation problems, may be of use in gauge fixing. Also, being global, they should not suffer from critical slowing-down as do local gradient based algorithms. We apply EA's and also a Steepest Descent (SD) based method to the problem of Landau Gauge Fixing and compare their performance.

In this report, the performance characteristics of the LMS Gradient Algorithm, two Adaptive Fixed-Point Iteration Algorithms and of a non-iterative method based on Levinson's Algorithm are considered for the case where an adaptive algorithm is used to determine the unit sample response for a system which attempts to discriminate between the signal and noise processes on the basis of bandwidth. Such a system is often referred to as a Spectral Line Enhancer. Theoretical bounds on the mean-square error as a function of the time index n are derived for each of the three iterative methods. A comparison of these bounds is made for the case where the input data to the Spectral Line Enhancer is composed of a single sinusoid of random phase in the presence of an additive autoregressive noise sequence. The results of extensive computer simulations of the adaptive algorithms considered are used to determine the usefulness of the theoretical bounds and to make comparisons of the performance of the four methods.

Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their recent success on various classification and ranking tasks. In this paper, we propose S-MART, a tree-based structured learning framework based on multiple additive regression trees. S-MART is especially suitable for handling tasks with dense features, and can be used to learn many different structures under various loss functions. We apply S-MART to the task of tweet entity linking --- a core component of tweet information extraction, which aims to identify and link name mentions to entities in a knowledge base. A novel inference algorithm is proposed to handle the special structure of the task. The experimental results show that S-MART significantly outperforms state-of-the-art tweet entity linking systems.

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Numerical techniques that improve the accuracy and stability of algorithms for the small disturbance and full potential equations used to calculate transonic flows are described. For the small disturbance equation, the algorithm improvements are: (1) the use of monotone switches in the type dependent finite differencing, and (2) the use of stable and simple second order accurate spatial differencing; these improvements are for steady and unsteady transonic flows. For the steady full potential equation, the improvement is in the use of a monotone switch in the type dependent finite differencing of an approximate factorization (AF2) algorithm. All these improvements are implemented in present computer codes by making minor coding modifications.

This thesis applies genetic algorithms to a mission routing problem. The mission routing problem involves determining an aircraft's best route between a staging base and a target. The goal is to minimize the route distance and the exposure to radar. Potential routes are mapped to a 3-dimensional mesh where the nodes correspond to checkpoints in the route and the arcs correspond to partial paths of the route. Each arc is weighted with respect to distance and exposure to radar. A genetic algorithm is a probabilistic search technique loosely based on theories of biological evolution. Genetic crossover and survival of the fittest are the basis of a genetic algorithm's control structure and can be used for general problems. Encoding and evaluation of the data structure is problem specific. This thesis focuses on approaches to mapping the mission routing problem's mesh to a data structure which the genetic algorithm's control structure can effectively and efficiently manipulate. Three broad methods are proposed: Bounded Progress, Free Progress, and Restricted Progress. The Bounded Progress method uses a tightly-coupled mesh while the Free Progress method uses a loosely-coupled mesh. The Restricted Progress method is a hybrid of the other two methods.

In this study we examine results from two atmospheric correction approaches applied to HICO data. The first atmospheric correction approach is the standard multispectral Gordon/Wang NIR atmospheric correction applied to HICO MODIS-like channels using appropriate MODIS relative spectral response tables. The second approach is the Cloud and Shadow (C&S) atmospheric correction which is a scene-dependent method that requires accurate detection of a cloud-shadow pair and a neighboring sunny region which has the same water properties as the shadow region. Although clouds are relatively easy to detect, detecting their shadows over deep water is quite challenging. In this study we also introduce an automated cloud shadow detection approach; for convenience we call it the Cloud Shadow Algorithm (CSA) and it seems to work reasonably well over homogeneous water bodies. We present some preliminary results comparing the two atmospheric correction approaches and also some preliminary results of the shadow detection approach.

We apply the recently proposed superiorization methodology (SM) to the inverse planning problem in radiation therapy. The inverse planning problem is represented here as a constrained minimization problem of the total variation (TV) of the intensity vector over a large system of linear two-sided inequalities. The SM can be viewed conceptually as lying between feasibility-seeking for the constraints and full-fledged constrained minimization of the objective function subject to these constraints. It is based on the discovery that many feasibility-seeking algorithms (of the projection methods variety) are perturbation-resilient, and can be proactively steered toward a feasible solution of the constraints with a reduced, thus superiorized, but not necessarily minimal, objective function value.

We reviewed all research articles in 10 recent volumes of the "Journal of Applied Behavior Analysis (JABA)": Vol. 28(3), 1995, through Vol. 38(2), 2005. Continuous recording was used in the majority (55%) of the 168 articles reporting data on free-operant human behaviors. Three methods for reporting interobserver agreement (exact agreement, block-by-block agreement, and time-window analysis) were employed in more than 10 of the articles that reported continuous recording. Having identified these currently popular agreement computation algorithms, we explain them to assist researchers, software writers, and other consumers of "JABA" articles. (Contains 2 figures.)

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

The results from a verification with pilot reports of four aircraft icing algorithms applied to Navy global numerical model data are presented. Significant differences in forecast performance among algorithms are closely related to differences in temperature and moisture thresholds utilized to infer icing. Near 850 mb (-5000 ft), three of the icing routines correctly forecast over 70% of observed icing occurrences, and obtained Hanssen and Kuipers skill scores (difference between hits and false alarms) in excess of 0.5 (0, random performance; I, perfect skill). Statistical tests indicated that differences in skill scores as a function of forecast lead time (0 to 24 hr) were generally not significant. The use of higher vertical resolution data was found very important for enhanced icing prediction performance. Overall results indicate that the ability of icing routines to differentiate icing type and intensity based on temperature, moisture and stability criteria is clearly limited. In terms of forecast skill and computational efficiency, algorithm comparisons indicate that the icing routine currently used as operational guidance at the National Centers for Environmental Prediction Aviation Weather Center would be a good selection for the Naval Research Laboratory's aviation support product suite.

We describe the application of differential reduction algorithms for Feynman Diagram calculation. We illustrate the procedure in the context of generalized hypergeometric functions, and give an example for a type of q-loop bubble diagram.

We investigate the estimation of specific intrinsic volumes of stationary Boolean models by local digital algorithms; that is, by weighted sums of $n \times\ldots \times n$ configuration counts. We show that asymptotically unbiased estimators for the specific surface area or integrated mean curvature do not exist if the dimension is at least two or three, respectively. For 3-dimensional stationary, isotropic Boolean models, we derive asymptotically unbiased estimators for the specific surface area and integrated mean curvature. For a Boolean model with balls as grains we even obtain an asymptotically unbiased estimator for the specific Euler characteristic.

In this article we compare the cross-correlation and breakfinder techniques applied to the measurements of redshifts from low-resolution spectra. We assume spectra obtained from multinarrowband imagery, a technique for multi-object spectrophotometry. Comparing the cross-correlation with the breakfinder, we find that neither is intrinsically superior to the other. They have comparable precision for early type galaxies but the cross-correlation is clearly superior for later type galaxies. On the other hand, unlike the breakfinder, the cross-correlation is sensitive to instrumental errors as well as the shape of the templates used. We carried out simulations with two types of global spectral errors: a slope error and sinusoidal errors. They can introduce serious systematic errors in redshifts measured with the cross-correlation. The breakfinder is a spectrally local test and does not suffer from this type of error. We therefore conclude that the two techniques are complementary and that they should both be used to help flag objects for which they give abnormally discrepant redshifts.

Hyperspectral imaging is an important tool in various fields, notably geosensing and astronomy, and with the development of new devices, it is now also being applied also in medicine. Concepts and tools from signal processing and data analysis need to be employed to analyze these large and complex data sets. In this paper, we present several techniques which generally apply to hyperspectral data, and we use them to analyze a particular data set. With light sources of increasingly broader ranges, spectral analysis of tissue sections has evolved from 2 wavelength image subtraction techniques to Raman near infra-red microspectroscopic analysis permitting discrimination of cell types and tissue patterns. We have developed and used a unique tuned light source based on micro-optoelectromechanical systems (MOEMS) and applied algorithms for spectral microscopic analysis of normal and malignant colon tissue. We compare the results to our previous studies which used a tunable liquid filter light source.

Nowadays, we are faced with the complex of process in water resource management. The complexity of water resources system makes the analysis of water resources investment alternatives difficult and complex. This leads to increase the application of systems analysis techniques. Optimization makes it possible to do exact mathematical modeling in a process, so by applying mathematical programming methods we will be able to optimize our models. Recently approximation algorithms are developed to combine the basic principles of heuristic algorithms to reach an efficient method in feasible region. These methods are known as meta-heuristic methods. Evolutionary algorithms (EAs), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Tabu Search (TS) and Artificial Neural Networks (ANN) are examples of these methods. The mathematical programming has two main problems. Sometimes they consider local optima points as global optimum points, and each of the mentioned methods is used for certain issues. In these studies the main focus is on developing tools to assist decision making and planning of water resources.The tools of systems analysis are varied in their usefulness. The approach and appropriate techniques naturally vary from problem to problem. It depends on the characteristics, i.e., the objective, scope of planning, state of development of system, the space and time of planning process. Our goal in this paper is to explain some advantages and disadvantages of meta-heuristic methods like genetic algorithm and simulated annealing algorithm in planning, limitations, and applications. Furthermore, we aim to note some points to apply the most ideal of using the programming methods in water resources systems optimization.

Time management is an important factor highly considered in any organization or system because it goes a very long way in determining productivity. In the light of this, software engineers using the scheduler have taken series of measures in designing systems that will process and complete jobs assigned to them in a predictably manageable time in order to increase the number of jobs processes per unit time. Decisions that the scheduler makes , concerning the sequence and length of time that processes may run are not easy ones, as the scheduler has only a limited amount of information about the processes that are ready to run. However, with the use of appropriate scheduling algorithms, important goals such as interactivity, effectiveness, cost and most importantly time can be balanced. In this work, three scheduling algorithms were considered, first-in-first-out (FIFO), round robin and shortest job First algorithms. A theoretical analysis that subject the algorithms to the same condition is performed through the use of exemplary job processes to determine the best among the algorithms. Job completion time, response time and permutation time were evaluated and it was discovered that shortest job first gives the optimal performance of scheduling processes followed by round robin and lastly FIFO.

Time management is an important factor highly considered in any organization or system because it goes a very long way in determining productivity. In the light of this, software engineers using the scheduler have taken series of measures in designing systems that will process and complete jobs assigned to them in a predictably manageable time in order to increase the number of jobs processes per unit time. Decisions that the scheduler makes , concerning the sequence and length of time that processes may run are not easy ones, as the scheduler has only a limited amount of information about the processes that are ready to run. However, with the use of appropriate scheduling algorithms, important goals such as interactivity, effectiveness, cost and most importantly time can be balanced. In this work, three scheduling algorithms were considered, first-in-first-out (FIFO), round robin and shortest job First algorithms. A theoretical analysis that subject the algorithms to the same condition is performed through the use of exemplary job processes to determine the best among the algorithms. Job completion time, response time and permutation time were evaluated and it was discovered that shortest job first gives the optimal performance of scheduling processes followed by round robin and lastly FIFO.

Time management is an important factor highly considered in any organization or system because it goes a very long way in determining productivity. In the light of this, software engineers using the scheduler have taken series of measures in designing systems that will process and complete jobs assigned to them in a predictably manageable time in order to increase the number of jobs processes per unit time. Decisions that the scheduler makes , concerning the sequence and length of time that processes may run are not easy ones, as the scheduler has only a limited amount of information about the processes that are ready to run. However, with the use of appropriate scheduling algorithms, important goals such as interactivity, effectiveness, cost and most importantly time can be balanced. In this work, three scheduling algorithms were considered, first-in-first-out (FIFO), round robin and shortest job First algorithms. A theoretical analysis that subject the algorithms to the same condition is performed through the use of exemplary job processes to determine the best among the algorithms. Job completion time, response time and permutation time were evaluated and it was discovered that shortest job first gives the optimal performance of scheduling processes followed by round robin and lastly FIFO.

Time management is an important factor highly considered in any organization or system because it goes a very long way in determining productivity. In the light of this, software engineers using the scheduler have taken series of measures in designing systems that will process and complete jobs assigned to them in a predictably manageable time in order to increase the number of jobs processes per unit time. Decisions that the scheduler makes , concerning the sequence and length of time that processes may run are not easy ones, as the scheduler has only a limited amount of information about the processes that are ready to run. However, with the use of appropriate scheduling algorithms, important goals such as interactivity, effectiveness, cost and most importantly time can be balanced. In this work, three scheduling algorithms were considered, first-in-first-out (FIFO), round robin and shortest job First algorithms. A theoretical analysis that subject the algorithms to the same condition is performed through the use of exemplary job processes to determine the best among the algorithms. Job completion time, response time and permutation time were evaluated and it was discovered that shortest job first gives the optimal performance of scheduling processes followed by round robin and lastly FIFO.

Time management is an important factor highly considered in any organization or system because it goes a very long way in determining productivity. In the light of this, software engineers using the scheduler have taken series of measures in designing systems that will process and complete jobs assigned to them in a predictably manageable time in order to increase the number of jobs processes per unit time. Decisions that the scheduler makes , concerning the sequence and length of time that processes may run are not easy ones, as the scheduler has only a limited amount of information about the processes that are ready to run. However, with the use of appropriate scheduling algorithms, important goals such as interactivity, effectiveness, cost and most importantly time can be balanced. In this work, three scheduling algorithms were considered, first-in-first-out (FIFO), round robin and shortest job First algorithms. A theoretical analysis that subject the algorithms to the same condition is performed through the use of exemplary job processes to determine the best among the algorithms. Job completion time, response time and permutation time were evaluated and it was discovered that shortest job first gives the optimal performance of scheduling processes followed by round robin and lastly FIFO.

The one-dimensional constant advection of a Gaussian function is used to test four positive-definite schemes -- Schemes I, II, Smolarkiwicz, and Arakawa --and three even-order accurate schemes -- III, IV, and V -- on the C staggered grid. It is shown that for the test problem, Schemes I and II produce identical numerical results. Arakawa's scheme produces the least damping among all the positive-definite schemes, but expensive computations are required with this scheme. With the advent of large digital computers, numerical modeling has taken an important place in atmospheric science. Constructing a numerical model often involves two steps. The first step is to formulate a set of continuous equations which represent the dynamics and the physics of interest in the atmosphere. The second step is to discretize these equations so that they may be solved on a computer.

Three solution algorithms, explicit under-relaxation, point implicit, and lower-upper symmetric Gauss-Seidel, are used to compute nonequilibrium flow around the Apollo 4 return capsule at the 62-km altitude point in its descent trajectory. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness.The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15 and 30, the lower-upper symmetric Gauss-Seidel method produces an eight order of magnitude drop in the energy residual in one-third to one-half the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 30 and above. At Mach 40 the performance of the lower-upper symmetric Gauss-Seidel algorithm deteriorates to the point that it is out performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.

Three solution algorithms, explicit under-relaxation, point implicit, and lower-upper symmetric Gauss-Seidel, are used to compute nonequilibrium flow around the Apollo 4 return capsule at the 62-km altitude point in its descent trajectory. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness.The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15 and 30, the lower-upper symmetric Gauss-Seidel method produces an eight order of magnitude drop in the energy residual in one-third to one-half the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 30 and above. At Mach 40 the performance of the lower-upper symmetric Gauss-Seidel algorithm deteriorates to the point that it is out performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.

Speaker : Chuck Knessl Keywords : Analysis of algorithms; Asympotics; WKB method; digital trees; quicksort; binary search trees; matched asympotic expansions. Abstract : We apply singular perturbation techniques to the analysis of algorithms and to trees. Examples include digital tress, binary search trees and quicksort algorithm.

Speaker : Chuck Knessl Keywords : Analysis of algorithms; Asympotics; WKB method; digital trees; quicksort; binary search trees; matched asympotic expansions. Abstract : We apply singular perturbation techniques to the analysis of algorithms and to trees. Examples include digital tress, binary search trees and quicksort algorithm.

Speaker : Chuck Knessl Keywords : Analysis of algorithms; Asympotics; WKB method; digital trees; quicksort; binary search trees; matched asympotic expansions. Abstract : We apply singular perturbation techniques to the analysis of algorithms and to trees. Examples include digital tress, binary search trees and quicksort algorithm.

Speaker : Chuck Knessl Keywords : Analysis of algorithms; Asympotics; WKB method; digital trees; quicksort; binary search trees; matched asympotic expansions. Abstract : We apply singular perturbation techniques to the analysis of algorithms and to trees. Examples include digital tress, binary search trees and quicksort algorithm.

This paper summarizes some of the work done by the Aer0-Astronautics Group of Rice University in the area of numerical methods and computing methods. It describes some of the philosophical thoughts that have guided this work throughout the years. Recommendations are offered concerning allocation of funds and distribution of funds. Additional recommendations are offered in order to bridge the gap between the top management of government agencies and the academic community. (Author)

This paper summarizes some of the work done by the Aer0-Astronautics Group of Rice University in the area of numerical methods and computing methods. It describes some of the philosophical thoughts that have guided this work throughout the years. Recommendations are offered concerning allocation of funds and distribution of funds. Additional recommendations are offered in order to bridge the gap between the top management of government agencies and the academic community. (Author)

This paper summarizes some of the work done by the Aer0-Astronautics Group of Rice University in the area of numerical methods and computing methods. It describes some of the philosophical thoughts that have guided this work throughout the years. Recommendations are offered concerning allocation of funds and distribution of funds. Additional recommendations are offered in order to bridge the gap between the top management of government agencies and the academic community. (Author)

Critical to the performace of the moving-bank multiple model adaptive estimator is the decision logic used to determine which elemental filters are implemented in the bank, and when to change this decision. The decision logics discussed focus on three situations: initial acquisition of the unknown parameter values through reducing bank discretization; tracking the unknown parameter values through bank movement; and reacquisition of the unknown parameters following a large jump change in their values through expanding bank discretization. Ambiguity function analysis is used to predict performance in these situations. The system to be controlled is a simplified model of a large scale space structure. Its equations of motion are developed and placed in state space form, the states being the positions and velocities of the rigid body mode and the second and fourth bending modes. The state space matrices describing the system are computed based on nominal values for all physical parameters with the exception of the mass density of the structure arms and their modulus of elasticity. These two parameters are allowed to vary in discrete steps establishing the parameter space. It is then attempted to control the states to the quiescent state, using moving-bank multiple model adaptive algorithms. The results indicate that, although the system under study did not have a great need for adaptive estimation and control the multiple model adaptive estimator performs essentially identically to a single filter artificially knowledgeable of the uncertain parameter values.

Critical to the performace of the moving-bank multiple model adaptive estimator is the decision logic used to determine which elemental filters are implemented in the bank, and when to change this decision. The decision logics discussed focus on three situations: initial acquisition of the unknown parameter values through reducing bank discretization; tracking the unknown parameter values through bank movement; and reacquisition of the unknown parameters following a large jump change in their values through expanding bank discretization. Ambiguity function analysis is used to predict performance in these situations. The system to be controlled is a simplified model of a large scale space structure. Its equations of motion are developed and placed in state space form, the states being the positions and velocities of the rigid body mode and the second and fourth bending modes. The state space matrices describing the system are computed based on nominal values for all physical parameters with the exception of the mass density of the structure arms and their modulus of elasticity. These two parameters are allowed to vary in discrete steps establishing the parameter space. It is then attempted to control the states to the quiescent state, using moving-bank multiple model adaptive algorithms. The results indicate that, although the system under study did not have a great need for adaptive estimation and control the multiple model adaptive estimator performs essentially identically to a single filter artificially knowledgeable of the uncertain parameter values.